A Machine Learning Engineer is building a Databricks ML pipeline to predict customer churn. The pipeline needs to include automated feature engineering, model training, evaluation, and deployment to a REST API endpoint using MLflow. What is the primary goal of an integration test for this pipeline?

• A. To ensure the pipeline runs with all components working together and data flowing correctly between stages
• B. To ensure that the pipeline's performance meets latency and scalability requirements under simulated production loads
• C. To verify that the pipeline can process a variety of data formats, even if some stages are skipped or bypassed
• D. To monitor the model's performance after it has been deployed into the production environment

A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company's cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider's Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
"cloud_creds" with key "db_key". These credentials can be used to authenticate to the cloud provider's SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

• A. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Add "environment_vars":
  {"DB_ACCESS_TOKEN": ""} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
• B. Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
  /.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.
• C. Add "environment_vars": {"DB_ACCESS_TOKEN": "{{secrets/cloud_creds/db_key}}"} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
• D. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Log the model to MLflow with the access token value as a custom MLflow artifact.
  Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider's SDK.

A Machine Learning Engineer is building a fraud detection model that needs to use both pre- computed features from a feature table and real-time calculated features based on user location data sent with each inference request. The engineer has created a Python UDF called calculate_distance in Unity Catalog at main.fraud_detection.calculate_distance that computes the distance between a transaction location and the user's current location. The feature table main.fraud_detection.user_features contains historical user spending patterns with primary key user_id.
The engineer has written the following code to implement this scenario:

Which benefit of this implementation approach makes it suited to the real-time fraud detection use case?

• A. The model automatically performs feature lookup and computation during inference without additional serving code.
• B. The FeatureFunction caches computed distance values in the online store to improve inference latency for location pairs.
• C. The FeatureLookup function avoids the need for joining the full table main.fraud_detection.user_features with training_set increasing efficiency.
• D. The Unity Catalog registry automatically creates REST API endpoints for UDF functions used in feature computation.

Which of the following statements about built-in library-specific MLflow Model flavors is true?

• A. Built-in library-specific flavors can only be used for logging models
• B. Built-in library-specific flavors allow models to be exported as library objects
• C. Built-in library-specific flavors allow models to be used with any library/span>
• D. Built-in library-specific flavors are required for model signature use

A machine learning engineer has a machine learning pipeline where predictions are updated annually. The final prediction dataset contains millions of rows, and that dataset is irregularly accessed. Which solution should the machine learning engineer use to maintain cost efficiency?

• A. Cloud-based low latency database
• B. On-premises low-latency database
• C. Cloud-based in-memory instance of a Spark DataFrame
• D. Cloud-based object storage