Free Databricks-Machine-Learning-Professional Exam Questions - Databricks Databricks-Machine-Learning-Professional Exam

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?

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

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.

Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A)

B)

It is not possible to deploy a scikit-learn model on a Spark DataFrame.

C)

D)

E)

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?

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?