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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	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 3	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 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

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Databricks Certified Machine Learning Professional Sample Questions (Q86-Q91):

NEW QUESTION # 86
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Models
B. Feature Store
C. Model Registry
D. Experiments
E. Model Serving

Answer: E

NEW QUESTION # 87
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. Edge/on-device
B. None of these strategies will accomplish the task.
C. Streaming
D. Batch
E. Real-time

Answer: E

NEW QUESTION # 88
Which statement describes streaming with Spark as a model deployment strategy?

A. The inference of incrementally processed records as soon as a Spark job is run
B. The inference of incrementally processed records as soon as trigger is hit
C. The inference of batch processed records as soon as a Spark job is run
D. The inference of batch processed records as soon as a trigger is hit
E. The inference of all types of records in real-time

Answer: A

NEW QUESTION # 89
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.
B.
C.
D. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
E.

Answer: C

NEW QUESTION # 90
Concept drift is when there is a change in which element?

A. The distribution of an input variable
B. The relationship between input variables and target variables
C. The distribution of the predicted target given by the model
D. The distribution of a target variable

Answer: B

Explanation:
Concept drift occurs when the underlying relationship between input features and the target variable changes over time. This means that even if the feature and label distributions remain stable, the way features influence the target (the predictive relationship) has shifted, leading to reduced model accuracy unless retraining occurs.

NEW QUESTION # 91
......

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