MLS-C01 Machine Learning Implementation and Operations Practice Test 18 — 15 Questions

Question 1

A company is using Amazon SageMaker to train a large natural language processing model. The training job uses a GPU instance and is expected to take several hours. The data scientist wants to monitor GPU utilization in real-time. Which approach is MOST effective?

Accepted Answer

Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console. Option A is correct because SageMaker publishes CloudWatch metrics for GPU utilization. Option B uses a custom solution when a built-in one exists. Option C is for debugging, not real-time monitoring. Option D is for managed spot training, not monitoring.

Answer

Use SageMaker Managed Spot Training to reduce cost and monitor utilization via spot instance status

Answer

Modify the training script to periodically log GPU utilization to a file in S3

Answer

Use SageMaker Debugger to capture GPU utilization tensors

Question 2

A data scientist needs to deploy a trained model to Amazon SageMaker for real-time inference. The model is stored as a .tar.gz file in Amazon S3. Which AWS service is used to create a SageMaker endpoint?

Accepted Answer

SageMaker Model and Endpoint Configuration. Option B is correct because a SageMaker endpoint requires a model and an endpoint configuration. Options A, C, D are not required for creating the endpoint.

Answer

AWS Lambda

Answer

AWS CloudFormation

Answer

Amazon ECS

Question 3

A company uses Amazon SageMaker to train machine learning models. The training data contains personally identifiable information (PII). The company needs to ensure that the data is encrypted in transit between S3 and SageMaker. Which configuration is REQUIRED?

Accepted Answer

Use SageMaker in a VPC with VPC endpoints for S3. Option A is correct because SageMaker uses HTTPS by default for S3 access. Option B is for data at rest. Option C is optional. Option D is not required for encryption in transit.

Answer

Enable S3 server-side encryption

Answer

Set the S3 bucket policy to require SSL

Answer

Use a custom Docker image with TLS configured

Question 4

A machine learning engineer is using Amazon SageMaker to train a model. The training job is taking too long. The engineer suspects the data loading is a bottleneck. Which action would MOST effectively diagnose the issue?

Accepted Answer

Use SageMaker Debugger to profile the training job. Option C is correct because SageMaker Debugger can profile the training job and identify bottlenecks. Option A may add overhead. Option B is not detailed. Option D is for inference.

Answer

Monitor CPU utilization in CloudWatch

Answer

Enable SageMaker Model Monitor

Answer

Increase the instance type to a larger one

Question 5

A company is using Amazon SageMaker to run a hyperparameter tuning job. The tuning job uses Bayesian optimization. Which THREE statements about Bayesian optimization are correct? (Choose THREE.)

Accepted Answer

It works well for continuous hyperparameters. Options A, B, and D are correct. Bayesian optimization uses past results to choose hyperparameters (A), it often finds better values than random search (B), and it is suitable for continuous hyperparameters (D). Option C is false because Bayesian optimization typically requires fewer trials than grid search. Option E is false because it can handle many hyperparameters, though may need more trials.

Answer

It can only handle a maximum of 5 hyperparameters

Answer

It requires more trials than grid search to find optimal values

Question 6

A data scientist wants to deploy a PyTorch model for real-time inference. Which SageMaker deployment option provides the lowest latency for single-digit millisecond responses?

Accepted Answer

SageMaker Real-Time Inference endpoint. SageMaker Real-Time Inference endpoints are designed for low-latency, real-time predictions. Option B is correct because SageMaker Serverless Inference can have cold starts and higher latency. Option C is for batch processing. Option D is for asynchronous inference with higher latency.

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Serverless Inference

Answer

SageMaker Batch Transform

Question 7

A team is training a large NLP model using SageMaker. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 61 GB GPU memory. Which action should the team take to resolve the issue without changing the model architecture?

Accepted Answer

Reduce the batch size in the training script. Reducing the batch size decreases GPU memory usage per iteration. Option A is correct. Option B changes the problem to regression. Option C increases memory usage. Option D is unrelated to GPU memory.

