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Databricks-Machine-Learning-Associate Practice Exam Questions with Answers Databricks Certified Machine Learning Associate Exam Certification

Question # 6

A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.

From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

A.

The home page of the MLflow Model Registry

B.

The experiment page in the Experiments observatory

C.

The model version page in the MLflow ModelRegistry

D.

The model page in the MLflow Model Registry

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Question # 7

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?

A.

Model tuning

B.

Model evaluation

C.

Model deployment

D.

Exploratory data analysis

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Question # 8

A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFramefeatures_df. A list of the names of the string columns is assigned to theinput_columnsvariable.

They have developed this code block to accomplish this task:

Databricks-Machine-Learning-Associate question answer

The code block is returning an error.

Which of the following adjustments does the data scientist need to make to accomplish this task?

A.

They need to specify the method parameter to the OneHotEncoder.

B.

They need to remove the line with the fit operation.

C.

They need to use Stringlndexer prior to one-hot encodinq the features.

D.

They need to useVectorAssemblerprior to one-hot encoding the features.

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Question # 9

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

A.

F1

B.

R-squared

C.

MAE

D.

MSE

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Question # 10

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective functionobjective_functionand they have defined the search spacesearch_space.

As a result, they have the following code block:

Databricks-Machine-Learning-Associate question answer

Which of the following changes do they need to make to the above code block in order to accomplish the task?

A.

Change SparkTrials() to Trials()

B.

Reduce num_evals to be less than 10

C.

Change fmin() to fmax()

D.

Remove the trials=trials argument

E.

Remove the algo=tpe.suggest argument

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Question # 11

A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.

Which of the following describes why?

A.

Gradient boosting is not a linear algebra-based algorithm which is required for parallelization

B.

Gradient boosting requires access to all data at once which cannot happen during parallelization.

C.

Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.

D.

Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.

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Question # 12

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?

A.

When the tuning process in randomized

B.

When the entire data can fit on each core

C.

When the model is unable to be parallelized

D.

When the data is particularly long in shape

E.

When the data is particularly wide in shape

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Question # 13

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?

A.

Logistic regression

B.

Singular value decomposition

C.

Iterative optimization

D.

Least-squares method

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Question # 14

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?

A.

PySpark DataFrame API

B.

pandas API on Spark

C.

Spark SQL

D.

Feature Store

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Question # 15

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?

A.

3

B.

5

C.

6

D.

18

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Question # 16

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)

Databricks-Machine-Learning-Associate question answer

B)

Databricks-Machine-Learning-Associate question answer

C)

Databricks-Machine-Learning-Associate question answer

D)

Databricks-Machine-Learning-Associate question answer

A.

Option A

B.

Option B

C.

Option C

D.

Option D

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Question # 17

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

A.

When the features are of the categorical type

B.

When the features are of the boolean type

C.

When the features contain a lot of extreme outliers

D.

When the features contain no outliers

E.

When the features contain no missingno values

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Question # 18

A machine learning engineer is trying to scale a machine learning pipelinepipelinethat contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

Databricks-Machine-Learning-Associate question answer

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to theestimatorparameter and then placing the updated cv object as the final stage of thepipelinein place of the original model.

Which of the following is a negative consequence of the approach suggested by the colleague?

A.

The model will take longerto train for each unique combination of hvperparameter values

B.

The feature engineering stages will be computed using validation data

C.

The cross-validation process will no longer be

D.

The cross-validation process will no longer be reproducible

E.

The model will be refit one more per cross-validation fold

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Question # 19

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

A.

Implement MLflow Experiment Tracking

B.

Scale up with Spark ML

C.

Enable autoscaling clusters

D.

Parallelize with Hyperopt

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Question # 20

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?

A.

fmin

B.

SparkTrials

C.

quniform

D.

search_space

E.

objective_function

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Question # 21

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:

Databricks-Machine-Learning-Associate question answer

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

A.

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

B.

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

C.

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

D.

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

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Question # 22

A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas Function API. They have developed theapply_modelfunction that will look up and load the correct model for each group, and they want to apply it to each group of DataFramedf.

They have written the following incomplete code block:

Databricks-Machine-Learning-Associate question answer

Which piece of code can be used to fill in the above blank to complete the task?

A.

applyInPandas

B.

groupedApplyInPandas

C.

mapInPandas

D.

predict

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