- A
Enable SageMaker's default encryption for the training job by setting the EnableDefaultEncryption flag.
Why wrong: No such flag; default encryption uses AWS-managed keys.
- B
Create a CMK in AWS KMS and add the SageMaker service principal to the key policy to allow it to use the key.
SageMaker needs permission to use the CMK.
- C
Enable S3 default encryption using the CMK on all buckets containing training data.
Why wrong: This encrypts data at rest in S3, but not SageMaker's own storage volumes.
- D
Specify the CMK's ARN in the VolumeKmsKeyId parameter when creating the training job.
This encrypts the ML storage volume attached to the training instances.
- E
Use CloudWatch Logs encryption to protect the training logs.
Why wrong: Logs are not training data.
Question 865 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.
A financial services company must ensure that all data used by Amazon SageMaker training jobs is encrypted at rest. The company wants to use a customer-managed key (CMK) for the encryption. Which steps are necessary to achieve this? (Choose TWO.)
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
✓
Create a CMK in AWS KMS and add the SageMaker service principal to the key policy to allow it to use the key.
Option B is correct because to use a customer-managed key (CMK) for encrypting SageMaker training job data, you must first create a CMK in AWS KMS and then add the SageMaker service principal (sagemaker.amazonaws.com) to the key policy. This grants SageMaker the necessary permissions to use the key for encrypting the ML storage volume (e.g., EBS volumes) attached to the training instances. Without this policy statement, SageMaker cannot access the CMK, and the encryption request will fail.
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.
- ✗
Enable SageMaker's default encryption for the training job by setting the EnableDefaultEncryption flag.
Why it's wrong here
No such flag; default encryption uses AWS-managed keys.
- ✓
Create a CMK in AWS KMS and add the SageMaker service principal to the key policy to allow it to use the key.
Why this is correct
SageMaker needs permission to use the CMK.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable S3 default encryption using the CMK on all buckets containing training data.
Why it's wrong here
This encrypts data at rest in S3, but not SageMaker's own storage volumes.
- ✓
Specify the CMK's ARN in the VolumeKmsKeyId parameter when creating the training job.
Why this is correct
This encrypts the ML storage volume attached to the training instances.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use CloudWatch Logs encryption to protect the training logs.
Why it's wrong here
Logs are not training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse encrypting data at rest in S3 (via S3 default encryption) with encrypting the SageMaker training job's local storage volumes, which are separate and require explicit configuration via the VolumeKmsKeyId parameter.
Detailed technical explanation
How to think about this question
When you specify a CMK via the VolumeKmsKeyId parameter in a SageMaker training job, SageMaker uses that key to encrypt the EBS volumes attached to the training instances. These volumes store the training data, intermediate results, and model artifacts during the job. The key policy must include a grant for the SageMaker service principal to allow it to call kms:GenerateDataKey and kms:Decrypt on the CMK. Without this, SageMaker cannot create the encrypted volume, and the job will fail with an access denied error.
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
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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: Create a CMK in AWS KMS and add the SageMaker service principal to the key policy to allow it to use the key. — Option B is correct because to use a customer-managed key (CMK) for encrypting SageMaker training job data, you must first create a CMK in AWS KMS and then add the SageMaker service principal (sagemaker.amazonaws.com) to the key policy. This grants SageMaker the necessary permissions to use the key for encrypting the ML storage volume (e.g., EBS volumes) attached to the training instances. Without this policy statement, SageMaker cannot access the CMK, and the encryption request will fail.
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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