Online MLS-C01 Exam Dumps Help You Build Confidence

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December 31, 2023

Practicing with AWS Certification MLS-C01 exam dumps questions can help you build confidence and reduce exam anxiety. By familiarizing yourself with the types of questions you can expect to see on the MLS-C01 AWS Certified Machine Learning - Specialty exam and mastering the concepts and skills required to answer them, you can approach the exam with greater confidence and a sense of calm. Besides, MLS-C01 dumps questions can help you achieve this level of knowledge and confidence. Study free Amazon MLS-C01 exam dumps below.

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1. A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data.

Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries

Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

2. A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%.

What should the Specialist consider to fix this issue1?

A longer training time

Making the network larger

Using a different optimizer

Using some form of regularization

3. A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.

A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.

Which algorithms are best suited to this scenario? (Choose two.)

Latent Dirichlet allocation (LDA)

Random Forest classifier

Neural topic modeling (NTM)

Linear support vector machine

Linear regression

4. A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.

What is the MOST effective way to encode this categorical feature into a numeric feature?

Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

Fix the spelling in the column by using char-RN

Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.

Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.

Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.

5. A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

What should the Specialist do to meet this objective?

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EM

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EM

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EM

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EM

6. A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes

Which function will produce the desired output?

Dropout

Smooth L1 loss

Softmax

Rectified linear units (ReLU)

7. A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.

How should the data scientist accomplish this?

Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.

Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.

Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.

Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

8. A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity. The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and …perature for each crane. The company contracts AWS ML experts to implement an ML solution.

Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

The historical sensor data does not include a significant number of data points and attributes for certain time periods.

The historical sensor data shows that simple rule-based thresholds can predict crane failures.

The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.

The historical sensor data from the cranes are available with high granularity for the last 3 years.

The historical sensor data contains most common types of crane failures that the company wants to predict.

9. A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.

Create a new endpoint configuration with two production variants.

Configure the endpoint to automatically scale with the Invocations Per Instance metric.

Deploy a second instance pool to support a blue/green deployment of models.

Reconfigure the endpoint to use burstable instances.

10. A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant

Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

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11. A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.

Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.

What technique should be used to convert this column to binary values.

Binarization

One-hot encoding

Tokenization

Normalization transformation

12. A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents.

How should a Machine Learning Specialist address this issue for future documents?

Convert current documents to SSML with pronunciation tags

Create an appropriate pronunciation lexicon.

Output speech marks to guide in pronunciation

Use Amazon Lex to preprocess the text files for pronunciation

13. An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic.

Which solution will meet these requirements?

A/B testing

Canary release

Shadow deployment

Blue/green deployment

14. A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.

What should the ML specialist do to resolve the violations?

Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.

Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.

Delete the endpoint and recreate it with the original configuration.

Retrain the model again by using a combination of the original training set and the new training set.

15. A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.

How should the Specialist address this issue and what is the reason behind it?

The learning rate should be increased because the optimization process was trapped at a local minimum.

The dropout rate at the flatten layer should be increased because the model is not generalized enough.

The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

16. A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.

Which model will meet the business requirement?

Logistic regression

Linear regression

K-means

Principal component analysis (PCA)

17. Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

Recall

Misclassification rate

Mean absolute percentage error (MAPE)

Area Under the ROC Curve (AUC)

18. A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.

Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.

Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.

Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

19. A retail company uses a machine learning (ML) model for daily sales forecasting. The company’s brand manager reports that the model has provided inaccurate results for the past 3 weeks.

At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company’s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.

What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.

Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.

Create a line chart with the weekly mean absolute error (MAE) of the model.

Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.

20. A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company’s data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.

The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.

Which solution will meet these requirements with the LEAST operational overhead?

Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.

Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.

Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.

Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.

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21. A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.

Which storage option provides the most processing flexibility and will allow access control with IAM?

Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.

Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

22. A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.

The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.

Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURE

Return the confidence scores for each page.

Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.

Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.

Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page

23. A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.

A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.

Which labeling approach will help the company improve this model?

Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

24. A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.

.... Dh Amazon SageMaker algorithm will meet this requirement?

XGBoost

Image Classification - TensorFlow

Object Detection - TensorFlow

Semantic segmentation - MXNet

25. A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.

Which solution will meet these requirements with the LEAST amount of effort?

Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.

Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.

26. A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

Use the event tracker in Amazon Personalize to include real-time user interactions.

Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

Add event type and event value fields to the interactions dataset in Amazon Personalize.

27. A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.

A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.

Which AWS service or feature will meet these requirements with the LEAST development effort over time?

AWS Glue jobs

Amazon EMR cluster

Amazon Athena

AWS Lambda

28. A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.

The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.

Which solution will result in the MOST cost savings?

Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.

Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml. r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

29. A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.

Which approach should the Specialist use to continue working?

Install Python 3 and boto3 on their laptop and continue the code development using that environment.

Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

30. A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.

A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.

What is the MOST direct approach to solve this problem within 2 days?

Train a custom classifier by using Amazon Comprehend.

Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.

Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.

Use a built-in seq2seq model in Amazon SageMaker.

