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DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Real Exam Questions

Databricks Certified Machine Learning Professional

60 questions available · Page 1 of 6

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Question 1 Single choice

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?

  1. A

    mlflow.load_model(model_uri)

  2. B

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

  3. C

    mlflow.sklearn.load_model(model_uri)

  4. D

    This can only be viewed in the MLflow Experiments UI

  5. E

    client.pyfunc.load_model(model_uri)

Show answer and explanation

Correct answer: C

Question 2 Single choice

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 the model_description variable.
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?

  1. A

    Replace update_registered_model with update_model_version

  2. B

    There no changes necessary

  3. C

    Replace description with artifact

  4. D

    Replace client.update_registered_model with mlflow

  5. E

    Add a Python model as an argument to update_registered_model

Show answer and explanation

Correct answer: B

Question 3 Single choice

A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block: image9

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

  1. A

    log_artifact

  2. B

    log_model

  3. C

    log_metric

  4. D

    log_param

  5. E

    There is no way to store values like this.

Show answer and explanation

Correct answer: C

Question 4 Single choice

A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.

Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

  1. A

    Because the streaming deployment is always on, all types of data must be handled without producing an error

  2. B

    All of these statements

  3. C

    Because the streaming deployment is always on, there is no practitioner to debug poor model performance

  4. D

    Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale

  5. E

    None of these statements

Show answer and explanation

Correct answer: B

Question 5 Single choice

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?

  1. A

    The MLflow Model Registry Model page

  2. B

    The Artifacts section of the MLflow Experiment page

  3. C

    Logged data visualizations cannot be viewed in Databricks

  4. D

    The Artifacts section of the MLflow Run page

  5. E

    The Figures section of the MLflow Run page

Show answer and explanation

Correct answer: D

Question 6 Single choice

A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.

Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

  1. A

    Option A

  2. B

    Option B

  3. C

    Option C

  4. D

    Option D

  5. E

    Option E

Show answer and explanation

Correct answer: D

Question 7 Single choice

Which of the following describes label drift?

  1. A

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

  2. B

    None of these describe label drift

  3. C

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

  4. D

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

  5. E

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

Show answer and explanation

Correct answer: E

Question 8 Single choice

Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

  1. A

    On-device

  2. B

    Streaming

  3. C

    Real-time

  4. D

    Batch

  5. E

    None of these deployments

Show answer and explanation

Correct answer: D

Question 9 Single choice

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?

  1. A

    https://<databricks-instance>/model-serving/recommender/Production/invocations

  2. B

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

  3. C

    https://<databricks-instance>/model/recommender/stage-production/invocations

  4. D

    https://<databricks-instance>/model-serving/recommender/stage-production/invocations

  5. E

    https://<databricks-instance>/model/recommender/Production/invocations

Show answer and explanation

Correct answer: E

Question 10 Single choice

A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.

Which of the following Databricks tools can be used to programmatically create the Job?

  1. A

    MLflow APIs

  2. B

    AutoML APIs

  3. C

    MLflow Client

  4. D

    Jobs cannot be created programmatically

  5. E

    Databricks REST APIs

Show answer and explanation

Correct answer: E