Databricks Machine Learning Professional Exam Practice Test Instant Access

Question 1

A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.

Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

AStartinq a manual parent run before calling fmin

BEnsuring that a built-in model flavor is used for the model logging

CStarting a manual child run within the objective function

DThere is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt

EMLflow Autoloqqinq will automatically accomplish this task with Hyperopt

Answer : A

Question 2

Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

AStarting a testing job when a new model is registered

BUpdating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage

CSending an email alert when an automated testing Job fails

DNone of these use cases require the use of an HTTP Webhook

ESending a message to a Slack channel when a model version transitions stages

Answer : B

Question 3

Which of the following describes label drift?

ALabel drift is when there is a change in the distribution of the predicted target given by the model

BNone of these describe label drift

CLabel drift is when there is a change in the distribution of an input variable

DLabel drift is when there is a change in the relationship between input variables and target variables

ELabel drift is when there is a change in the distribution of a target variable

Answer : C

Question 4

Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

Apython_function provides no benefits over the built-in library-specific model flavors

Bpython_function can be used to deploy models in a parallelizable fashion

Cpython_function can be used to deploy models without worrying about which library was used to create the model

Dpython_function can be used to store models in an MLmodel file

Epython_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments

Answer : B

Question 5

A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.

Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

Aclient.list_run_infos(exp_id)

Bspark.read.format('delta').load(exp_id)

CThere is no way to programmatically return row-level results from an MLflow Experiment.

Dmlflow.search_runs(exp_id)

Espark.read.format('mlflow-experiment').load(exp_id)

Answer : B

Question 6

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

AAll of these reasons

BJS is not normalized or smoothed

CNone of these reasons

DJS is more robust when working with large datasets

EJS does not require any manual threshold or cutoff determinations

Answer : D

Question 7

A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.

Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

features_df.write.mode("fs").path("new_table")

AOption A

BOption B

COption C

DOption D

Efeatures_df.write.mode('feature').path('new_table')

Answer : D

Databricks Certified Machine Learning Professional Databricks Machine Learning Professional Exam Practice Test