Quick Answer
The answer is the model registry, along with Ground Truth and fully managed notebook instances, which are three core components of Amazon SageMaker. These components form the backbone of a complete machine learning pipeline: Ground Truth handles data labeling, notebooks provide a managed Jupyter environment for exploration and development, and the model registry catalogs and versions trained models for governance and deployment. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of SageMaker’s modular architecture rather than just its training or inference capabilities. A common trap is confusing SageMaker Studio—the integrated IDE—with a separate component, or listing services like Lambda or S3, which are supporting AWS services but not SageMaker components. Remember the mnemonic “G-N-R” (Ground Truth, Notebooks, Registry) to recall the three distinct building blocks that cover data prep, development, and model management.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is evaluating different AWS services for building a machine learning pipeline. Which THREE components are part of Amazon SageMaker? (Select THREE.)
AWS Glue
Why wrong: Glue is a managed ETL service, independent of SageMaker.
Notebook instances
SageMaker Notebook Instances are fully managed Jupyter notebooks.
Ground Truth
Ground Truth is a SageMaker feature for creating and managing data labeling workflows.
Model registry
SageMaker Model Registry helps catalog, version, and manage models.
Amazon Athena
Why wrong: Athena is a serverless query service for S3, not part of SageMaker.
Answer choices
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 provides fully managed notebook instances that allow data scientists to spin up Jupyter notebooks for data exploration, preprocessing, and model development without managing underlying infrastructure. These instances come pre-installed with common ML frameworks and can be easily scaled.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Glue is a managed ETL service, independent of SageMaker.
Notebook instances
Why this is correct
SageMaker Notebook Instances are fully managed Jupyter notebooks.
Related concept
Read the scenario before looking for a memorised answer.
Ground Truth
Why this is correct
Ground Truth is a SageMaker feature for creating and managing data labeling workflows.
Related concept
Read the scenario before looking for a memorised answer.
Model registry
Why this is correct
SageMaker Model Registry helps catalog, version, and manage models.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Athena
Why it's wrong here
Athena is a serverless query service for S3, not part of SageMaker.
Common exam traps
The trap here is that 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.
Detailed technical explanation
SageMaker Notebook instances are EC2-based instances that include pre-configured kernels for Python, R, and popular ML libraries like TensorFlow and PyTorch. They integrate with SageMaker Experiments for tracking and SageMaker Model Registry for versioning, forming a cohesive MLOps pipeline. Ground Truth uses a combination of automated labeling and human workers via Amazon Mechanical Turk or private workforces to create high-quality training datasets.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: Notebook instances — Amazon SageMaker provides fully managed notebook instances that allow data scientists to spin up Jupyter notebooks for data exploration, preprocessing, and model development without managing underlying infrastructure. These instances come pre-installed with common ML frameworks and can be easily scaled.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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.
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