Improve Your Knowledge with MLA-C01 Exam Dumps

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February 9, 2025

Practicing with MLA-C01 questions can help you identify areas where you need to improve your knowledge. By answering MLA-C01 questions and reviewing your responses, you can identify gaps in your understanding and focus your study efforts on those areas. The MLA-C01 exam has a strict time limit, and you need to manage your time effectively to answer all the questions. Practicing with Amazon MLA-C01 dumps questions can help you develop time management skills by simulating the exam's time constraints. You'll learn how to pace yourself, manage your time effectively, and ensure that you complete the MLA-C01 exam within the allotted time. Test Amazon MLA-C01 exam free dumps below.

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1. Your company has a significant amount of data stored in an Amazon DynamoDB database. As part of a new machine learning project, you need to access this data from an Amazon SageMaker notebook for analysis and model development. The goal is to ensure that the data is efficiently accessible from the notebook while maintaining performance and minimizing potential delays.

Use the AWS SDK for Python (Boto3) to query DynamoDB directly from the SageMaker notebook

Use AWS Glue to transfer your table from DynamoDB to S3 bucket. Configure Sagemaker notebook to connect to S3 bucket for accessing the data

Use Sagemaker Data Wrangler to directly import data from Amazon DynamoDb database to Sagemaker notebook

Export your DynamoDB table into a .csv file and upload this file to the S3 bucket. Configure Sagemaker notebook to connect to S3 bucket for accessing the data

2. What is the bias versus variance trade-off in machine learning?

The bias versus variance trade-off refers to the balance between underfitting and overfitting, where high bias leads to overfitting and high variance leads to underfitting

The bias versus variance trade-off is a technique used to improve model performance by increasing both bias and variance simultaneously to achieve better generalization

The bias versus variance trade-off refers to the challenge of balancing the error due to the model's complexity (variance) and the error due to incorrect assumptions in the model (bias), where high bias can cause underfitting and high variance can cause overfitting

The bias versus variance trade-off involves choosing between a model with high complexity that may capture more noise (high bias) and a simpler model that may generalize better but miss important patterns (high variance)

3. You are working as a data scientist at a company that specializes in predictive analytics. You are tasked with training a deep learning model using Amazon SageMaker to predict customer churn. The dataset

you have is large and contains millions of records. The training process is taking longer than expected, and you suspect that the hyperparameters need fine-tuning. You want to balance the training time while ensuring the model converges effectively. You have set the batch size to 256, epochs to 50, and learning rate to 0.01. However, the training job is still not performing as expected.

Given this scenario, which of the following adjustments is MOST LIKELY to reduce the training time without compromising model performance?

Decrease the learning rate and increase the batch size

Increase the batch size and decrease the number of epochs

Increase the number of epochs and maintain the batch size

Decrease the batch size and increase the number of epochs

4. A company is using a fleet of Amazon EC2 instances to ingest data from on-premises data sources. The data is in JSON format and ingestion rates can be as high as 1 MB/s. When an EC2 instance is rebooted, the data in flight is lost. The company’s data science team wants to query ingested data in near-real time.

Which solution provides near-real-time data querying that is scalable with minimal data loss?

Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Write to Amazon S3 bucket and use Amazon Redshift Spectrum to query the data

Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Publish data to Amazon ElastiCache for Redis. Subscribe to the Redis channel to query the data

Publish data to Amazon Kinesis Data Streams, use Kinesis Data Analytics to query the data

Store ingested data in an EC2 instance store. Publish data to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the data

5. You are an ML engineer at an e-commerce company tasked with building an automated recommendation system that scales during peak shopping seasons. The solution requires provisioning multiple compute resources, including SageMaker for model training, EC2 instances for data preprocessing, and an RDS database for storing user interaction data. You need to automate the deployment and management of these resources, ensuring that the stacks can communicate effectively. The company prioritizes infrastructure as code (IaC) to maintain consistency and scalability across environments.

