Label drift occurs where there is a change in which element?

• A.The relationship between input variables and target variables
• B.The distribution of the predicted target given by the model
• C.The distribution of a target variable
• D.The distribution of an input variable

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

• A.Streaming
• B.None of these strategies will accomplish the task.
• C.Edge/on-device
• D.Batch
• E.Real-time

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A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?

• A.spark.read.format("delta").table(path).drop("star_rating")
• B.spark.sql("SELECT * EXCEPT star_rating FROM path")
• C.spark.read.format("delta").load(path).drop("star_rating")
• D.spark.read.table(path).drop("star_rating")
• E.Delta tables cannot be modified

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

• A.Development. Staging. Production
• B.Development. Staging. Production. Archived
• C.None. Staging. Production. Archived
• D.Staging. Production. Archived
• E.None. Staging. Production

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A Machine Learning Engineer has deployed a fraud detection model in Databricks Model Serving to detect fraudulent transactions. The engineer wants to compare the model's predictions with the actual fraud classifications from the Fraud Ops team to monitor model performance. The Fraud Ops team uses a unique transaction_id to investigate fraudulent activity and persist their findings to a fraud_findings table. The engineer enabled inference tables on the endpoint, but they are not sure how to map the models' predictions to the Fraud Ops team's classifications. How can the engineer uniquely join the models' prediction to the fraud_findings table with the fewest code changes?

• A.Store databricks_request_id returned from each model serving request and persist it to the fraud_findings table. Join the inference table with the fraud_findings table using databricks_request_id as the join key.
• B.Join the inference table with the fraud_findings table using timestamp_ms as the join key.
• C.Populate the client_request_id field with the transaction_id in the model serving request body.
  Join the inference table with the fraud_findings table using client_request_id (which contains the transaction_id) as the join key.
• D.Modify the model to include an additional input: transaction_id. Log, register and deploy the new model. In the model serving request body, add transaction_id as an additional input feature. Join the inference table with the fraud_findings table using transaction_id as the join key.

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