MLA-C01 ML Solution Monitoring, Maintenance and Security Practice Test 4 — 15 Questions

Question 1

A company wants to monitor their machine learning model for bias over time. Which THREE AWS services or features can they use to achieve this? (Choose THREE.)

Accepted Answer

Amazon SageMaker Clarify. Options A, B, and C are correct. SageMaker Clarify can detect bias in training data and predictions. SageMaker Model Monitor can track bias metrics over time if configured. SageMaker Pipelines can include bias check steps for automated monitoring. Option D is for tracking experiments, not monitoring bias. Option E is for API logging.

Answer

Amazon SageMaker Experiments

Answer

AWS CloudTrail

Question 2

A company stores training data in Amazon S3 and uses Amazon SageMaker for model training. They need to ensure data is encrypted at rest. Which THREE encryption options are supported by SageMaker for data stored in S3? (Choose THREE.)

Accepted Answer

SSE-C (customer-provided keys). Options A, B, and C are correct because SageMaker supports all three Amazon S3 server-side encryption options. Option D is not supported for automatic decryption by SageMaker. Option E is for content delivery, not storage encryption.

Answer

Client-side encryption

Answer

Amazon CloudFront encryption

Question 3

Refer to the exhibit. A team observes that their SageMaker endpoint scales out quickly when load increases, but scales in very slowly when load decreases, causing over-provisioning. What is the most likely cause?

Accepted Answer

ScaleInCooldown is too high. Option B is correct because the ScaleInCooldown is 600 seconds (10 minutes), meaning the system waits 10 minutes after a scale-in activity before triggering another scale-in action. This delay causes slow scale-in. Option A would affect scale-out speed. Option C relates to target value. Option D is incorrect because the metric is appropriate.

Answer

TargetValue is too high

Answer

ScaleOutCooldown is too low

Answer

Wrong predefined metric selected

Question 4

Refer to the exhibit. A team receives an error when running a SageMaker Model Monitor schedule for data quality. What should they do to resolve this issue?

Accepted Answer

Create a baseline job using the training dataset. Option B is correct because Model Monitor requires a baseline (constraints and statistics) generated from the training data. The error indicates the baseline is missing. Option A enables capture but does not resolve baseline. Option C is incorrect because the schedule is fine but baseline missing. Option D is about permissions, not baseline.

Answer

Update the IAM role to allow S3 access

Answer

Restart the monitoring schedule

Answer

Enable data capture on the endpoint

Question 5

Refer to the exhibit. A user has the above IAM policy attached but cannot access files in SageMaker Studio. What additional permission is most likely needed?

Accepted Answer

s3:GetObject on the relevant S3 buckets. Option B is correct because SageMaker Studio users need S3 read/write permissions to access data files stored in S3 buckets. The policy only allows creating a presigned URL for Studio, but not S3 access. Option A is for listing apps, not files. Option C is for user profiles. Option D is for KMS decryption if applicable, but not the most common cause.

Answer

sagemaker:ListApps

Answer

sagemaker:DescribeUserProfile

Answer

kms:Decrypt

Question 6

A data science team deploys a machine learning model to a SageMaker endpoint for real-time inference. They need to monitor the model for feature distribution drift over time to ensure the model's predictions remain accurate. Which AWS service should they use?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor is specifically designed to detect drift in feature distributions and prediction quality over time.

Answer

Amazon CloudWatch Evidently

Answer

AWS Glue DataBrew

Answer

SageMaker Clarify

Answer

SageMaker Debugger

Question 7

A company's SageMaker endpoint is experiencing increased latency during peak hours. The endpoint uses a single ml.m5.large instance. The deployment is critical and must maintain low latency. Which action is MOST effective to reduce latency without sacrificing cost efficiency?

Accepted Answer

Add an auto-scaling policy based on request count. Adding auto-scaling based on request count allows the endpoint to handle spikes without over-provisioning, balancing cost and latency.

Answer

Deploy multiple variants with A/B testing

Answer

Use Elastic Inference to attach an accelerator

Answer

Switch to a ml.c5.large instance

Answer

Enable SageMaker Model Monitor

Question 8

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?

