Question 310 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A team is collaborating on a machine learning project and needs to ensure that data used for training is consistent across experiments. The team wants to version datasets, track data lineage, and be able to reproduce past experiments. The team uses SageMaker for model training. Which combination of services and features should the team use?
⚠ Common exam trap
Candidates often confuse S3 versioning alone with full data lineage and experiment tracking, overlooking the need for a governance layer like Lake Formation and a dedicated experiment tracking service like SageMaker Experiments to tie datasets to specific training runs.
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
✓
Use SageMaker with AWS Lake Formation to manage data access, version datasets in S3, and use SageMaker Experiments to track training jobs.
It combines AWS Lake Formation for fine-grained data access control and governance, S3 versioning for dataset versioning, and SageMaker Experiments to track training jobs and lineage. This trio directly addresses the need for consistent data across experiments, versioning, lineage tracking, and reproducibility in SageMaker.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Pipelines to automate training and store datasets in S3 with versioning enabled.
Why it's wrong here
Pipelines help but lack native dataset versioning; S3 versioning alone is not enough.
- ✗
Store datasets in Amazon DynamoDB and use Amazon Athena to query specific versions.
Why it's wrong here
DynamoDB is not designed for large dataset storage.
- ✓
Use SageMaker with AWS Lake Formation to manage data access, version datasets in S3, and use SageMaker Experiments to track training jobs.
Why this is correct
This combination provides data versioning, lineage, and experiment tracking.
- ✗
Use S3 versioning to store all dataset versions and AWS Glue Data Catalog to track schema changes.
Why it's wrong here
This provides versioning but not easy experiment tracking or lineage.
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 |
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 24, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.