AIF-C01 Fundamentals of AI and ML Practice Question
A data scientist is evaluating different AWS services for building a machine learning pipeline. Which THREE components are part of Amazon SageMaker? (Select THREE.)
⚠ Common exam trap
Candidates often confuse AWS Glue (a separate ETL service) as part of SageMaker because both are used in ML pipelines, but Glue is not a SageMaker component.
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
✓
Notebook instances
Amazon SageMaker is a fully managed ML platform, and three of the listed items are native SageMaker capabilities. B (Notebook instances) is correct because SageMaker provides managed Jupyter notebook instances for data exploration, preprocessing, and model development. C (Ground Truth) is correct because SageMaker Ground Truth is the built-in data labeling service used to create high-quality training datasets, including with automated labeling and human review workflows. D (Model registry) is correct because SageMaker includes a model registry for cataloging, versioning, and managing trained models through approval and deployment stages. A (AWS Glue) is a separate ETL and data catalog service, and E (Amazon Athena) is a separate serverless query service for S3 data, so neither is a component of 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.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a separate extract-transform-load service for cataloguing and preparing data, and is not a SageMaker component. It is tempting because Glue frequently feeds SageMaker pipelines, but integration does not make it part of the platform; SageMaker's own components include Studio, Autopilot, Data Wrangler, Feature Store, Pipelines, Debugger and Model Monitor.
- ✓
Notebook instances
Why this is correct
Notebook instances are a core SageMaker component, providing managed Jupyter environments for data exploration, preprocessing and model development within the pipeline. They satisfy the stem's requirement to identify genuine SageMaker features, unlike unrelated AWS analytics or storage services that lack integrated notebook tooling.
- ✓
Ground Truth
Why this is correct
Amazon SageMaker Ground Truth provides data labelling for supervised learning datasets, satisfying the pipeline's need for annotated training data. It integrates directly with SageMaker training jobs, unlike standalone labelling tools. Ground Truth is therefore a genuine SageMaker component, making it one of the three correct selections.
- ✓
Model registry
Why this is correct
Model registry is a core SageMaker component, providing centralised versioning, approval status and metadata tracking for trained models. It satisfies the pipeline's need to catalogue and govern model artefacts across training and deployment stages, integrating directly with SageMaker Pipelines and Model Monitor rather than requiring external tooling.
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena is a standalone serverless query service for data in Amazon S3, and is not a SageMaker component. It is tempting because Athena is commonly used to explore training datasets before modelling, but co-usage does not make it part of the platform; SageMaker's components include Studio, Data Wrangler, Feature Store, Pipelines, Debugger and Model Monitor.
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 |
Go deeper
Related to this question
About these practice questions
Courseiva writes every AIF-C01 question from scratch — 862 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-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 AIF-C01 exam.