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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	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 5	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 6	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 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q84-Q89):

NEW QUESTION # 84
Which statement describes streaming with Spark as a model deployment strategy?

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

Answer: C

NEW QUESTION # 85
In a Spark ML Pipeline, what is the role of a Transformer?

A. Converts datasets into new datasets
B. Fits data
C. Evaluates models
D. Trains models

Answer: A

Explanation:
A Transformer takes a dataset and transforms it into another dataset.

NEW QUESTION # 86
Which of the following describes the concept of MLflow Model flavors?

A. A convention that deployment tools can use to understand the model
B. A convention that MLflow Model Registry can use to organize its Models by project
C. A convention that deployment tools can use to wrap preprocessing logic into a Model
D. A convention that MLflow Model Registry can use to version models
E. A convention that MLflow Experiments can use to organize their Runs by project

Answer: E

NEW QUESTION # 87
Which component manages model versions?

A. Feature Store
B. MLflow Tracking
C. Spark ML
D. Model Registry

Answer: D

Explanation:
Model Registry handles:
versioning
stage transitions
governance.

NEW QUESTION # 88
A data scientist set up a machine learning pipeline to automatically log feature importance data from a CSV with each run. They now want to view the feature importance data in Databricks.
Which location in Databricks will show this data?

A. The Data section of the MLflow Run page/span>
B. The Artifacts section of the MLflow Experiment page
C. The MLflow Model Registry Model page
D. The Artifacts section of the MLflow Run page

Answer: D

Explanation:
When a data scientist logs feature importance data (e.g., as a CSV or JSON file) using MLflow, it is stored as an artifact of that specific run. These artifacts are visible under the Artifacts section of the MLflow Run page in Databricks, where users can view or download the logged files associated with that particular experiment run.

NEW QUESTION # 89
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