Which approach is the MOST SUITABLE for automating the provisioning of compute resources and ensuring seamless communication between stacks?

Manually provision the SageMaker, EC2, and RDS resources using the AWS Management Console, ensuring that communication is established by manually updating security groups and networking configurations

Use AWS Elastic Beanstalk to deploy the entire ML solution, relying on its built-in environment management to handle the provisioning and communication between resources automatically

Use AWS CDK (Cloud Development Kit) to define the infrastructure in a high-level programming language, deploying each service as an independent stack without configuring inter-stack communication

Use AWS CloudFormation with nested stacks to automate the provisioning of SageMaker, EC2, and RDS resources, and configure outputs from one stack as inputs to another to enable communication between them

6. You are a machine learning engineer working for a telecommunications company that needs to develop a predictive maintenance model. The goal is to predict when network equipment is likely to fail based on historical sensor data. The data includes features such as temperature, pressure, usage, and error rates recorded over time. The company wants to avoid unplanned downtime and optimize maintenance schedules by predicting failures just in time.

Given the nature of the data and the business objective, which Amazon SageMaker built-in algorithm is the MOST SUITABLE for this use case?

DeepAR Algorithm to forecast future equipment failures based on historical data

Time Series K-Means Algorithm to cluster similar patterns in the sensor data and predict failures

Random Cut Forest (RCF) Algorithm to detect anomalies in sensor data that may indicate impending failures

Linear Learner Algorithm to classify equipment status as 'healthy' or 'at risk' based on sensor readings

7. You are a data scientist at a healthcare company working on deploying a machine learning model that predicts patient outcomes based on real-time data from wearable devices. The model needs to be containerized for easy deployment and scaling across different environments, including development, testing, and production. The company wants to ensure that container images are managed efficiently, securely, and consistently across all environments.

Given these requirements, which combination of AWS services is the MOST SUITABLE for building, storing, deploying, and maintaining the containerized ML solution?

Use Docker Hub to store the container images, Amazon EKS for orchestrating the containers, and AWS Lambda to trigger updates to the containers when new images are pushed

Use Amazon ECR to store the container images, Amazon EKS for orchestrating the containers, and AWS CodePipeline for automating the CI/CD pipeline, ensuring that updates to the model are seamlessly deployed

Use Amazon ECR to store container images, manually deploy containers on Amazon EC2 instances, and use AWS CloudFormation to manage the infrastructure configuration

Use Amazon ECS to manage and deploy the containerized model, Amazon S3 to store container images, and manually push updates to the containers using the AWS CLI

8. You are a machine learning engineer at an e-commerce company that uses a recommendation model to suggest products to customers. The model was trained on data from the past year, but after being in production for several months, you notice that the model's recommendations are becoming less relevant. You suspect that either data drift or model drift could be causing the decline in performance. To investigate and resolve the issue, you need to understand the difference between these two types of drift and how to monitor them using Amazon SageMaker.

Which of the following statements BEST describes the difference between data drift and model drift, and how you would address them using Amazon SageMaker?

Data drift occurs when the distribution of the input data changes over time, while model drift happens when the model’s underlying assumptions or parameters become outdated. To address data drift, you should use SageMaker Model Monitor to track changes in input data distribution. For model drift, you should periodically retrain the model using the latest data

Data drift is a sudden change in the model’s accuracy, while model drift is a gradual degradation in model performance. You should use SageMaker Feature Store to manage both types of drift by standardizing input data

Data drift occurs when the model’s predictions start to deviate from the expected outcomes, while model drift occurs when the model's accuracy declines due to changes in the input data. SageMaker Pipelines should be used to automate retraining for both types of drift

Data drift refers to changes in the model’s accuracy due to shifts in the data, while model drift refers to changes in the underlying data features over time. To address both, you should use SageMaker Clarify to detect bias and retrain the model monthly

9. Your company is starting a new machine learning project, and the data preparation tasks are being handled by a team of business analysts. These analysts are more comfortable working with visual tools rather than writing code, and they need to combine, transform, and clean large datasets efficiently. The goal is to use a SageMaker tool that allows them to perform these tasks using a visual, point-and-click interface.

