Databricks-ML-Assoc ML Workflows Practice Question
A team is using Databricks Feature Store to create a feature table that will be used for both batch training and online inference. They need to ensure the feature table supports point-in-time lookups for training and low-latency reads for serving. Which TWO of the following statements are correct about meeting these requirements? (Choose two.)
⚠ Common exam trap
The trap here is assuming that online stores are created automatically or that a specific file format is required for low-latency reads, when publishing is an explicit step.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The feature table must be published to an online store to support low-latency online inference.
To support both batch training and online inference, the feature table must be published to an online store for low-latency reads, and point-in-time lookups are handled using the primary key and timestamp key during training set creation. Parquet storage and automatic online store creation are not requirements, and partitioning is optional.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Point-in-time lookups require the feature table to be partitioned by the timestamp key.
Why it's wrong here
While partitioning by date can improve performance, point-in-time lookups do not require partitioning; they rely on the timestamp key column and the Feature Store's lookup logic. Partitioning is optional and not a prerequisite. The create_training_set method works with or without partitioning, though performance may vary.
- ✓
The feature table must be published to an online store to support low-latency online inference.
Why this is correct
Databricks Feature Store can publish feature tables to an online store (e.g., DynamoDB, Cosmos DB, or online tables) to serve features with low latency. Without publishing, the feature table is only available for batch reads from Delta, which is not suitable for real-time serving. Publishing creates a synchronized online copy that Model Serving can query.
- ✗
Online stores are automatically created when a feature table is registered, requiring no additional configuration.
Why it's wrong here
Publishing to an online store is an explicit operation; online stores are not created automatically upon registration. The user must configure and publish the feature table to a supported online store. This step is necessary to enable low-latency serving. Assuming automatic creation would leave the table unavailable for online inference.
- ✓
Point-in-time lookups are automatically handled by the feature table's primary key and timestamp key during training.
Why this is correct
Feature Store uses the primary key and timestamp key to perform point-in-time lookups, ensuring that for each label event, only feature values with timestamps at or before the event time are used. This prevents label leakage. The create_training_set method leverages these keys automatically when constructing the training dataset.
- ✗
The feature table must be stored in Parquet format to enable low-latency reads.
Why it's wrong here
Feature tables in Databricks Feature Store are Delta tables; Parquet is the underlying file format but the table abstraction is Delta. Low-latency online reads are achieved by publishing to an online store, not by choosing Parquet. Storing as Parquet alone does not provide the required serving latency or point-in-time semantics.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-ML-Assoc exam.