This MLA-C01 practice question tests your understanding of data preparation for machine learning. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 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.)
Amazon S3.
S3 is natively integrated with SageMaker.
Amazon Redshift.
Why wrong: Redshift requires a cluster and connectors.
Amazon DynamoDB.
Why wrong: DynamoDB can be accessed via SDK but not directly without code.
Amazon RDS (MySQL).
Why wrong: RDS requires a VPC connection and driver setup.
Amazon Athena.
Athena can be queried directly from SageMaker notebooks.
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 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.
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.
Amazon S3.
Why this is correct
S3 is natively integrated with SageMaker.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
Under the hood, SageMaker Studio uses a JupyterLab environment with a pre-installed SageMaker Python SDK that includes the `s3fs` library, enabling file-system-like access to S3 via `s3://` URIs. For Athena, SageMaker Studio notebooks can use the AWS Data Wrangler library (now part of AWS SDK for pandas) or the `awswrangler` module to query Athena tables directly, leveraging the built-in IAM role without provisioning any compute or storage infrastructure. This direct access is possible because both S3 and Athena are serverless services that integrate natively with SageMaker's execution role and network configuration.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
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 24, 2026
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.
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