Online Access Free Databricks-Machine-Learning-Professional Practice Test

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

• A.On-device
• B.None of these deployments
• C.Batch
• D.Streaming
• E.Real-time

Which of the following describes label drift?

• A.Label drift is when there is a change in the relationship between input variables and target variables
• B.None of these describe label drift
• C.Label drift is when there is a change in the distribution of an input variable
• D.Label drift is when there is a change in the distribution of a target variable
• E.Label drift is when there is a change in the distribution of the predicted target given by the model

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

• A.Add the nested=True argument to the parent run
• B.Indent the child run blocks within the parent run block
• C.Provide the same name to the run name parameter for all three run blocks
• D.Remove the nested=True argument from the child runs
• E.Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?

• A.mlflow.sklearn.load_model(model_uri)
• B.mlflow.spark.load_model(model_uri)
• C.mlflow.pyfunc.load_model(model_uri)
• D.mlflow.pyfunc.read_model(model_uri)
• E.mlflow.sklearn.read_model(model_uri)

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.features_df.write.mode("feature").path("new_table")
• E.