MLS-C01 Machine Learning Implementation and Operations Practice Test 10 — 15 Questions

Question 1

A company is using Amazon SageMaker to train a deep learning model for image classification. The training job is using a single p3.2xlarge instance and takes 10 hours. The data scientist wants to reduce training time using distributed training. Which SageMaker feature should be used?

Accepted Answer

Use the SageMaker distributed data parallelism library with multiple p3.2xlarge instances.. Option A is correct because SageMaker's distributed data parallelism automatically splits batches across GPUs and synchronizes gradients, reducing training time. Option B is wrong because model parallelism is for models too large for a single GPU. Option C is wrong because Hyperparameter Tuning does not distribute training. Option D is wrong because Managed Spot Training saves cost but does not reduce training time.

Answer

Use SageMaker Managed Spot Training to reduce cost, but training time remains the same.

Answer

Use SageMaker Hyperparameter Tuning to find optimal hyperparameters faster.

Answer

Use the SageMaker distributed model parallelism library with a single p3dn.24xlarge instance.

Question 2

A company is deploying a machine learning model using Amazon SageMaker. The model needs to be updated frequently with new data. Which TWO approaches can be used to update the model without downtime? (Choose TWO.)

Accepted Answer

Use SageMaker A/B testing to gradually shift traffic to the new model variant.. Option A and C are correct because A/B testing allows gradual rollout, and blue/green deployment with production variants ensures zero downtime. Option B is wrong because recreating the endpoint causes downtime. Option D is wrong because updating the model artifact while endpoint is active is not supported without redeployment. Option E is wrong because stopping the endpoint causes downtime.

Answer

Delete the existing endpoint and create a new one with the updated model.

Answer

Directly update the model artifact in the existing endpoint configuration.

Answer

Stop the endpoint, update the model, and restart the endpoint.

Question 3

A data scientist is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error message indicating that the container exited with a non-zero code. Which THREE steps should the data scientist take to diagnose the issue? (Choose THREE.)

Accepted Answer

Use the SageMaker Debugger to capture system metrics and output tensors for analysis.. Option A, B, and D are correct because checking CloudWatch logs, testing locally with SageMaker Local Mode, and using the SageMaker Debugger can help identify the error. Option C is wrong because increasing instance count does not fix the error. Option E is wrong because Retry may mask the issue.

Answer

Retry the training job with the same configuration; the error might be transient.

Answer

Increase the number of training instances to distribute the workload.

Question 4

A company is building a machine learning pipeline on AWS. The pipeline includes data ingestion, preprocessing, training, and deployment. Which THREE AWS services can be used to orchestrate the pipeline? (Choose THREE.)

Accepted Answer

AWS Glue Workflows. Option A, B, and C are correct because Step Functions, SageMaker Pipelines, and AWS Glue Workflows provide orchestration capabilities. Option D is wrong because Lambda is a compute service, not a workflow orchestrator. Option E is wrong because CloudWatch is for monitoring.

Answer

Amazon CloudWatch

Answer

AWS Lambda

Question 5

A data scientist wants to deploy a PyTorch model for real-time inference with low latency. Which AWS service should they use?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints provide low-latency inference for custom models. Option A is wrong because SageMaker batch transform is for offline processing. Option B is wrong because AWS Lambda has a 15-minute timeout and is not optimized for large model inference. Option D is wrong because Amazon ECS requires more operational overhead.

Answer

Amazon Elastic Container Service (ECS)

Answer

Amazon SageMaker batch transform

Answer

AWS Lambda

Question 6

A company's ML model training on Amazon SageMaker is taking longer than expected. The training job uses a single ml.p3.2xlarge instance. Which change is most likely to reduce training time?

Accepted Answer

Use distributed training with multiple GPU instances. Distributed training across multiple GPUs can parallelize computation. Option A is wrong because more vCPUs without GPU may not help for deep learning. Option C is wrong because Managed Spot Training may interrupt and not speed up. Option D is wrong because increasing instance storage does not affect compute speed.

Answer

Increase the instance's EBS volume size

Answer

Enable Managed Spot Training

Answer

Switch to a compute-optimized instance with more vCPUs

Question 7

A team is using Amazon SageMaker Autopilot to automatically build models. The dataset has 50 features and 1 million rows. After training, Autopilot generates multiple candidates. The team wants to deploy the model with the highest accuracy. What is the best practice to select and deploy the model?

Accepted Answer

Deploy the candidate with the highest objective metric value from the Autopilot leaderboard. SageMaker Autopilot's best candidate is determined by the objective metric. Option B is wrong because deploying all candidates wastes resources. Option C is wrong because the highest accuracy may not generalize; using the best objective metric is standard. Option D is wrong because manual selection is subjective.

