What is the primary benefit of using Unity Catalog for data management in an ML environment?

It enables real-time data streaming for model serving.

It automatically optimizes data storage formats for machine learning.

It provides a centralized governance and access control layer for data across workspaces.

It provides a version control system for machine learning models.

When designing a Databricks environment for ML workloads, which compute strategy best supports scalability and cost efficiency?

Create separate clusters for different workloads and use autoscaling and pools.

Use only serverless compute to avoid cluster management.

Keep clusters running 24/7 to avoid startup delays.

Use a single large cluster for all workloads to maximize utilization.

A company wants to implement a data management strategy for ML that supports both batch and streaming data. They need to ensure data quality and consistency. What is the recommended approach?

Use a data lake with raw files and rely on downstream processing for quality.

Use Parquet files for batch and Kafka for streaming, with no central governance.

Use Delta Lake for both batch and streaming data, with schema enforcement and Unity Catalog for governance.

Use separate storage systems for batch and streaming data.

A team uses Databricks Asset Bundles to manage ML assets. They have separate bundle configurations for dev, staging, and production. They want to promote a model from staging to production by reusing the same bundle definition with different parameters. What is the best approach?

Create a new bundle for production and import the staging configuration.

Copy the bundle configuration from staging to production and modify the model version.

Use the same bundle configuration for all environments and manually update the model version in the UI after deployment.

Use a single bundle with environment-specific target files that override the model version.

DatabricksCertified Machine Learning Professional

Domain 2Objective 3

Environment Architectures MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 5)

Part of the ML Ops domain, which accounts for 44% of the MACHINE-LEARNING-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~22–35 in this domain), expect 4–7 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

9concepts

44%of the exam

Questions 21–25

21foundation · easy
What is the primary benefit of using Unity Catalog for data management in an ML environment?
Select an answer first
22foundation · easy
When designing a Databricks environment for ML workloads, which compute strategy best supports scalability and cost efficiency?
Select an answer first
23foundation · easy
In a Databricks Asset Bundle, which field is used to assign an alias to a registered model version?
Select an answer first
24application · medium
A company wants to implement a data management strategy for ML that supports both batch and streaming data. They need to ensure data quality and consistency. What is the recommended approach?
Select an answer first
25expert · hard
A team uses Databricks Asset Bundles to manage ML assets. They have separate bundle configurations for dev, staging, and production. They want to promote a model from staging to production by reusing the same bundle definition with different parameters. What is the best approach?
Select an answer first

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