Accepted Answer

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.

Answer

Set the S3 bucket policy to require aws:SourceArn to match the SageMaker endpoint and allow kms:GenerateDataKey and kms:Decrypt.

Answer

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.

Answer

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.

Answer

Use envelope encryption by generating a data key and storing it alongside the model artifact.

Question 9

A machine learning engineer is monitoring a production SageMaker endpoint using Amazon CloudWatch. They want to set up alarms for anomalous behavior. Which TWO CloudWatch metrics are MOST appropriate for detecting a sudden increase in request latency?

Accepted Answer

ModelLatency. ModelLatency directly measures request latency, and CPUUtilization can indicate resource saturation leading to latency increases.

Answer

5XXError

Answer

MemoryUtilization

Answer

Invocations

Question 10

A data science team detects that a deployed model's prediction accuracy is degrading over time due to concept drift. They need to implement a retraining strategy. Which THREE actions are recommended best practices for handling concept drift?

Accepted Answer

Monitor prediction quality using ground truth labels when available.. Monitoring prediction quality, using drift detection to trigger retraining, and incrementally updating the model are key practices.

Answer

Automatically roll back to a previous model version upon drift detection.

Answer

Retrain the model on a fixed schedule regardless of performance.

Question 11

A company operates multiple AWS accounts with SageMaker workloads. They need to implement governance and security controls for model monitoring and maintenance. Which THREE actions should they take to meet compliance requirements?

Accepted Answer

Deploy a SageMaker model registry in a centralized account.. CloudTrail logging, AWS Config rules for encryption, and a centralized model registry help enforce governance across accounts.

Answer

Enable VPC Flow Logs for SageMaker notebooks.

Answer

Use IAM roles with cross-account trust policies for all SageMaker endpoints.

Question 12

Refer to the exhibit. A data engineer investigates why a SageMaker endpoint is returning errors. The endpoint configuration has been updated to point to a new model version. What is the MOST likely cause of the error?

Accepted Answer

The endpoint is still using the previous configuration.. The error indicates the model 'my-model-v2' does not exist in SageMaker, suggesting the endpoint configuration still references an older model that was not updated correctly.

Answer

The endpoint instance type is insufficient.

Answer

The container image for the new model is not compatible.

Answer

The IAM role does not have permission to invoke the endpoint.

Answer

The new model artifact is not properly uploaded to S3.

Question 13

Refer to the exhibit. A data scientist uses a SageMaker notebook instance to read a model file from S3 bucket 'my-bucket'. The bucket uses SSE-KMS encryption with a KMS key. The IAM role attached to the notebook has the above policy. However, reading the file fails. What is the MOST likely reason?

Accepted Answer

The condition requires encryption to be AES256, which is SSE-S3, but the bucket uses KMS.. The S3 condition requires server-side encryption to be AES256 (SSE-S3), but the bucket uses SSE-KMS, so the S3 request does not satisfy the condition, denying access.

Answer

The resource ARN for S3 does not include the bucket itself (only objects inside).

Answer

The policy allows s3:GetObject only if server-side encryption is AES256, but the bucket uses KMS.

Answer

The kms key ARN is incorrect.

Answer

The policy allows kms:Decrypt but does not allow kms:GenerateDataKey.

Question 14

Refer to the exhibit. A data engineer runs a SageMaker processing job that fails. What is the MOST likely cause of the failure?

Accepted Answer

The input file does not exist at the specified S3 path.. The failure reason explicitly states the input file cannot be read and advises checking the path or file existence.

Answer

The processing instance type is too small.

Answer

The processing job code has a bug.

Answer

The S3 bucket is in a different region.

Answer

The IAM role does not have s3:GetObject permission.

Question 15

A company wants to maintain multiple versions of a trained model in a central repository and track metadata such as training metrics, hyperparameters, and approval status. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is designed for model versioning, metadata tracking, and approval workflows.

Answer

SageMaker Pipelines

Answer

SageMaker Feature Store

Answer

SageMaker Experiments

Answer

SageMaker Studio