Which SageMaker tool(s) would best suit the team's requirements for preparing and analyzing data without writing code?

Prepare data using Amazon EMR in Studio

Use Amazon SageMaker Data Wrangler within Amazon SageMaker Canvas

Prepare data with SQL in Amazon SageMaker Studio. The SQL extension connections to Amazon Athena for working with datasets

Use Amazon SageMaker Data Wrangler within Amazon SageMaker JumpStart

10. You are a machine learning engineer tasked with building a deep learning model to classify images for an autonomous vehicle project. The dataset is massive, consisting of millions of labeled images. Initial training runs on a single GPU instance in Amazon SageMaker are taking too long, and the training costs are rising. You need to reduce the model training time without compromising performance significantly.

Which of the following approaches is the MOST LIKELY to effectively reduce the training time while maintaining model performance?

Implement distributed training using multiple GPU instances to parallelize the training process, reducing the overall time

Reduce the size of the training dataset to speed up training, even if it means using fewer examples per class

Switch to a smaller instance type to reduce computational costs, accepting a longer training time as a trade-off

Enable early stopping to halt training when the model’s performance on the validation set stops improving, thereby avoiding overfitting

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11. What is Feature Engineering in the context of machine learning?

Feature Engineering involves selecting, modifying, or creating features from raw data to improve the performance of machine learning models, and it is important because it can significantly enhance model accuracy and efficiency

Feature Engineering refers to the visualization of data to understand patterns, and it is important because it helps in identifying trends in the dataset

Feature Engineering is the process of tuning hyperparameters in a machine learning model, and it is important because it optimizes the model’s performance

Feature Engineering is the process of collecting raw data, and it is important because it ensures the availability of data for model training

12. Your data science team is working on developing a machine learning model to predict customer churn. The dataset that you are using contains hundreds of features, but you suspect that not all of these features are equally important for the model's accuracy. To improve the model's performance and reduce its complexity, the team wants to focus on selecting only the most relevant features that contribute significantly to minimizing the model's error rate.

Which feature engineering process should your team apply to select a subset of features that are the most relevant towards minimizing the error rate of the trained model?

Feature extraction

Feature creation

Feature transformation

Feature selection

13. You are a data scientist at a retail company responsible for deploying a machine learning model that predicts customer purchase behavior. The model needs to serve real-time predictions with low latency to support the company’s recommendation engine on its e-commerce platform. The deployment solution must also be scalable to handle varying traffic loads during peak shopping periods, such as Black Friday and holiday sales. Additionally, you need to monitor the model's performance and automatically roll out updates when a new version of the model is available.

Given these requirements, which AWS deployment service and configuration is the MOST SUITABLE for deploying the machine learning model?

Deploy the model on Amazon EC2 instances with a load balancer to distribute traffic, manually scaling the instances based on expected traffic during peak periods

Deploy the model on Amazon SageMaker with batch transform jobs, running the jobs periodically to generate predictions and storing the results in Amazon S3 for the recommendation engine

Deploy the model using Amazon SageMaker real-time hosting services with an auto-scaling endpoint, enabling you to automatically adjust the number of instances based on traffic demand

Use AWS Lambda to deploy the model as a serverless function, automatically scaling based on the number of requests, and store the model artifacts in Amazon S3

14. In what scenario would traditional programming techniques be preferable over ML?

When complex logic and scalability are required.

When the problem can be solved with simple rules.

When personalized recommendations are needed.

When quick adaptation to new data is necessary.

15. You are a data scientist working on a loan approval model for a bank. The model predicts whether a loan application should be approved or rejected based on various features such as income, credit score, and employment history. The bank is particularly concerned about ensuring that the model is fair and does not discriminate against any demographic group, such as age, gender, or ethnicity. To address this, you need to select the appropriate evaluation metrics to assess both the model’s performance and any potential bias.

