← MLS-C01·Amazon Web Services

Question 656 of 1,755

Data Engineering →easyMultiple ChoiceObjective-mapped

MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. 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 needs to train a machine learning model using a large dataset (500 GB) stored in an S3 bucket. The training will be performed on a SageMaker notebook instance. The data scientist wants to minimize data transfer costs and reduce training time. Which data ingestion approach should the data engineer recommend?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Use the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.
SageMaker can read data directly from S3, minimizing transfer and storage costs.
B
Copy the dataset to the notebook instance's attached EBS volume before training.
Why wrong: Copying data incurs transfer costs and uses local storage, increasing time and cost.
C
Load the dataset into an Amazon RDS database and query it from the notebook.
Why wrong: RDS is for transactional data, not for large ML datasets; incurs additional costs.
D
Mount the S3 bucket to the notebook instance using Amazon Elastic File System (EFS).
Why wrong: EFS is a network file system, not optimized for S3 access; it adds cost and complexity.

Full breakdown with real-world context →

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

✓

Use the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.

Option A is correct because the SageMaker SDK allows training jobs to read data directly from S3 using the Pipe or File mode, which avoids copying the 500 GB dataset to the notebook instance's EBS volume. This minimizes data transfer costs (no egress from S3 to the notebook) and reduces training time by streaming data directly to the training container without intermediate storage.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Use the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.
Why this is correct
SageMaker can read data directly from S3, minimizing transfer and storage costs.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Copy the dataset to the notebook instance's attached EBS volume before training.
Why it's wrong here
Copying data incurs transfer costs and uses local storage, increasing time and cost.
✗
Load the dataset into an Amazon RDS database and query it from the notebook.
Why it's wrong here
RDS is for transactional data, not for large ML datasets; incurs additional costs.
✗
Mount the S3 bucket to the notebook instance using Amazon Elastic File System (EFS).
Why it's wrong here
EFS is a network file system, not optimized for S3 access; it adds cost and complexity.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume they must copy data locally for faster access (Option B), not realizing that SageMaker's native S3 integration with Pipe mode is designed specifically to avoid that overhead and is the most cost-effective and performant approach for large datasets.

Detailed technical explanation

How to think about this question

SageMaker's Pipe mode streams data directly from S3 to the training algorithm via a FIFO pipe, allowing the model to start training without waiting for the entire dataset to download. This is especially beneficial for large datasets (e.g., 500 GB) because it reduces disk I/O bottlenecks and enables efficient use of GPU/CPU resources during training. In contrast, File mode downloads the entire dataset to the training instance's local storage before training begins, which can be slower and more costly for large datasets.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Related MLS-C01 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Data Engineering practice questions

Practise MLS-C01 questions linked to Data Engineering.

Machine Learning Implementation and Operations practice questions

Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.

Modeling practice questions

Practise MLS-C01 questions linked to Modeling.

Exploratory Data Analysis practice questions

Practise MLS-C01 questions linked to Exploratory Data Analysis.

MLS-C01 fundamentals practice questions

Practise MLS-C01 questions linked to MLS-C01 fundamentals.

MLS-C01 scenario practice questions

Practise MLS-C01 questions linked to MLS-C01 scenario.

MLS-C01 troubleshooting practice questions

Practise MLS-C01 questions linked to MLS-C01 troubleshooting.

Practice this exam

Start a free MLS-C01 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

MLS-C01 practice-test guide →Study guide →Browse all practice tests

FAQ

Questions learners often ask

What does this MLS-C01 question test?

Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook. — Option A is correct because the SageMaker SDK allows training jobs to read data directly from S3 using the Pipe or File mode, which avoids copying the 500 GB dataset to the notebook instance's EBS volume. This minimizes data transfer costs (no egress from S3 to the notebook) and reduces training time by streaming data directly to the training container without intermediate storage.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

Keep practising

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

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.