- A
Set the S3 bucket policy to require aws:SourceArn to match the SageMaker endpoint and allow kms:GenerateDataKey and kms:Decrypt.
Why wrong: GenerateDataKey is not needed for inference; also the condition may not restrict access to the role only.
- B
Create a KMS grant to allow the SageMaker service to use the key on behalf of the role, and set the S3 bucket to use AWS-managed SSE-S3.
Why wrong: SSE-S3 is not customer-managed KMS, failing the encryption requirement.
- C
Configure the KMS key policy to allow s3:PutObject and s3:GetObject for the SageMaker role, and enable S3 default encryption with the KMS key.
Why wrong: KMS key policy cannot grant S3 actions, and default encryption does not enforce role-only access.
- D
Use envelope encryption by generating a data key and storing it alongside the model artifact.
Why wrong: This approach is not integrated with KMS and does not meet the requirement for customer-managed keys.
- E
Attach a policy to the SageMaker role that allows kms:Decrypt on the KMS key, and set an S3 bucket policy that denies all access unless the request uses server-side encryption with the KMS key.
Correct. The role can decrypt, and the bucket policy enforces SSE-KMS, preventing unencrypted access.
Quick Answer
The answer is to attach a policy to the SageMaker execution role granting kms:Decrypt on the customer-managed KMS key, and to configure an S3 bucket policy that denies all PutObject requests unless they include the x-amz-server-side-encryption header set to aws:kms with the specific key ARN. This dual-layer configuration is correct because the SageMaker role needs the Decrypt permission to read encrypted model artifacts during inference, while the S3 bucket policy enforces that all uploaded artifacts are encrypted at rest with the designated KMS key, preventing any unencrypted storage. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how SageMaker model artifact encryption with KMS and access control requires both IAM and resource-based policies to work together—a common trap is thinking a bucket policy alone suffices, but the role must also have explicit kms:Decrypt. Remember the mnemonic: "Role decrypts, bucket enforces" to keep the two policy layers straight.
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. 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 financial services company uses SageMaker to train and deploy models. They must ensure that all model artifacts stored in S3 are encrypted at rest using customer-managed KMS keys. Additionally, only the SageMaker service role should have access to the encryption key for decrypting artifacts during inference. Which IAM policy configuration meets these requirements?
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
Attach a policy to the SageMaker role that allows kms:Decrypt on the KMS key, and set an S3 bucket policy that denies all access unless the request uses server-side encryption with the KMS key.
The role must have kms:Decrypt permission, and the S3 bucket policy must enforce SSE-KMS to ensure encryption with the correct key.
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.
- ✗
Set the S3 bucket policy to require aws:SourceArn to match the SageMaker endpoint and allow kms:GenerateDataKey and kms:Decrypt.
Why it's wrong here
GenerateDataKey is not needed for inference; also the condition may not restrict access to the role only.
- ✗
Create a KMS grant to allow the SageMaker service to use the key on behalf of the role, and set the S3 bucket to use AWS-managed SSE-S3.
Why it's wrong here
SSE-S3 is not customer-managed KMS, failing the encryption requirement.
- ✗
Configure the KMS key policy to allow s3:PutObject and s3:GetObject for the SageMaker role, and enable S3 default encryption with the KMS key.
Why it's wrong here
KMS key policy cannot grant S3 actions, and default encryption does not enforce role-only access.
- ✗
Use envelope encryption by generating a data key and storing it alongside the model artifact.
Why it's wrong here
This approach is not integrated with KMS and does not meet the requirement for customer-managed keys.
- ✓
Attach a policy to the SageMaker role that allows kms:Decrypt on the KMS key, and set an S3 bucket policy that denies all access unless the request uses server-side encryption with the KMS key.
Why this is correct
Correct. The role can decrypt, and the bucket policy enforces SSE-KMS, preventing unencrypted access.
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 MLA-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.
- →
ML Solution Monitoring, Maintenance and Security — study guide chapter
Learn the concepts, then practise the questions
- →
ML Solution Monitoring, Maintenance and Security practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
507 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Attach a policy to the SageMaker role that allows kms:Decrypt on the KMS key, and set an S3 bucket policy that denies all access unless the request uses server-side encryption with the KMS key. — The role must have kms:Decrypt permission, and the S3 bucket policy must enforce SSE-KMS to ensure encryption with the correct key.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
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 →
Keep practising
More MLA-C01 practice questions
- A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality…
- A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accura…
- A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices shou…
- A company is using SageMaker to train a neural network for image classification. The training job is taking too long. Th…
- A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target vari…
- A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multipl…
Last reviewed: Jun 23, 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.
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.
Sign in to join the discussion.