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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 8	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q123-Q128):

NEW QUESTION # 123
A machine learning engineer has created a webhook with the following code block:

Which code block will trigger this webhook to run the associate job?

Answer: C

Explanation:
The webhook in the first image is configured to trigger on the
MODEL_VERSION_TRANSITIONED_TO_PRODUCTION event. The chosen code uses
client.transition_model_version_stage(...) to move the model version to "Production", which matches the event type in the webhook and will correctly trigger the associated job.

NEW QUESTION # 124
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. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
B. Replace predict with a stream-friendly prediction function
C. Replace "inference" with the path to the location of the Delta table
D. Replace spark.read with spark.readStream
E. Replace formatfdelta") with format("stream")

Answer: A

NEW QUESTION # 125
Which of the following lists all of the model stages are available in the MLflow Model Registry?

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

Answer: B

NEW QUESTION # 126
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. 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.
B. Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.
C. Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.
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.

Answer: A

Explanation:
Using smaller, production-like datasets and reduced training iterations preserves the full pipeline structure while significantly reducing execution time. This aligns with MLOps best practices by maintaining high-fidelity integration testing in a staging environment that mirrors production behavior, without the cost and delay of running full-scale training workloads.

NEW QUESTION # 127
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. There are no necessary changes
B. Replace POST with GET in the call to http request
C. Replace list with view in the endpoint URL
D. Replace list with webhooks in the endpoint URL
E. Replace POST with PUT in the call to http request

Answer: D

NEW QUESTION # 128
......

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