Answer

Switch to a regression model

Answer

Increase the number of epochs

Answer

Enable SageMaker Managed Warm Pools

Question 8

A company uses SageMaker Pipelines to automate model retraining. The pipeline fails intermittently at the Preprocess step with a 'ResourceLimitExceeded' error. The team uses a ml.m5.xlarge instance. What is the most likely cause?

Accepted Answer

The account has reached the limit for concurrent ml.m5.xlarge instances. The error indicates reaching a service quota. SageMaker has a default limit of concurrent training jobs per account. Option A is correct. Option B would cause different errors. Option C is unrelated. Option D would cause a SageMaker service error, not a resource limit.

Answer

The preprocessing script has a memory leak

Answer

The S3 bucket has insufficient permissions

Answer

The pipeline execution role is missing the PassRole permission

Question 9

A machine learning engineer needs to store and version datasets for reproducibility. Which AWS service is designed for this purpose?

Accepted Answer

SageMaker Feature Store. SageMaker Feature Store stores, manages, and versions features for ML. Option B is correct. Option A is a general object store. Option C is for code versioning. Option D is for data warehousing.

Answer

AWS CodeCommit

Answer

Amazon S3

Answer

Amazon Redshift

Question 10

A data scientist uses SageMaker to train a model. The training job takes 10 hours, but the team needs to reduce costs. Which approach is MOST cost-effective?

Accepted Answer

Enable Managed Spot Training. Spot instances can reduce costs up to 90%. Managed Spot Training is the most cost-effective. Option C is correct. Option A increases cost. Option B may reduce time but not necessarily cost. Option D is for hyperparameter tuning.

Answer

Use SageMaker Automatic Model Tuning

Answer

Use a larger instance type to finish faster

Answer

Use SageMaker Distributed Training with more instances

Question 11

A company deploys a SageMaker endpoint for real-time inference. After a week, the response latency increases from 50 ms to 500 ms. CPU utilization is at 30%. What is the most likely cause?

Accepted Answer

The inference code makes a call to a downstream service that is throttling requests. Increased latency with low CPU suggests a bottleneck elsewhere, often due to throttling by downstream services like a database. Option B is correct. Option A would show high CPU. Option C would cause errors. Option D would cause high memory usage.

Answer

The model has a memory leak

Answer

The instance type is underpowered for the inference load

Answer

The SageMaker endpoint is experiencing a network outage

Question 12

A team needs to automatically retrain a model every week using new data. Which SageMaker feature is designed to schedule and automate this workflow?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines allows building end-to-end ML workflows with scheduling. Option A is correct. Option B is for model monitoring. Option C is for feature engineering. Option D is for automatic model tuning.

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Model Monitor

Answer

SageMaker Data Wrangler

Question 13

A model deployed on a SageMaker endpoint is producing predictions that are consistently biased against a certain demographic. Which step should the team take FIRST to address this issue?

Accepted Answer

Use SageMaker Clarify to analyze bias in the training data and predictions. The first step is to analyze the data and model for bias. SageMaker Clarify can detect bias. Option B is correct. Option A is a solution after analysis. Option C changes model. Option D is a general practice.

Answer

Enable SageMaker Model Monitor to track prediction quality

Answer

Switch to a different algorithm that is less prone to bias

Answer

Retrain the model with balanced data

Question 14

A machine learning team is using SageMaker to train a model with a custom Docker container. The training script runs locally but fails on SageMaker with a 'Permission denied' error when writing to /opt/ml/model. What is the likely cause?

Accepted Answer

The container's user does not have write permission to /opt/ml/model. SageMaker expects the container to write the model artifact to /opt/ml/model. If the user in the container lacks write permissions, it fails. Option C is correct. Option A is unrelated. Option B would cause different errors. Option D is about reading data.

Answer

The Docker image is too large

Answer

The training script is trying to read from /opt/ml/input/data instead of /opt/ml/input/data/training

Answer

The training data is not in the correct S3 bucket

Question 15

A company wants to monitor SageMaker endpoints for data drift. Which TWO services can be used together to detect and alert on drift?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor detects drift and CloudWatch Alarms can send alerts. Options A and D are correct. Option B is for code, Option C is for logs, Option E is for data preparation.

Answer

SageMaker Data Wrangler

Answer

AWS CodePipeline

Answer

Amazon CloudWatch Logs