Answer

Deploy all candidates behind a multi-model endpoint and route traffic based on request features

Answer

Select the model with the highest validation accuracy after performing additional hyperparameter tuning

Answer

Manually review each candidate's architecture and select the one with the simplest design

Question 8

An ML engineer needs to store and version training datasets and model artifacts. Which AWS service should they use?

Accepted Answer

Amazon Simple Storage Service (S3). Amazon S3 provides scalable object storage with versioning. Option A is wrong because EBS is block storage for single EC2 instances. Option C is wrong because EFS is file storage for shared access. Option D is wrong because DynamoDB is a NoSQL database.

Answer

Amazon DynamoDB

Answer

Amazon Elastic File System (EFS)

Answer

Amazon Elastic Block Store (EBS)

Question 9

A team is training a large language model using PyTorch on multiple GPUs. The training is taking too long due to inefficient data loading. Which AWS service can help accelerate data loading by caching data close to the GPU instances?

Accepted Answer

Amazon FSx for Lustre. Amazon EBS Snapshots are not for caching. FSx for Lustre provides high-performance file system optimized for ML workloads. Option B is wrong because S3 Transfer Acceleration speeds up uploads, not loading. Option D is wrong because CloudFront is a CDN for web content.

Answer

Amazon EBS Snapshots for fast restore

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon CloudFront

Question 10

A company's ML pipeline uses AWS Step Functions to orchestrate data preprocessing, training, and evaluation. The training step occasionally fails due to a transient error. What is the most robust way to handle this without manual intervention?

Accepted Answer

Implement a retry policy with exponential backoff on the training step in the state machine. Retry with exponential backoff handles transient errors. Option A is wrong because a separate Lambda adds complexity. Option B is wrong because it only alerts, doesn't fix. Option D is wrong because it restarts the whole workflow, wasting resources.

Answer

Configure a CloudWatch alarm to notify the team when the step fails

Answer

Use a parallel state to run multiple training instances simultaneously

Answer

Use a custom Lambda function to catch the error and restart the training step

Question 11

A data scientist needs to perform hyperparameter optimization for a gradient boosting model. Which built-in Amazon SageMaker feature should they use?

Accepted Answer

Amazon SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning performs hyperparameter optimization. Option A is wrong because SageMaker Debugger is for monitoring. Option C is wrong because SageMaker Neo is for model compilation. Option D is wrong because SageMaker Clarify is for bias detection.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Neo

Question 12

A company's ML model is deployed on a SageMaker endpoint. The model's predictions are used in a customer-facing application that requires low latency. Over time, the model's performance degrades due to data drift. What is the most suitable approach to detect this drift automatically?

Accepted Answer

Enable Amazon SageMaker Model Monitor to continuously check for data drift. SageMaker Model Monitor can detect data drift automatically. Option A is wrong because CloudWatch alarms are for infrastructure metrics, not drift. Option B is wrong because S3 events trigger on object changes, not drift. Option D is wrong because retraining on all data is inefficient.

Answer

Set up a CloudWatch alarm on the endpoint's invocation latency

Answer

Periodically retrain the model using all historical data

Answer

Use Amazon S3 events to trigger a Lambda function that compares distributions

Question 13

An ML team is using SageMaker Processing jobs to run feature engineering scripts. The scripts require a specific Python package not included in the default SageMaker image. How should the team provide this package?

Accepted Answer

Create a custom Docker image that includes the package and use it for the Processing job. A custom container allows full control over dependencies. Option A is wrong because pip install in script is not persisted. Option B is wrong because SageMaker doesn't support requirements.txt directly. Option D is wrong because a prebuilt image may not have the package.

Answer

Include 'pip install <package>' in the processing script

Answer

Use the SageMaker prebuilt deep learning container with the package

Answer

Place a requirements.txt file in the input data S3 bucket

Question 14

Which TWO options are valid ways to reduce inference latency for a model deployed on a SageMaker real-time endpoint? (Select TWO.)

Accepted Answer

Enable SageMaker Neo to compile the model for the target instance. Using a GPU instance can accelerate model computations. Enabling SageMaker Neo compilation optimizes the model for target hardware. Option C is wrong because increasing timeout does not reduce latency. Option D is wrong because batch transform is not real-time. Option E is wrong because increased instance count does not directly reduce latency per request.

Answer

Use SageMaker batch transform instead of real-time endpoint

Answer

Deploy the model to multiple instances behind a load balancer

Answer

Increase the endpoint's invocation timeout

Question 15

Which THREE steps should be taken to secure a SageMaker notebook instance that accesses sensitive data? (Select THREE.)

Accepted Answer

Enable encryption at rest for the notebook's EBS volume. Using a VPC with no internet access keeps traffic private. IAM roles enforce least privilege access. Encryption at rest protects data on the notebook. Option D is wrong because root access should be disabled, not enabled. Option E is wrong because public internet access should be disabled for security.

Answer

Grant root access to the notebook instance for flexibility

Answer

Allow direct internet access from the notebook for downloading packages