Databricks Databricks Certifications DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Questions & Answers

• Question 31:

  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?

  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

  Correct Answer: E

• Question 32:

  A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon

  investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.

  Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

  A. Z-Ordering

  B. Bin-packing

  C. Write as a Parquet file

  D. Data skipping

  E. Tuning the file size

  Correct Answer: A

• Question 33:

  Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

  A. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario

  B. The context parameter allows the user to document the performance of a model after it has been deployed

  C. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model

  D. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario

  E. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files

  Correct Answer: E

• Question 34:

  A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df,

  but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.

  Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

  A. df = fs.get_missing_features(spark_df, model_uri) fs.score_model(model_uri, df)

  B. fs.score_model(model_uri, spark_df)

  C. df = fs.get_missing_features(spark_df, model_uri) fs.score_batch(model_uri, df)

  D. df = fs.get_missing_features(spark_df) fs.score_batch(model_uri, df)

  E. fs.score_batch(model_uri, spark_df)

  Correct Answer: E

• Question 35:

  A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Which of the following deployment strategies can be used to meet these requirements?

  A. Edge/on-device

  B. Streaming

  C. None of these strategies will meet the requirements.

  D. Batch

  E. Real-time

  Correct Answer: E

• Question 36:

  A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model". Which of the following lines of code can they use to register the model to the MLflow Model Registry?

  A. mlflow.register_model(model_uri, "best_model")

  B. mlflow.register_model(run_id, "best_model")

  C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")

  D. mlflow.register_model(model_uri, "model")

  E. mlflow.register_model(f"runs:/{run_id}/model")

  Correct Answer: A

• Question 37:

  A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model

  to "project".

  Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

  A. mlflow.register_model

  B. MlflowClient.update_registered_model

  C. mlflow.add_model_version

  D. MlflowClient.get_model_version

  E. The machine learning engineer needs to create an entirely new MLflow Model Registry model

  Correct Answer: A

• Question 38:

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

  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

  Correct Answer: C

• Question 39:

  Which of the following lists all of the model stages are available in the MLflow Model Registry?

  A. Development, Staging, Production

  B. None, Staging, Production

  C. Staging, Production, Archived

  D. None, Staging, Production, Archived

  E. Development, Staging, Production, Archived

  Correct Answer: D

• Question 40:

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

  A. Starting a testing job when a new model is registered

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

  C. Sending an email alert when an automated testing Job fails

  D. None of these use cases require the use of an HTTP Webhook

  E. Sending a message to a Slack channel when a model version transitions stages

  Correct Answer: E