How does a feature store enforce temporal consistency during a point-in-time lookup?

By returning the feature value that was recorded at the exact timestamp of the lookup, even if that value is missing.

By returning the feature value that was recorded at the time the feature table was last updated.

By returning the most recent feature value that was recorded at or before the lookup timestamp for each entity.

By returning the feature value that was recorded after the lookup timestamp but before the next event for that entity.

Which Databricks SDK is used to configure online tables for low-latency serving?

The MLflow SDK, using the model registry API.

The Databricks SQL SDK, using the query API.

The Databricks SDK for Python, using the feature store API.

The Databricks CLI, using the cluster API.

A ride-sharing company needs to serve real-time features for a model that predicts trip demand. The model is called from a REST API and requires sub-100ms latency. The features are currently in a Delta table. Which configuration should they use to achieve low-latency serving?

Use a materialized view in the Delta table.

Cache the Delta table in a Spark cluster and query it.

Create an online table from the Delta table using the Databricks SDK.

Query the Delta table directly from the REST API.

Which Databricks capability is commonly used to implement streaming feature ingestion?

Structured Streaming with Delta Lake.

DatabricksCertified Machine Learning Professional

Domain 1Objective 4

Advanced Feature Store Concepts MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 3)

Part of the Model Development 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 6–9 from this objective — we provide 19 practice questions to prepare you well beyond it. (estimate)

19questions here

4free pages

6concepts

44%of the exam

Questions 11–15

11foundation · easy
How does a feature store enforce temporal consistency during a point-in-time lookup?
Select an answer first
12application · medium
A retail company is training a model to predict next-day sales. The training data contains transaction timestamps, and the feature store contains daily aggregated features like average purchase value and number of returns. The data scientist joins the feature table to the training data using the transaction timestamp as the lookup key. Which property of the feature store ensures that each training row uses only features that were available at that transaction's time, preventing future information from leaking into the model?
Select an answer first
13foundation · easy
Which Databricks SDK is used to configure online tables for low-latency serving?
Select an answer first
14application · medium
A ride-sharing company needs to serve real-time features for a model that predicts trip demand. The model is called from a REST API and requires sub-100ms latency. The features are currently in a Delta table. Which configuration should they use to achieve low-latency serving?
Select an answer first
15foundation · easy
Which Databricks capability is commonly used to implement streaming feature ingestion?
Select an answer first

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