Pass Databricks Machine Learning Associate Exam to Get Databricks Certification

The Databricks Machine Learning Associate exam is hot, and passing it requires a deep understanding of Databricks solutions. Practicing with Databricks Databricks Machine Learning Associate dumps questions can help you reinforce your knowledge and increase your chances of passing the exam. Databricks Machine Learning Associate dumps are available to help you prepare for the Databricks Machine Learning Associate exam. Using Databricks Machine Learning Associate exam dumps questions is one effective way to supplement your study plan and increase your chances of success on exam day. Test free Machine Learning Associate Databricks Machine Learning Associate exam dumps below.

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1. Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

Open the MLmodel artifact in the MLflow run paqe

Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe

Click the "Source" link in the row corresponding to the run in the MLflow experiment page

Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page

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2. The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.

Which of the following approaches does Spark ML use to distribute the training of a linear regression

model for large data?

Logistic regression

Spark ML cannot distribute linear regression training

Iterative optimization

Least-squares method

Singular value decomposition

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3. A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster.

Which of the following approaches will guarantee a reproducible training and test set for each model?

Manually configure the cluster

Write out the split data sets to persistent storage

Set a speed in the data splitting operation

Manually partition the input data

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4. A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.

They attempt to run the following code block, but it does not accomplish the desired task:





Which of the following changes can the data scientist make to accomplish the task?

Replace the GridSearchCV operation with RandomizedSearchCV

Replace the GridSearchCV operation with cross_validate

Replace the GridSearchCV operation with ParameterGrid

Replace the random_state=0 argument with random_state=1

Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')

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5. Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

TrainValidationSplit

DataFrame.where

CrossValidator

TrainValidationSplitModel

DataFrame.randomSplit

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6. A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.

Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.

They can check the Databricks Runtime ML box when creating their clusters.

They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.

They can set the runtime-version variable in their Spark session to “ml”.

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7. A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.

Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

They can turn on Databricks Autologging

They can specify nested=True when starting the child run for each unique combination of hyperparameter values

They can start each child run inside the parent run's indented code block using mlflow.start runO

They can start each child run with the same experiment ID as the parent run

They can specify nested=True when starting the parent run for the tuning process

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8. Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

F1

R-squared

MAE

MSE

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9. A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.

Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

PySpark DataFrame API

pandas API on Spark

Spark SQL

Feature Store

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10. In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

When the features are of the categorical type

When the features are of the boolean type

When the features contain a lot of extreme outliers

When the features contain no outliers

When the features contain no missing no values

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11. A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.

Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

A holdout set is not necessary when using a train-validation split

Reproducibility is achievable when using a train-validation split

Fewer hyperparameter values need to be tested when using a train-validation split

Bias is avoidable when using a train-validation split

Fewer models need to be trained when using a train-validation split

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12. Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

Keras

Scikit-learn

PyTorch

Spark ML

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13. A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.

Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

They can refactor their notebook to process the data in parallel.

They can refactor their notebook to use the PySpark DataFrame AP

They can refactor their notebook to use the Scala Dataset AP

They can refactor their notebook to use Spark SQ

They can refactor their notebook to utilize the pandas API on Spark.

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14. The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.

Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

Logistic regression

Singular value decomposition

Iterative optimization

Least-squares method

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15. A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.

In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

When the tuning process in randomized

When the entire data can fit on each core

When the model is unable to be parallelized

When the data is particularly long in shape

When the data is particularly wide in shape

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16. A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.

Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

Model tuning

Model evaluation

Model deployment

Exploratory data analysis

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17. Which of the following machine learning algorithms typically uses bagging?

IGradient boosted trees

K-means

Random Forest

Decision tree

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18. A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:





Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

The data will be limited to a single executor preventing the model from being loaded multiple times

The model will be limited to a single executor preventing the data from being distributed

The model only needs to be loaded once per executor rather than once per batch during the inference process

The data will be distributed across multiple executors during the inference process

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19. A data scientist is using Spark ML to engineer features for an exploratory machine learning project.

They decide they want to standardize their features using the following code block:





Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.

Which of the following changes can the data scientist make to address the concern?

Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values

Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values

Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data

Utilize the Pipeline API to standardize the training data according to the test data's summary statistics

Utilize the Pipeline API to standardize the test data according to the training data's summary statistics

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20. A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema: prediction DOUBLE actual DOUBLE

Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

A)





B)





C)





D)





E)

Option A

Option B

Option C

Option D

Option E

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21. A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:

● Hyperparameter 1: [2, 5, 10]

● Hyperparameter 2: [50, 100]

Which of the following represents the number of machine learning models that can be trained in parallel during this process?

3

5

6

18

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22. A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model in parallel. They elect to use the Hyperopt library to facilitate this process.

Which of the following Hyperopt tools provides the ability to optimize hyperparameters in parallel?

fmin

SparkTrials

quniform

search_space

objective_function

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23. A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.

Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

Change the number of compute nodes to be half or less than half of the number of evaluations.

Change the number of compute nodes and the number of evaluations to be much larger but equal.

Change the iterative optimization algorithm used to facilitate the tuning process.

Change the number of compute nodes to be double or more than double the number of evaluations.

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