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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?

A)

B)

C)

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

D)

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 the inference table is a stream source?

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 machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.

Which of the following tools can the machine learning engineer use to assess their theory?

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?

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.