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31. An online delivery company wants to choose the fastest courier for each delivery at the moment an

order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model fof each city where the company operates. The engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory,

....operation engineers want to avoid managing unnecessary resources.

Which solution will enable the company to achieve its goal with the LEAST operational overhead?

Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.

Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.

Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.

Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints. one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpomtName parameter according to the city of each request.

32. When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

The training channel identifying the location of training data on an Amazon S3 bucket.

The validation channel identifying the location of validation data on an Amazon S3 bucket.

The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

Hyperparameters in a JSON array as documented for the algorithm used.

The Amazon EC2 instance class specifying whether training will be run using CPU or GP

The output path specifying where on an Amazon S3 bucket the trained model will persist.

33. A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.

A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.

Which solution will meet these requirements?

Apply anomaly detection to remove outliers from the training dataset before training.

Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.

Apply normalization to the features of the training dataset before training.

Apply undersampling to the training dataset before training.

34. A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.

How should the data scientist meet these requirements?

Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.

Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.

Run a SageMaker training job with an EFS file system as the data source.

35. A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.

To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.

Which deployment architecture for the model will address these business requirements?

Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.

Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.

Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.

Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.

36. A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property.

The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.

Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

Apply integer transformation and set Red = 1, White = 5, and Green = 10.

Add new columns that store one-hot representation of colors.

Replace the color name string by its length.

Create three columns to encode the color in RGB format.

Replace each color name by its training set frequency.

37. IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution. The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning

The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts. The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data.

The following are high priorities for the company:

• Solution simplicity

• Fast development time

• Low cost

• High flexibility

What technologies meet the company's requirements?

Amazon S3 and Amazon Athena

Amazon Redshift and AWS Glue

Amazon DynamoDB and DynamoDB Accelerator (DAX)

Amazon RDS and Amazon ES

38. A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable ecall metric. The Data Scientist has already tried varying the number and size of the MLP’s hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.

Which techniques should be used to meet these requirements?

Gather more data using Amazon Mechanical Turk and then retrain

Train an anomaly detection model instead of an MLP

Train an XGBoost model instead of an MLP

Add class weights to the MLP’s loss function and then retrain

39. A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

Perform one-hot encoding on highly correlated features

Use matrix multiplication on highly correlated features.

Create a new feature space using principal component analysis (PCA)

Apply the Pearson correlation coefficient

40. A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

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41. A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company’s data currently resides on premises and is 40 ТВ in size.

The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.

Which solution meets these requirements?

Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.

Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.

Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AW

Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

42. A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.

What steps could be used to accomplish this task? (Choose two.)

Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.

Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.

Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.

Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.

43. A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.

Which solution will meet these requirements with the LEAST development effort?

Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.

Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

44. A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10.000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels * 224 pixels. After several training runs, the model is overfitting on the training data.

Which actions should the ML specialist take to address this problem? (Select TWO.)

Use Amazon SageMaker Ground Truth to label the unlabeled images

Use image preprocessing to transform the images into grayscale images.

Use data augmentation to rotate and translate the labeled images.

Replace the activation of the last layer with a sigmoid.

Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.

45. A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.

Which of the following services can feed data to the MapReduce jobs? (Select TWO)

AWSDMS

Amazon Kinesis

AWS Data Pipeline

Amazon Athena

Amazon ES

46. A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.

What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

1 shards

10 shards

100 shards

1,000 shards

47. A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.

Which action will provide the MOST secure protection?

Remove Amazon S3 access permissions from the SageMaker execution role.

Encrypt the weights of the CNN model.

Encrypt the training and validation dataset.

Enable network isolation for training jobs.

48. A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members’ faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.

The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.

How should a machine learning specialist architect the solution to satisfy these requirements?

Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support.

Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.

Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VP

Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VP

49. A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.

The solution needs to do the following:

Calculate an anomaly score for each web traffic entry.

Adapt unusual event identification to changing web patterns over time.

Which approach should the data scientist implement to meet these requirements?

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.

Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.

Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

50. A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago

Which method should the Specialist try to improve model performance?

The model needs to be completely re-engineered because it is unable to handle product inventory changes

The model's hyperparameters should be periodically updated to prevent drift

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

The model should be periodically retrained using the original training data plus new data as product inventory changes

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51. An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.

Which solution should the agency consider?

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non- employees are detected.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

52. A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.

How will the data scientist MOST effectively model the problem?

The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.

The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.

Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.

Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

53. A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

54. A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll).

The dataset:

* Must be accessible from a VPC only.

* Must not traverse the public internet.

How can these requirements be satisfied?

Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VP

Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

55. A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor.

Which machine learning approach fulfills the company’s long-term needs?

Convert the images to grayscale and retrain the model

Reduce the number of distinct items from 10 to 2, build the model, and iterate

Attach different colored labels to each item, take the images again, and build the model

Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

56. A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.

Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)

AWS CloudTrail

AWS Health

AWS Trusted Advisor

Amazon CloudWatch

AWS Config

57. A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker.

The model produces the following results:

• True positive rate (TPR): 0.700

• False negative rate (FNR): 0.300

• True negative rate (TNR): 0.977

• False positive rate (FPR): 0.023

• Overall accuracy: 0.949

Which solution should the data scientist use to improve the performance of the model?

Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

Undersample the minority class.

Oversample the majority class.

58. During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates.

What is the MOST likely cause of this issue?

The class distribution in the dataset is imbalanced

Dataset shuffling is disabled

The batch size is too big

The learning rate is very high

59. A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.

Which nodes should the Specialist launch on Spot Instances?

Master node

Any of the core nodes

Any of the task nodes

Both core and task nodes

60. A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent.

How should the Specialist frame this business problem?

Streaming classification

Binary classification

Multi-category classification

Regression classification

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61. A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.

The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes

What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.

Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

62. A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.

The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict

device malfunctions. The ML spoctafst must ensure that the data does not contain outliers before training the ..el.

What can the ML specialist meet these requirements with the LEAST operational overhead?

Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.

Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data Wrangler data flow to remove outliers based on the bias report.

Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.

Use Amazon Lookout for Equipment to find and remove outliers from the dataset.

63. A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects. The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches

What actions would allow the Specialist to get relevant numerical representations?

Reduce image resolution and use reduced resolution pixel values as features

Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels

Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

Average colors by channel to obtain three-dimensional representations of images.

64. A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

65. A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.

What model evaluation technique should the Specialist use to understand how different classification

thresholds will impact the model's performance?

Receiver operating characteristic (ROC) curve

Misclassification rate

Root Mean Square Error (RM&)

L1 norm

66. A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs.

The workflow consists of the following processes

* Start the workflow as soon as data is uploaded to Amazon S3

* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3

* Store the results of joining datasets in Amazon S3

* If one of the jobs fails, send a notification to the Administrator

Which configuration will meet these requirements?

Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

67. A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.

Which TensorFlow estimator configuration will train the model MOST cost-effectively?

Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method.

Adjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

68. An Amazon SageMaker notebook instance is launched into Amazon VPC. The SageMaker notebook references data contained in an Amazon S3 bucket in another account. The bucket is encrypted using SSE-KMS. The instance returns an access denied error when trying to access data in Amazon S3.

Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)

An AWS KMS key policy that allows access to the customer master key (CMK)

A SageMaker notebook security group that allows access to Amazon S3

An 1AM role that allows access to the specific S3 bucket

A permissive S3 bucket policy

An S3 bucket owner that matches the notebook owner

A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

69. An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.

The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.

Which solution will improve the computational efficiency of the models?

Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.

Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.

Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.

Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.

70. A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

Drop the features with low correlation scores by using a Jupyter notebook.

Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.

Concatenate the features with high correlation scores by using a Jupyter notebook.

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71. A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.

Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.

Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.

72. A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.

Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

Add a VPC endpoint policy to allow access to the IAM users.

Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.

Modify the security group on the endpoint network interface to restrict access to the instances.

Modify the ACL on the endpoint network interface to restrict access to the instances.

Add a SageMaker Runtime VPC endpoint interface to the VP

73. A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.

Which solution should a machine learning specialist implement to meet these requirements?

Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

74. A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance

How should the records be stored in Amazon S3 to improve query performance?

CSV files

Parquet files

Compressed JSON

RecordIO

75. The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

The model predicts both the trend and the seasonality well.

The model predicts the trend well, but not the seasonality.

The model predicts the seasonality well, but not the trend.

The model does not predict the trend or the seasonality well.

76. A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.

Which solution requires the LEAST effort to be able to query this data?

Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

Use AWS Glue to catalogue the data and Amazon Athena to run queries.

Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

77. A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.

Which approach will provide the MAXIMUM performance boost?

Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.

Reduce the learning rate and run the training process until the training loss stops decreasing.

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

78. A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases

Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

Increase the randomization of training data in the mini-batches used in training.

Allocate a higher proportion of the overall data to the training dataset

Apply L1 or L2 regularization and dropouts to the training.

Reduce the number of layers and units (or neurons) from the deep learning network.

79. A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.

A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.

Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly

Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

80. An e-commerce company needs a customized training model to classify images of its shirts and pants products. The company needs a proof of concept in 2 to 3 days with good accuracy.

Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

m5 4xlarge (general purpose)

r5.2xlarge (memory optimized)

p3.2xlarge (GPU accelerated computing)

p3 8xlarge (GPU accelerated computing)

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81. A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0. The abnormal operation label is marked as 1. Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

Precision = 0.91 Recall = 0.6

Precision = 0.61 Recall = 0.98

Precision = 0.7 Recall = 0.9

Precision = 0.98 Recall = 0.8

82. An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.

How should a machine learning specialist meet these requirements?

Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.

Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.

Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.

Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.

83. A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.

How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.

Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

84. A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

What type of machine learning model should be used?

Classification month-to-month using supervised learning of the 200 categories based on claim contents.

Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

85. A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure,” and "documentary.”

A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor.". The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.

How should the ML specialist fix the problem?

A. Add the unrecognized words in the enumeration values list as new values in the slot type.

B. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.

C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.

D. Add the unrecognized words as synonyms in the custom slot type.

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