Match each AWS service to its primary purpose in a machine learning pipeline.
Drag a concept onto its matching description — or click a concept then click the description.
Build, train, and deploy ML models
ETL and data cataloging
Object storage for datasets and models
Serverless compute for preprocessing
Image and video analysis
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker: Fully managed service for building, training, and deploying machine learning models.
The correct matches associate each service with its primary ML pipeline role. SageMaker is for end-to-end ML, Lambda for serverless inference, S3 for data/model storage, Glue for data preparation. Distractors confuse these roles.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon SageMaker: Fully managed service for building, training, and deploying machine learning models.
Why this is correct
SageMaker provides a complete environment for ML workflows, from data labeling to model deployment.
AWS Lambda: Serverless compute service that can be used for real-time inference without managing servers.
Why this is correct
Lambda allows running code in response to events, ideal for lightweight inference tasks.
Amazon S3: Scalable object storage service for storing training data, models, and logs.
Why this is correct
S3 is used as a central data lake for ML artifacts due to its durability and low cost.
AWS Glue: Serverless ETL service for preparing and transforming data before training.
Why this is correct
Glue automates data cataloging and ETL jobs to clean and structure data for ML.
Amazon SageMaker: Serverless compute service for inference.
Why it's wrong here
Incorrect — this describes AWS Lambda, not SageMaker. SageMaker is a managed platform, not serverless compute.
AWS Lambda: Scalable object storage for models.
Why it's wrong here
Incorrect — this describes Amazon S3. Lambda is for compute, not storage.
Quick reference
| 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
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.