- A
Provide the same KMS key for both model artifacts and inference data.
Why wrong: Using the same key is an option but not required; different keys can be used with proper permissions.
- B
Use a customer-managed key (CMK) with the same key material.
Why wrong: Key material is irrelevant; permissions are key.
- C
Grant the SageMaker execution role access to both KMS keys.
The role needs decrypt on the artifact key and encrypt/decrypt on the inference key.
- D
Configure the endpoint to use SSE-S3 instead of SSE-KMS.
Why wrong: Changing encryption type would require re-uploading artifacts.
Question 119 of 1,000
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 trained a model using SageMaker and stored the model artifacts in S3 with server-side encryption using AWS KMS (SSE-KMS). They need to deploy the model to a SageMaker endpoint that uses a different KMS key for inference data encryption. What must they do to ensure the endpoint can decrypt the model artifacts?
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
✓
Grant the SageMaker execution role access to both KMS keys.
Option C is correct because the SageMaker endpoint needs to decrypt the model artifacts stored with SSE-KMS using the original KMS key, and then re-encrypt the inference data with a different KMS key. The SageMaker execution role must have kms:Decrypt permission on the key used for the model artifacts and kms:Encrypt permission on the key used for inference data encryption. Without granting access to both keys, the endpoint cannot read the model artifacts or encrypt the output.
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.
- ✗
Provide the same KMS key for both model artifacts and inference data.
Why it's wrong here
Using the same key is an option but not required; different keys can be used with proper permissions.
- ✗
Use a customer-managed key (CMK) with the same key material.
Why it's wrong here
Key material is irrelevant; permissions are key.
- ✓
Grant the SageMaker execution role access to both KMS keys.
Why this is correct
The role needs decrypt on the artifact key and encrypt/decrypt on the inference key.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure the endpoint to use SSE-S3 instead of SSE-KMS.
Why it's wrong here
Changing encryption type would require re-uploading artifacts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume the same key must be used for both operations or that identical key material makes keys interchangeable, but AWS KMS treats each key as a separate resource with distinct ARNs and policies, requiring explicit permissions for each.
Detailed technical explanation
How to think about this question
Under the hood, when SageMaker loads model artifacts from S3 with SSE-KMS, it uses the SageMaker execution role to call kms:Decrypt on the KMS key that encrypted the artifacts. For inference data encryption at the endpoint, SageMaker uses the specified KMS key to call kms:Encrypt for output data. The execution role must have both kms:Decrypt and kms:Encrypt permissions on the respective keys, and the KMS key policy must also allow the role to use the key. A real-world scenario is when a team uses a centralized key for model storage (e.g., from a data lake) but a separate key for production inference to isolate data access.
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 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.
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.
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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: Grant the SageMaker execution role access to both KMS keys. — Option C is correct because the SageMaker endpoint needs to decrypt the model artifacts stored with SSE-KMS using the original KMS key, and then re-encrypt the inference data with a different KMS key. The SageMaker execution role must have kms:Decrypt permission on the key used for the model artifacts and kms:Encrypt permission on the key used for inference data encryption. Without granting access to both keys, the endpoint cannot read the model artifacts or encrypt the output.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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