Given these requirements, which combination of evaluation metrics and bias detection methods is MOST APPROPRIATE for ensuring fair and accurate model predictions?

Measure the model’s performance using accuracy and AUC-ROC, and check for bias by comparing feature distributions across demographic groups

Evaluate the model using F1 score and AUC-ROC, and and check for bias by comparing feature distributions across demographic groups

Use accuracy as the primary evaluation metric and perform feature importance analysis to ensure that the model’s decisions are driven by relevant features

Evaluate the model using F1 score and AUC-ROC, and assess whether the model has similar true positive rates across different demographic groups

16. How is machine learning utilized in healthcare?

Automating the patient check-in process

Managing medical staff schedules

Suggesting treatment for patients by analyzing clinical data

Scheduling patient appointments

17. You are preparing a dataset for training a machine learning model using SageMaker Data Wrangler. The dataset has several missing values spread across different columns, and these columns contain numeric data. Before training the model, it is essential to handle these missing values to ensure the model performs optimally. The goal is to replace the missing values in each numeric column with the mean of that column.

Which transformation in SageMaker Data Wrangler should you apply to replace the missing values in numeric columns with the mean of those columns?

Encode

Impute

Scale

Drop

18. A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.

Which strategy will allow the data scientist to identify fraudulent accounts?

Create a FindMatches machine learning transform in AWS Glue.

Run the built-in FindDuplicates Amazon Athena query.

Create an AWS Glue crawler to infer duplicate accounts in the source data.

Search for duplicate accounts in the AWS Glue Data Catalog.

19. What is the main goal of testing the model in ML?

To prepare the data for training

To generate predictions for new data

To construct input representations from raw variables

To evaluate the quality of the model on held-out data

20. What is uncertainty in the context of machine learning?

The clarity of the model's decision-making process

The speed of the algorithm

The amount of data available for training

An imperfect outcome

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21. You are an ML engineer at a data analytics company tasked with training a deep learning model on a large, computationally intensive dataset. The training job can tolerate interruptions and is expected to run for several hours or even days, depending on the available compute resources. The company has a limited budget for cloud infrastructure, so you need to minimize costs as much as possible.

Which strategy is the MOST EFFECTIVE for your ML training job while minimizing cost and ensuring the job completes successfully?

Start the training job using only Spot Instances to minimize cost, and switch to On-Demand instances manually if any Spot Instances are interrupted during training

Use Amazon SageMaker Managed Spot Training to dynamically allocate Spot Instances for the training job, automatically retrying any interrupted instances via checkpoints

Deploy the training job on a fixed number of On-Demand EC2 instances to ensure stability, and manually add Spot Instances as needed to speed up the job during off-peak hours

Use Amazon EC2 Auto Scaling to automatically add Spot Instances to the training job based on demand, and configure the job to continue processing even if some Spot Instances are interrupted

22. You are tasked with building a predictive model for customer lifetime value (CLV) using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize hyperparameters to achieve the best possible performance. You decide to use SageMaker’s automatic model tuning (hyperparameter optimization) with Random Search strategy to fine-tune the model. You have a large dataset, and the tuning job involves several hyperparameters, including the learning rate, batch size, and dropout rate. During the tuning process, you observe that some of the trials are not converging effectively, and the results are not as expected. You suspect that the hyperparameter ranges or the strategy you are using may need adjustment.

Which of the following approaches is MOST LIKELY to improve the effectiveness of the hyperparameter tuning process?

