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

Topic	Details
Topic 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	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 5	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 6	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 8	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 9	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 10	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 11	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 (Q129-Q134):

NEW QUESTION # 129
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. Label drift is when there is a change in the distribution of the predicted target given by the model
C. None of these describe label drift
D. Label drift is when there is a change in the distribution of an input variable
E. Label drift is when there is a change in the distribution of a target variable

Answer: D

NEW QUESTION # 130
Why is Delta Lake time travel useful in ML pipelines?

A. Smaller datasets
B. Reproducible training datasets
C. Model tuning
D. Faster model inference

Answer: B

Explanation:
Time travel allows training on exact historical datasets.

NEW QUESTION # 131
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

A. All of these statements must be true prior to deploying the new model
B. The new model must perform better than the original model on the most recently available data
C. The new model must perform better than the original model on a random subset of the available data
D. The now model must perform better than the original model on all of the available data

Answer: B

Explanation:
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

NEW QUESTION # 132
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?

A. Databricks REST APIs
B. AutoML APIs
C. MLflow APIs
D. Jobs cannot be created programmatically
E. MLflow Client

Answer: A

NEW QUESTION # 133
How can you save a trained Spark ML PipelineModel?

A. pipeline.persist()
B. pipeline.store()
C. pipeline.save()
D. pipelineModel.write().save()

Answer: D

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")

NEW QUESTION # 134
......

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