MLS-C01 Practice Test 24 — 15 Questions

Question 1

A SageMaker endpoint creation fails with the above CloudWatch Logs excerpt. What is the MOST likely cause?

Accepted Answer

The model file is too large and takes longer than 300 seconds to load. Option C is correct because the model file may be too large to load within the default timeout. Option A (missing inference script) would cause a different error. Option B (incorrect S3 permissions) would appear earlier. Option D (instance type) would cause resource issues but not a timeout on loading.

Answer

The S3 bucket containing the model artifacts has incorrect permissions

Answer

The inference script has a syntax error

Answer

The instance type does not have enough memory to load the model

Question 2

A data scientist is deploying a model to a SageMaker endpoint and needs to optimize for cost while maintaining low latency. Which TWO actions should the data scientist take?

Accepted Answer

Use SageMaker Serverless Inference. Options A and D are correct. Option A: Serverless Inference automatically scales and costs are based on usage. Option D: Auto Scaling adjusts instance count based on traffic, reducing cost during low demand. Option B is wrong because larger instances increase cost. Option C is wrong because batch transform is not real-time. Option E is wrong because reducing instances increases latency.

Answer

Use a larger instance type

Answer

Deploy to a single instance

Answer

Switch to batch transform

Question 3

A machine learning engineer needs to deploy a model that performs real-time inference with strict latency requirements of under 100 milliseconds. The model is a large ensemble of 10 deep learning models. Which SageMaker deployment strategy is MOST appropriate?

Accepted Answer

Use a SageMaker Inference Pipeline with serial inference within a single endpoint.. Large ensemble models can be deployed using SageMaker Inference Pipelines to chain multiple containers. Real-time endpoints with a single variant are standard for low latency. Multi-model endpoints are for multiple models, not ensembles. Batch transform is for offline. Multi-variant endpoints are for A/B testing.

Answer

Use batch transform and cache predictions.

Answer

Deploy each model as a separate endpoint and route traffic using Application Load Balancer.

Answer

Use a multi-model endpoint to host all models.

Question 4

A data scientist needs to run a one-time training job on a large dataset using SageMaker. The job requires a specific PyTorch version and custom dependencies. Which approach is MOST efficient?

Accepted Answer

Use the SageMaker PyTorch estimator with a pre-built container.. SageMaker provides pre-built deep learning containers (DLCs) for PyTorch. Using a SageMaker framework estimator with a pre-built PyTorch container is the easiest and most efficient. The framework estimator automatically handles the container and dependencies. Creating a custom container is more work. Using the generic container requires manually installing dependencies. Using a notebook instance is for interactive development, not one-time training.

Answer

Create a custom Docker container and push to ECR.

Answer

Launch a SageMaker notebook instance, install dependencies, and run training script.

Answer

Use the SageMaker generic container and install PyTorch via a lifecycle configuration.

Question 5

A machine learning engineer is designing an automated ML pipeline for training and deploying models. The pipeline must include data validation, model training, hyperparameter tuning, and model deployment. The engineer wants to use AWS services that integrate well and provide version control. Which THREE services should be combined to achieve this? (Choose THREE.)

Accepted Answer

AWS Step Functions. Option A (AWS Step Functions) orchestrates the pipeline steps. Option C (Amazon SageMaker) provides training, tuning, and deployment. Option D (AWS CodePipeline) manages version control and automation of the entire CI/CD workflow. Option B (AWS Glue) is for ETL but not pipeline orchestration. Option E (Amazon EMR) is for big data processing, not ML pipeline management.

Answer

AWS Glue

Answer

Amazon EMR

Question 6

An IAM policy is attached to a SageMaker notebook instance. The data scientist wants to use the notebook to train a model using data from S3 bucket 'my-bucket'. However, the training job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The notebook instance role does not have iam:PassRole permission to pass the SageMaker execution role. Option B is correct because the notebook instance role needs permission to pass the execution role to SageMaker (iam:PassRole). The policy allows s3 and sagemaker actions but not PassRole. Option A is incorrect because GetObject and PutObject are allowed. Option C is incorrect because SageMaker actions are allowed. Option D is incorrect because the resource is correct.

Answer

The sagemaker:CreateTrainingJob permission is not allowed on the specific resource

Answer

The S3 bucket resource ARN is incorrectly formatted

Answer

The s3:GetObject permission is missing for the bucket

Question 7

A data scientist runs the AWS CLI command shown in the exhibit. The output shows that job-2 failed. Which action should the data scientist take to diagnose the failure?

Accepted Answer

Run `aws sagemaker describe-training-job --training-job-name job-2` to see the failure reason. Option A is correct because the DescribeTrainingJob API provides detailed status and failure reason. Option B (list-training-jobs) already used. Option C (CloudWatch Logs) is useful but the first step is to get the failure reason from DescribeTrainingJob. Option D (S3 logs) is not standard.

Answer

Check the CloudWatch Logs log group for job-2

Answer

Check the S3 bucket for any error logs uploaded by the training job

Answer

Run `aws sagemaker list-training-jobs --name-contains job-2` to get more details

Question 8

A company is using Amazon SageMaker to train a model and wants to automatically retrain the model every week using new data. Which AWS service should be used to orchestrate the retraining pipeline?

