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

Topic	Details
Topic 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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Databricks Certified Machine Learning Professional Sample Questions (Q166-Q171):

NEW QUESTION # 166
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches. Which tool can be used to provide this type of continuous processing?

A. MLflow
B. Spark UDFs
C. Delta Lake
D. Structured Streaming

Answer: B

NEW QUESTION # 167
Which MLflow command logs a trained model?

A. mlflow.log_model()
B. mlflow.start_run()
C. mlflow.run()
D. mlflow.register_model()

Answer: A

Explanation:
mlflow.log_model() saves the model artifact within the run.

NEW QUESTION # 168
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URls can be used to query the described model version?

A. https:///model-serving/recommender/Production/invocations
B. https:///model/recommender/stage-production/invocations
C. https:///model/recommender/Production/invocations
D. The version number of the model version in Production is necessary to complete this task.

Answer: C

Explanation:
In MLflow Model Serving, the correct URL pattern to query a specific stage of a registered model is https:///model/<model_name>/<stage>/invocations Thus, to query the model named recommender in the Production stage, the proper endpoint is
https:///model/recommender/Production/invocations.
This allows direct access to the active production version without needing its version number.

NEW QUESTION # 169
What tool helps avoid feature mismatch between training and inference?

A. Feature Store
B. Model Registry
C. MLflow Projects
D. Spark SQL

Answer: A

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 170
Why are Delta tables often used to store machine learning features?

A. They allow faster GPU training
B. They reduce model size
C. They support schema enforcement and time travel
D. They replace Spark DataFrames

Answer: C

Explanation:
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.

NEW QUESTION # 171
......

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