Decrease the number of total trials but increase the number of parallel jobs to speed up the tuning process

Switch from the Random Search strategy to the Bayesian Optimization strategy and narrow the range of critical hyperparameters

Use the Grid Search strategy with a wide range for all hyperparameters and increase the number of total trials

Increase the number of hyperparameters being tuned and widen the range for all hyperparameters

23. You are a Data Scientist working for an e-commerce platform that uses a machine learning model to recommend products to customers. The model has been in production for over a year and was initially performing well. However, you have recently noticed a decrease in the model's accuracy, particularly when recommending products to new customers. This decline suggests that the model may be experiencing drift due to changing customer preferences and market trends. To address this issue, you need to implement a strategy for detecting and managing model drift using Amazon SageMaker.

Which of the following strategies should you implement to effectively detect and manage model drift in your product recommendation model using Amazon SageMaker? (Select two)

Retrain the model with the new training data, ensuring that the model remains up to date with new customer preferences

Deploy multiple versions of the model simultaneously using Amazon SageMaker multi-model endpoints, and switch between them based on performance metrics

Manually review model performance every quarter and initiate retraining only if a significant drop in accuracy is observed, minimizing unnecessary retraining costs

Use Amazon SageMaker Clarify to continuously monitor and mitigate bias in the model, and initiate model retraining due to changes in data distribution

Use Amazon SageMaker Model Monitor to set up monitoring for data quality and data drift, enabling you to receive alerts and initiate model retraining when the distribution of input data changes significantly from the training data

24. You are an ML Engineer at a financial services company tasked with deploying a machine learning model for real-time fraud detection in production. The model requires low-latency inference to ensure that fraudulent transactions are flagged immediately. However, you also need to conduct extensive testing and experimentation in a separate environment to fine-tune the model and validate its performance before deploying it. You must provision compute resources that are appropriate for both environments, balancing performance, cost, and the specific needs of testing and production.

Which of the following strategies should you implement to effectively provision compute resources for both the production environment and the test environment using Amazon SageMaker, considering the different requirements for each environment? (Select two)

Leverage AWS Inferentia accelerators in the production environment to meet high throughput and low latency requirements

Provision CPU-based instances in both production and test environments to reduce costs, as CPU instances are generally cheaper than GPU instances

Use GPU-based instances in both production and test environments to ensure that the model inference and testing are both performed at maximum speed, regardless of cost

Use CPU-based instances in the test environment to save on costs during experimentation

Provision identical instances in both production and test environments to ensure consistent performance between the two, eliminating the risk of discrepancies during deployment

25. What is the primary distinction between discriminative models and generative models in the context of generative AI?

Discriminative models are only used for text classification, while generative models are only used for image classification

Discriminative models are used to generate new data, while generative models are used only for classification

Generative models are trained on labeled data, while discriminative models can be trained on both labeled and unlabeled data

Generative models focus on generating new data from learned patterns, whereas discriminative models classify data by distinguishing between different classes

26. 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 an acceptable recall 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?

Train an anomaly detection model instead of an MLP

Train an XGBoost model instead of an MLP

Gather more data using Amazon Mechanical Turk and then retrain

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

27. Which aspect of AI does not directly involve machine learning?

Data extraction in Amazon Textract.

Intelligent search in Amazon Kendra.

Document analysis in Amazon Comprehend.

Rule-based automation without learning.

28. You are a Data Scientist working for an e-commerce company that is developing a machine learning model to predict whether a customer will make a purchase based on their browsing behavior. You need to evaluate the model's performance using different evaluation metrics to understand how well the model is predicting the positive class (i.e., customers who will make a purchase). The dataset is imbalanced, with a small percentage of customers making a purchase. Given this context, you must decide on the most appropriate evaluation techniques to assess your model's effectiveness and identify potential areas for improvement.