Accepted Answer

AWS Step Functions. AWS Step Functions is the recommended service for orchestrating complex workflows, including SageMaker training jobs, model creation, and deployment. Lambda is for short functions. CloudWatch Events can trigger on a schedule but not orchestrate a pipeline. Data Pipeline is older and less flexible. S3 Events trigger on object creation but not scheduling.

Answer

Amazon CloudWatch Events

Answer

AWS Lambda

Answer

AWS Data Pipeline

Question 9

Which THREE measures can help reduce inference latency for a deep learning model deployed on SageMaker real-time endpoints? (Select THREE.)

Accepted Answer

Enable SageMaker Neo to compile the model.. To reduce latency, use GPU instances, enable model compilation with SageMaker Neo, reduce input size, and use multi-model endpoints to share resources. However, multi-model endpoints add latency when switching models. Increasing batch size usually increases latency per request but can improve throughput. The three correct measures are: use GPU instances, enable SageMaker Neo, and reduce input data size.

Answer

Increase the batch size for inference.

Answer

Use a multi-model endpoint to share the instance.

Question 10

A data scientist is using AWS Glue to prepare training data. The job reads from an S3 bucket, performs transformations, and writes to another S3 bucket. The job is failing due to insufficient memory. Which solution should the data scientist use to fix this?

Accepted Answer

Increase the number of DPU (Data Processing Units) for the job.. Option C is correct because increasing the number of workers adds more memory. Option A is wrong because changing the file format does not increase memory. Option B is wrong because it's for ETL performance. Option D is wrong because using Athena is not a job.

Answer

Use AWS Glue's job bookmark feature.

Answer

Use Amazon Athena instead of AWS Glue.

Answer

Use a columnar file format like Parquet.

Question 11

A machine learning engineer is deploying a TensorFlow model to an Amazon SageMaker endpoint. The endpoint is behind an Application Load Balancer (ALB) for A/B testing. The engineer notices that the new variant is not receiving any traffic. What is the most likely cause?

Accepted Answer

The ALB target group weight for the new variant is set to 0.. Option A is correct because the ALB target group weight determines traffic distribution. Option B is wrong because routing is based on weights, not health checks if healthy. Option C is wrong because SageMaker endpoint is the target. Option D is wrong because it would cause errors, not zero traffic.

Answer

The new variant's health checks are failing.

Answer

The model is not compatible with the ALB's protocol.

Answer

The ALB is not configured to route to SageMaker endpoints.

Question 12

A data scientist is reviewing the training logs from a SageMaker training job. The model's loss decreases steadily and accuracy increases. However, when the model is evaluated on a holdout test set, the accuracy is only 0.65. Which issue does this behavior suggest?

Accepted Answer

The model is overfitting to the training data.. Option C is correct because low test accuracy indicates overfitting. Option A is wrong because loss and accuracy improve. Option B is wrong because it's not a data issue. Option D is wrong because it's not underfitting.

Answer

The learning rate is too high.

Answer

The model is underfitting the training data.

Answer

The training data is corrupted.

Question 13

A company uses SageMaker to train a model each night. The training data is stored in an S3 bucket with SSE-S3 encryption. The training job fails with an access denied error. Which configuration is needed?

Accepted Answer

Create an IAM role with S3 read access and assign it to the SageMaker training job. Option C is correct because SageMaker needs an IAM role with S3 permissions and the role must be passed to the training job. Option A is wrong because KMS is not used with SSE-S3. Option B is wrong because bucket policy alone is insufficient without the SageMaker role. Option D is wrong because VPC is not related to encryption.

Answer

Configure the training job to run in a VPC with S3 VPC Endpoint

Answer

Enable SSE-KMS on the S3 bucket

Answer

Add a bucket policy allowing s3:GetObject for all principals

Question 14

Refer to the exhibit. A SageMaker endpoint is returning 5xx errors. The logs show the above error. Which change will most likely resolve the issue?

Accepted Answer

Use a larger instance type with more memory. Option B is correct because increasing memory (e.g., upgrading instance type) addresses the OutOfMemoryError. Option A is wrong because batch size is not relevant for inference (single request). Option C is wrong because the error is memory, not model file size. Option D is wrong because AutoScaling does not change instance memory.

Answer

Reduce the batch size in the inference script

Answer

Enable Auto Scaling on the endpoint

Answer

Compress the model artifact

Question 15

A company uses SageMaker to run training jobs on a schedule. The training data is stored in an S3 bucket that receives new data every hour. Which TWO approaches can the company use to trigger a training job when new data arrives?

Accepted Answer

Set up an Amazon EventBridge rule that triggers on S3 Object Created events and targets a Lambda function. Options B and D are correct. Option B: S3 events can invoke Lambda to start training. Option D: EventBridge rule can trigger on custom events. Option A is wrong because CloudWatch Logs are for logs, not events. Option C is wrong because SQS is not directly integrated with SageMaker. Option E is wrong because Step Functions would need a trigger.

Answer

Use an SQS queue to buffer S3 events and poll from a training instance

Answer

Use AWS Step Functions with a scheduled execution

Answer

Use CloudWatch Logs to monitor S3 access logs and trigger a Lambda function

MLS-C01 Practice Test 24 — 15 Questions

Exhibit

MLS-C01 Practice Test 24 — 15 Questions

Exhibit