A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?

• A.Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.
• B.Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.
• C.Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.
• D.Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.

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In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?

• A.The arrival of a new model version in the MLflow Model Registry
• B.The launch of a new cost-efficient job cluster
• C.CI/CD pipelines are not needed for machine learning pipelines
• D.The launch of a new cost-efficient SQL endpoint
• E.The arrival of a new feature table in the Feature Store

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

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A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?

• A."MODEL_VERSION_TRANSITIONED_TO_STAGING"
• B."MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
• C."MODEL_VERSION_TRANSITIONED_STAGE"
• D."MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
• E."MODEL_VERSION_CREATED"

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A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

• A.Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
• B.Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.
• C.Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.
• D.Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.

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