The answer is to create a custom Docker image that includes the required Python package and use it for the SageMaker Processing job. This is correct because SageMaker Processing jobs run in isolated containers, and any dependencies must be baked into the image at build time; a pip install command inside a script is ephemeral and lost after the job ends, while SageMaker does not natively parse a requirements.txt file for Processing jobs. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of containerization for custom environments, often appearing as a trap where candidates mistakenly think they can install packages on the fly or rely on prebuilt images that may lack specific libraries. The key distinction is that SageMaker Processing requires a fully self-contained image, not runtime modifications. Memory tip: “Bake it, don’t break it”—always build dependencies into the image before the job starts.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An ML team is using SageMaker Processing jobs to run feature engineering scripts. The scripts require a specific Python package not included in the default SageMaker image. How should the team provide this package?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a custom Docker image that includes the package and use it for the Processing job
A custom container allows full control over dependencies. Option A is wrong because pip install in script is not persisted. Option B is wrong because SageMaker doesn't support requirements.txt directly. Option D is wrong because a prebuilt image may not have the package.
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.
✗
Include 'pip install <package>' in the processing script
Why it's wrong here
Works but slows down every run; not best practice.
✗
Use the SageMaker prebuilt deep learning container with the package
Why it's wrong here
Prebuilt containers may not have the required package.
✗
Place a requirements.txt file in the input data S3 bucket
Why it's wrong here
SageMaker does not automatically install from it.
✓
Create a custom Docker image that includes the package and use it for the Processing job
Why this is correct
Standard best practice for custom dependencies.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a custom Docker image that includes the package and use it for the Processing job — A custom container allows full control over dependencies. Option A is wrong because pip install in script is not persisted. Option B is wrong because SageMaker doesn't support requirements.txt directly. Option D is wrong because a prebuilt image may not have the package.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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