MLA-C01 Practice Test 22 — 15 Questions

Question 1

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 2

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

Question 3

A company uses Amazon SageMaker Ground Truth to create a labeled dataset. They want to monitor the accuracy of human labelers during the labeling process. Which metric should they track?

Accepted Answer

Accuracy against blinded ground truth. Option C is correct because tracking accuracy against blinded ground truth (known as audit tasks) is the standard way to measure labeler performance. Options A and B are operational metrics not directly measuring accuracy. Option D is not directly accuracy.

Answer

Labeling job cost

Answer

Number of tasks completed

Answer

Task acceptance rate

Question 4

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 5

An e-commerce company uses a machine learning model to predict customer churn. They notice that the model's performance degrades after a major marketing campaign changes customer behavior. Which approach is MOST effective to detect and respond to this type of concept drift?

Accepted Answer

Use SageMaker Model Monitor to track prediction distribution and trigger retraining.. SageMaker Model Monitor can automatically detect drift in prediction distributions and trigger retraining pipelines.

Answer

Deploy an A/B test to compare the current model with a baseline.

Answer

Manually review model accuracy each month.

Answer

Set up a weekly batch transform job to compute accuracy against historical data.

Answer

Increase the number of instances for the endpoint.

Question 6

A machine learning engineer wants to encrypt model artifacts stored in Amazon S3. The artifacts are created and used by SageMaker training jobs and endpoints. What is the simplest way to ensure encryption at rest?

Accepted Answer

Create an S3 bucket with default encryption using SSE-S3 and allow SageMaker access.. Option A is correct because SSE-S3 is the simplest encryption method and works seamlessly with SageMaker. Option B is not a thing; SageMaker does not have default encryption for artifacts. Option C is possible but not simplest. Option D is for versioning, not encryption.

Answer

Use SageMaker's default encryption with an AWS managed key.

Answer

Enable S3 bucket versioning and MFA delete.

Answer

Use a custom KMS key and grant SageMaker permission to use it.

Question 7

A machine learning engineer is setting up automated retraining for a model using SageMaker Pipelines. The pipeline should trigger when a data drift alert is received from Model Monitor. Which event source should the engineer use to initiate the pipeline?

Accepted Answer

Amazon CloudWatch Events (Amazon EventBridge) rule that captures Model Monitor outcome.. Option D is correct because SageMaker Model Monitor publishes drift results as Amazon CloudWatch Events (now EventBridge). Option A: S3 events on monitoring output are not directly linked to drift alerts. Option B: no webhook exists. Option C: polling is inefficient.

Answer

AWS Lambda function that polls CloudWatch logs.

Answer

S3 event notification on the monitoring output bucket.

Answer

SageMaker model monitor webhook.

Question 8

A company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes in the underlying data distribution. Which monitoring solution should they implement to detect this issue automatically?

Accepted Answer

Set up Amazon SageMaker Model Monitor with data quality monitoring.. Option D is correct because Amazon SageMaker Model Monitor can monitor data quality and model quality drift. Option A (CloudWatch Logs) is for logs, not drift detection. Option B (CloudTrail) tracks API calls. Option C (AWS Config) tracks resource configuration.

Answer

Configure AWS Config rules to check the model accuracy metric.

Answer

Use AWS CloudTrail to monitor changes to the model's S3 bucket.

Answer

Enable Amazon CloudWatch Logs on the endpoint and set alarms on inference latency.

Question 9

A company uses SageMaker Model Monitor to detect drift. They want to receive notifications when drift is detected. Which TWO services can be used together to send notifications? (Choose TWO.)

Accepted Answer

Amazon SNS topics to send email or SMS.. Options B and D are correct. Amazon CloudWatch Alarm can trigger on a Model Monitor metric, and it can publish to an SNS topic to send email or SMS. Option A (Lambda) could be used but is not a direct notification service. Option C (SQS) is for queues. Option E (EventBridge) can route events but not directly send notifications; usually triggers Lambda.

Answer

Amazon EventBridge to trigger a notification.

Answer

AWS Lambda to process the drift and send an email via SES.

Answer

Amazon SQS to queue the notification.

Question 10

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 11

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 12

A team uses SageMaker Model Monitor to track data quality. They notice that the monitor's constraint violations are increasing but the model performance remains good. What should they do?

Accepted Answer

Investigate the specific features that are violating constraints to see if they are still relevant.. Option A is correct because investigating specific violating features helps determine if constraints are still relevant. Option B is reactive without analysis. Option C ignores the issue. Option D may be unnecessary if performance is still good.

Answer

Disable the monitor because it is not affecting performance.

Answer

Relax the constraint thresholds to reduce alerts.

Answer

Retrain the model using the latest data.

Question 13

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 14

A company's ML pipeline runs in multiple AWS accounts (dev, test, prod). They want to enforce that only approved models from a central Model Registry can be deployed to the production account. Which combination of services is MOST appropriate to implement this governance?

Accepted Answer

AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status.. AWS Organizations SCPs restrict actions, CodePipeline automates cross-account deployment, and Model Registry provides approval gates.

Answer

AWS Config, Amazon GuardDuty, and AWS Security Hub.

Answer

Amazon API Gateway, AWS Step Functions, and Amazon DynamoDB.

Answer

AWS Service Catalog, AWS KMS, and AWS CloudTrail.

Answer

AWS CloudFormation StackSets, Amazon EventBridge, and AWS Lambda.

Question 15

A company is using an Amazon SageMaker pipeline for automated retraining. The pipeline fails intermittently due to transient errors in the training job. Which steps should the team take to ensure the pipeline completes successfully? (Choose THREE.)

Accepted Answer

Enable managed spot training for cost savings and use checkpointing to resume from interruptions.. Options A, C, and E are correct. A: Add retry policies for the training step. C: Use spot instances with managed spot training to handle interruptions. E: Implement exponential backoff in custom code for API calls. Option B is wrong because increasing instance count does not solve transient errors; it adds cost. Option D is wrong because SageMaker does not support automatic checkpointing across retries; you need to implement custom checkpointing.

Answer

Use a larger instance type for the training job to reduce the chance of failure.

Answer

Implement automatic model checkpointing by setting the CheckpointConfig in the pipeline step.

MLA-C01 Practice Test 22 — 15 Questions

Exhibit

MLA-C01 Practice Test 22 — 15 Questions

Exhibit