MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company wants to track the lineage of their ML models, including the training dataset, hyperparameters, and training job used to produce each model version. Which AWS service should they use?
⚠ Common exam trap
Watch out — candidates often confuse general-purpose data storage or cataloging services (like DynamoDB or Glue Data Catalog) with the specialized ML lineage tracking service, overlooking that SageMaker ML Lineage Tracking is the only AWS service designed to model the directed relationships between ML artifacts, actions, and contexts.
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
✓
SageMaker ML Lineage Tracking
SageMaker ML Lineage Tracking is the correct choice because it is purpose-built to record and query the provenance of ML models, capturing relationships between datasets, training jobs, hyperparameters, and model versions. It creates a directed acyclic graph (DAG) of entities (e.g., artifacts, actions, contexts) that allows you to trace how a specific model version was produced, which directly meets the requirement for lineage tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
SageMaker ML Lineage Tracking automatically records relationships between training datasets, hyperparameters, training jobs and resulting model versions, forming a queryable lineage graph. This directly satisfies the requirement to trace each model version back to its originating artefacts, unlike experiment tracking or model registry alone, which store metrics or versions without capturing those dependency links.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a key-value and document database requiring you to design and maintain your own lineage schema; it provides no native tracking of SageMaker training jobs, datasets or model versions. It is tempting because it is correct when you must store application state or custom records at scale.
- ✗
AWS Glue Data Catalog
Why it's wrong here
Glue Data Catalog stores table and schema metadata for data sources, not model, hyperparameter and training-job lineage. It is tempting because it is the correct choice for a central metadata repository that Athena, Redshift and EMR query for table definitions.
- ✗
Amazon S3 object tagging
Why it's wrong here
S3 object tagging attaches key-value metadata to individual objects; it does not record relationships between a model, its training dataset, hyperparameters and training job. It is tempting because tags are correct for cost allocation, lifecycle rules and access classification of stored objects.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.