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

Databricks Certified Machine Learning Professional Questions and Answers

Question 1

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

Options:

A.

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

B.

python_function can be used to deploy models in a parallelizable fashion

C.

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

D.

python_function can be used to store models in an MLmodel file

E.

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

Question 2

Which of the following describes the concept of MLflow Model flavors?

Options:

A.

A convention that deployment tools can use to wrap preprocessing logic into a Model

B.

A convention that MLflow Model Registry can use to version models

C.

A convention that MLflow Experiments can use to organize their Runs by project

D.

A convention that deployment tools can use to understand the model

E.

A convention that MLflow Model Registrycan use to organize its Models by project

Question 3

Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

Options:

A.

Streaming

B.

Batch

C.

Edge/on-device

D.

None of these strategies will accomplish the task.

E.

Real-time

Question 4

Which of the following is a simple, low-cost method of monitoring numeric feature drift?

Options:

A.

Jensen-Shannon test

B.

Summary statistics trends

C.

Chi-squared test

D.

None of these can be used to monitor feature drift

E.

Kolmogorov-Smirnov (KS) test

Question 5

A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project"model"and they'd like to add the text in themodel_descriptionvariable.

The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

Options:

A.

Replace update_registered_model with update_model_version

B.

There no changes necessary

C.

Replace description with artifact

D.

Replace client.update_registered_model with mlflow

E.

Add a Python model as an argument to update_registered_model

Question 6

A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model.

Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

Options:

A.

Staging. Production. Archived

B.

Production

C.

None. Staging. Production. Archived

D.

Staging. Production

E.

[None. Staging. Production

Question 7

In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?

Options:

A.

The launch of a new cost-efficient SQL endpoint

B.

CI/CD pipelines are not needed for machine learning pipelines

C.

The arrival of a new feature table in the Feature Store

D.

The launch of a new cost-efficient job cluster

E.

The arrival of a new model version in the MLflow Model Registry

Question 8

A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.

Which of the following locations in Databricks will show these data visualizations?

Options:

A.

The MLflow Model RegistryModel paqe

B.

The Artifacts section of the MLflow Experiment page

C.

Logged data visualizations cannot be viewed in Databricks

D.

The Artifacts section of the MLflow Run page

E.

The Figures section of the MLflow Run page

Question 9

A data scientist has developed a modelmodeland computed the RMSE of the model on the test set. They have assigned this value to the variablermse. They now want to manually store the RMSE value with the MLflow run.

They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

Options:

A.

log_artifact

B.

log_model

C.

log_metric

D.

log_param

E.

There is no way to store values like this.

Question 10

After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.

Which of the following SQL commands can be used to accomplish this task?

Options:

A.

VERSION

B.

DESCRIBE

C.

HISTORY

D.

DESCRIBE HISTORY

E.

TIMESTAMP

Question 11

A machine learning engineer wants to move their model versionmodel_versionfor the MLflow Model Registry modelmodelfrom the Staging stage to the Production stage using MLflow Clientclient. At the same time, they would like to archive any model versions that are already in the Production stage.

Which of the following code blocks can they use to accomplish the task?

A)

B)

C)

D)

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

Question 12

Which of the following is a probable response to identifying drift in a machine learning application?

Options:

A.

None of these responses

B.

Retraining and deploying a model on more recent data

C.

All of these responses

D.

Rebuilding the machine learning application with a new label variable

E.

Sunsetting the machine learning application

Question 13

A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.

Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

Options:

A.

Batch servinghas built-in capabilities in Databricks Machine Learning

B.

There is no advantage to using batch serving deployments over real-time serving deployments

C.

Computing predictions in real-time provides more up-to-date results

D.

Testing is not possible in real-time serving deployments

E.

Querying stored predictions can be faster than computing predictions in real-time

Question 14

A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.

Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

Options:

A.

mlflow.load_model(model_uri)

B.

client.list_artifacts(run_id)["feature-importances.csv"]

C.

mlflow.sklearn.load_model(model_uri)

D.

This can only be viewed in the MLflow Experiments UI

E.

client.pyfunc.load_model(model_uri)

Question 15

A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.

Which of the following code blocks will accomplish this task inside of an existing MLflow run block?

Options:

A.

B.

C.

mlflow.log_data(importance_path, "feature-importance.csv")

D.

mlflow.log_artifact(importance_path, "feature-importance.csv")

E.

None of these code blocks tan accomplish the task.

Question 16

A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.

Which of the following model URIs can be used to query the described model version?

Options:

A.

https:// /model-serving/recommender/Production/invocations

B.

The version number of the model version in Production is necessary to complete this task.

C.

https:// /model/recommender/stage-production/invocations

D.

https:// /model-serving/recommender/stage-production/invocations

E.

https:// /model/recommender/Production/invocations

Question 17

A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when theinferencetable is a stream source?

Options:

A.

Replace "inference" with the path to the location of the Delta table

B.

Replace schema(schema) with option("maxFilesPerTriqqer", 1}

C.

Replace spark.read with spark.readStream

D.

Replace formatfdelta") with format("stream")

E.

Replace predict with a stream-friendly prediction function

Question 18

Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

Options:

A.

mlflow.shap.log_explanation

B.

None of these operations can accomplish the task.

C.

mlflow.shap

D.

mlflow.log_figure

E.

client.log_artifact