Which of the following evaluation techniques and metrics should you prioritize when assessing the performance of your model, considering the dataset's imbalance and the need for a comprehensive understanding of both false positives and false negatives? (Select two)

Prioritize Root mean squared error (RMSE) as the key metric, as it measures the average magnitude of the errors between predicted and actual values

Utilize the AUC-ROC curve to evaluate the model’s ability to distinguish between classes across various thresholds, particularly in the presence of class imbalance

Evaluate the model using the confusion matrix, which provides insights into true positives, false positives, true negatives, and false negatives, allowing you to calculate additional metrics such as precision, recall, and F1 score

Use accuracy as the primary metric, as it measures the percentage of correct predictions out of all predictions made by the model

Use precision and recall to focus on the model's ability to correctly identify positive cases while minimizing false positives and false negatives

29. You are a machine learning engineer managing an ML model deployed on Amazon SageMaker to provide real-time predictions for a financial application. The model is critical for fraud detection and must operate with high availability and low latency. Recently, users have reported increased latency in receiving predictions, and there have been occasional timeout errors. To maintain the performance and reliability of the model, you need to configure monitoring and alerting tools to troubleshoot and resolve these issues promptly.

Which of the following approaches is the MOST EFFECTIVE for configuring and using Amazon CloudWatch tools to troubleshoot and analyze the performance of your ML solution?

Enable Amazon CloudWatch Logs to capture detailed logs from the SageMaker endpoint, configure CloudWatch Alarms to monitor invocation latency and error rates, and set up notifications to alert the team when thresholds are breached

Use CloudWatch Logs Insights to manually search and analyze logs from the SageMaker training jobs, and configure CloudWatch Alarms to track CPU and memory usage on the EC2 instances running the model

Enable Amazon CloudTrail for logging all API calls, and use CloudWatch Logs to monitor network traffic. Create CloudWatch Alarms based on API activity to detect performance issues Rely on the SageMaker console to monitor model performance metrics, and configure CloudWatch Logs to store training logs without setting up specific alarms, analyzing logs only when issues arise

30. How does the Amazon flywheel model ensure continuous improvement and growth?

By using feedback loops that integrate customer experience, selection, and pricing.

By limiting the scope of operational improvements to cost reduction only.

By avoiding changes to business practices.

By maintaining a static approach to business operations.

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31. You are a data scientist at a financial services company tasked with deploying a lightweight machine learning model that predicts creditworthiness based on a customer’s transaction history. The model needs to provide real-time predictions with minimal latency, and the traffic pattern is unpredictable, with occasional spikes during business hours. The company is cost-conscious and prefers a serverless architecture to minimize infrastructure management overhead.

Which approach is the MOST SUITABLE for deploying this solution, and why?

Deploy the model directly within AWS Lambda as a function, and expose it through an API Gateway endpoint, allowing the function to scale automatically with traffic and provide real-time predictions

Deploy the model as a SageMaker endpoint for real-time inference, and configure AWS Lambda to preprocess incoming requests before sending them to the SageMaker endpoint for prediction

Deploy the model using Amazon ECS (Elastic Container Service) and configure an AWS Lambda to trigger the ECS service on-demand, ensuring that the model is only running during peak traffic periods

Use an Amazon EC2 instance to host the model, with AWS Lambda functions handling the communication between the API Gateway and the EC2 instance for prediction requests

32. Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?

Relying solely on data encryption to protect the AI training data

Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities

Using a single authentication mechanism for all users and services accessing the AI models

Implementing a single-layer firewall to block unauthorized access to the AI models

33. You are a data scientist at an insurance company that uses a machine learning model to assess the risk of potential clients and set insurance premiums accordingly. The model was trained on data from the past few years, but recently, the company has expanded its services to new regions with different demographic characteristics. You are concerned that these changes in the data distribution might affect the model's performance and lead to biased or inaccurate predictions. To address this, you decide to use Amazon SageMaker Clarify to monitor and detect any significant shifts in data distribution that could impact the model.

Which of the following actions is the MOST EFFECTIVE for detecting changes in data distribution using SageMaker Clarify and mitigating their impact on model performance?

