MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The engineer needs to ensure that the feature group can serve both batch and real-time predictions. The feature group is configured with an online store enabled. Which additional configuration is required to support batch predictions?
⚠ Common exam trap
The trap here is assuming that the online store can handle batch predictions or that encryption or event time settings enable batch access, when in fact an offline store is required.
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
✓
Enable an offline store for the feature group and specify an S3 bucket for storage.
Feature Store's online store is for real-time serving, while the offline store is for batch serving and training. To support batch predictions, the feature group must have an offline store enabled, which stores feature data in S3. This allows the engineer to retrieve historical features in bulk for batch inference jobs. Without an offline store, only real-time serving is possible.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the online store's read capacity to handle batch loads.
Why it's wrong here
The online store is designed for high-throughput, low-latency reads of individual records, not for batch retrieval of large datasets. Increasing read capacity may improve performance for real-time serving but does not provide the capability to retrieve historical feature data in bulk. Batch predictions require an offline store.
- ✗
Set the feature group's event time to the ingestion time to allow batch queries.
Why it's wrong here
The event time is used to track the timestamp of each feature record and is important for point-in-time joins, but it does not enable batch predictions. Setting event time to ingestion time may affect data accuracy but does not provide an offline store. Batch predictions still require an offline store configuration.
- ✓
Enable an offline store for the feature group and specify an S3 bucket for storage.
Why this is correct
To support batch predictions, the feature group must have an offline store, which stores historical feature data in Amazon S3. The online store is optimized for low-latency real-time serving, but batch predictions require access to historical data. Enabling the offline store and specifying an S3 bucket allows batch retrieval of features for training and batch inference.
- ✗
Configure the feature group to use a custom KMS key for encryption, which enables batch access.
Why it's wrong here
Using a custom KMS key for encryption enhances security but does not enable batch predictions. Encryption is orthogonal to the storage and retrieval mechanisms. Batch predictions require an offline store; encryption alone does not provide that functionality.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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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 Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.