MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer needs to provide the data science team with access to various data sources for machine learning. The team uses Amazon SageMaker Studio. Which TWO data sources can be accessed directly from SageMaker Studio notebooks without additional infrastructure? (Choose two.)
⚠ Common exam trap
Many exam-takers assume any AWS database service (like Redshift, DynamoDB, or RDS) can be accessed 'directly' from SageMaker Studio, but the exam specifically tests the distinction between services that require additional infrastructure (VPC, endpoints, or client libraries) and those that are natively integrated without extra setup.
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
✓
Amazon S3.
Amazon SageMaker Studio notebooks have a built-in SageMaker SDK that can directly read from and write to Amazon S3 using the `s3fs` filesystem or the SageMaker `s3_utils` module. This integration requires no additional infrastructure because S3 is the default storage backend for SageMaker, and the notebook environment is pre-configured with the necessary IAM roles and boto3 libraries to access S3 buckets directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon S3.
Why this is correct
S3 is natively integrated with SageMaker.
- ✗
Amazon Redshift.
Why it's wrong here
Redshift requires a cluster and connectors.
- ✗
Amazon DynamoDB.
Why it's wrong here
DynamoDB can be accessed via SDK but not directly without code.
- ✗
Amazon RDS (MySQL).
Why it's wrong here
RDS requires a VPC connection and driver setup.
- ✓
Amazon Athena.
Why this is correct
Athena can be queried directly from SageMaker notebooks.
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
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Written by Johnson Ajibi, MSc IT Security
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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.