Set up a continuous monitoring job with SageMaker Clarify to track changes in feature distribution over time and alert you when a significant feature attribution drift is detected, allowing you to investigate and potentially retrain the model

Implement a random sampling process to manually review a subset of incoming data each month, comparing it with the original training data to check for distribution changes

Use SageMaker Clarify’s bias detection capabilities to analyze the model’s output and identify any disparities between different demographic groups, retraining the model only if significant bias is detected

Use SageMaker Clarify to perform a one-time bias analysis during model training, ensuring that the model is initially fair and accurate, and manually monitor future data distribution changes

34. You are a data scientist at a healthcare company developing a machine learning model to analyze medical imaging data, such as X-rays and MRIs, for disease detection. The dataset consists of 10 million high-resolution images stored in Amazon S3, amounting to several terabytes of data. The training process requires processing these images efficiently to avoid delays due to I/O bottlenecks, and you must ensure that the chosen data access method aligns with the large dataset size and the high throughput requirements of the model.

Given the size and nature of the dataset, which SageMaker input mode and AWS Cloud Storage configuration is the MOST SUITABLE for this use case?

Select the Pipe input mode to stream the data directly from Amazon S3 to the training instances, allowing the model to start processing data immediately without requiring local storage for the entire dataset

Use the File input mode with EFS (Amazon Elastic File System) to mount the dataset across multiple instances, ensuring data is shared and accessible during distributed training

Implement the FastFile input mode with FSx for Lustre, to enable on-demand streaming of data chunks from Amazon S3 with low latency and high throughput

Use the File input mode to download the entire dataset from Amazon S3 to the training instances' local storage before starting the training process, ensuring that all data is available locally during training

35. You are a data scientist at a healthcare startup tasked with developing a machine learning model to predict the likelihood of patients developing a specific chronic disease within the next five years. The dataset available includes patient demographics, medical history, lab results, and lifestyle factors, but it is relatively small, with only 1,000 records. Additionally, the dataset has missing values in some critical features, and the class distribution is highly imbalanced, with only 5% of patients labeled as having developed the disease.

Given the data limitations and the complexity of the problem, which of the following approaches is the MOST LIKELY to determine the feasibility of an ML solution and guide your next steps?

Proceed with training a deep neural network (DNN) model using the available data, as DNNs can handle small datasets by learning complex patterns

Increase the dataset size by generating synthetic data and then train a simple logistic regression model to avoid overfitting

Conduct exploratory data analysis (EDA) to understand the data distribution, address missing values, and assess the class imbalance before determining if an ML solution is feasible

Immediately apply an oversampling technique to balance the dataset, then train an XGBoost model to maximize performance on the minority class

36. You are a machine learning engineer at a biotech company developing a custom deep learning model for analyzing genomic data. The model relies on a specific version of TensorFlow with custom Python libraries and dependencies that are not available in the standard SageMaker environments. To ensure compatibility and flexibility, you decide to use the "Bring Your Own Container" (BYOC) approach with Amazon SageMaker for both training and inference.

Given this scenario, which steps are MOST IMPORTANT for successfully deploying your custom container with SageMaker, ensuring that it meets the company’s requirements?

Package the model as a SageMaker-compatible file, upload it to Amazon S3, and use a pre-built SageMaker container for training, ensuring that the training job uses the custom environment

Create a Docker container with the required environment, push the container image to Amazon ECR (Elastic Container Registry), and use SageMaker’s Script Mode to execute the training script within the container

Build a Docker container with the required TensorFlow version and dependencies, push the container image to Docker Hub, and reference the image in SageMaker when creating the training job

Deploy the model locally using Docker, then use the AWS Management Console to manually copy the environment and model files to a SageMaker instance for training

37. A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.

What should the Specialist do to optimize the data for training on SageMaker?

Use AWS Glue to compress the data into the Apache Parquet format.

Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

Transform the dataset into the RecordIO protobuf format.

Use the SageMaker batch transform feature to transform the training data into a DataFrame.

38. A Machine Learning Specialist is developing a custom video recommendation model for an application.

The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.

Which approach allows the Specialist to use all the data to train the model?

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset.

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