MLS-C01 Machine Learning Implementation and Operations Practice Test 9 — 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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console. Amazon SageMaker automatically publishes GPU utilization metrics (e.g., `GPUUtilization`, `GPUMemoryUtilization`) to Amazon CloudWatch for training jobs running on GPU instances. By enabling CloudWatch metrics (which is the default behavior for SageMaker training jobs), the data scientist can view real-time GPU utilization directly in the CloudWatch console without any code modifications. This is the most effective approach because it requires no changes to the training script and provides native, real-time 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 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. Amazon SageMaker uses HTTPS by default for all communication with Amazon S3, ensuring data is encrypted in transit. No additional configuration is required to achieve this. Option A is not required because VPC endpoints provide private connectivity but do not add encryption beyond what HTTPS already provides. Option B (S3 server-side encryption) protects data at rest. Option C (bucket policy requiring SSL) is already satisfied as SageMaker uses HTTPS. Option D (custom Docker image with TLS) is unnecessary as SageMaker handles TLS for S3 traffic by default.

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 3

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 4

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 is designed to store, manage, and version features for machine learning, ensuring reproducibility. Option A (CodeCommit) is for version control of code. Option B (S3) is an object store not specialized for ML features. Option D (Redshift) is a data warehouse. Therefore, Option C is correct.

Answer

AWS CodeCommit

Answer

Amazon S3

Answer

Amazon Redshift

Question 5

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The training data must be in RecordIO-protobuf or TFRecord format.. SageMaker Pipe mode requires training data to be in a format that can be streamed efficiently, specifically RecordIO-protobuf or TFRecord. These formats allow the algorithm to read data sequentially without random access, which is essential for streaming from S3. Other formats like CSV or JSON require File mode.

Answer

The training data must be compressed using Gzip.

Answer

The S3 bucket must have public read access.

Answer

The training data must be stored as a single large file.

Question 6

A company is using Amazon Forecast for demand forecasting. The data includes time series data for multiple items. The company wants to ensure that the forecast is updated daily as new data arrives. Which approach should be used to automate this process?

Accepted Answer

Enable the 'AutoPredictor' feature in Forecast to automatically update the predictor when new data arrives.. Amazon Forecast's AutoPredictor feature automatically retrains the predictor when new data is imported via CreateDatasetImportJob, eliminating the need for manual scheduling. Option A is incorrect because it mentions CreatePredictorBacktestExportJob, which is used for exporting backtest results, not for retraining the predictor. The correct API for retraining is CreatePredictor. Option B is wrong because Forecast does not support real-time data ingestion via Kinesis. Option C is wrong because Amazon SageMaker is not used for Forecast retraining; Forecast handles its own model training.

Answer

Use AWS Lambda to invoke the Forecast CreateDatasetImportJob and CreatePredictorBacktestExportJob APIs on a daily schedule triggered by Amazon CloudWatch Events.

Answer

Use Amazon Kinesis Data Streams to stream new data directly into Forecast.

Answer

Use Amazon SageMaker to retrain the model daily and replace the forecast endpoint.

Question 7

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team has limited budget and wants to minimize labeling costs while ensuring high-quality labels. Which approach is MOST cost-effective?

Accepted Answer

Use active learning to automatically label high-confidence data and send only uncertain data to a private workforce.. Active learning in SageMaker Ground Truth uses a machine learning model to automatically label data points where the model has high confidence, and only sends low-confidence or ambiguous items to human labelers. This dramatically reduces the number of human annotations required, directly lowering labeling costs while maintaining label quality on the uncertain cases. It is the only option that explicitly balances cost minimization with quality assurance through selective human involvement.

Answer

Use only a private workforce of domain experts to label all data.

Answer

Use a public workforce and have each data point labeled by three workers.

Answer

Use the built-in automated labeling feature without human review.

Question 8

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The SageMaker execution role does not have the necessary permissions to access the S3 bucket.. Even with a bucket policy granting cross-account access, the SageMaker execution role must have an IAM policy that allows s3:GetObject (and any other required actions) on the S3 bucket. Without these permissions, the training job will fail with AccessDenied. Option A is incorrect because SSE-KMS encryption would require KMS permissions, but the issue is specifically about access permissions, not encryption. Option C is incorrect because the data does not need to be public; cross-account access via bucket policy is sufficient. Option D is incorrect because cross-account access does not require a VPC endpoint.

Answer

The data is encrypted with SSE-KMS and the SageMaker role lacks KMS permissions.

Answer

The S3 bucket is not configured with public access.

Answer

The S3 bucket is in a different region and requires a VPC endpoint.

Question 9

A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives a large number of requests with variable traffic patterns. The team wants to minimize cost while ensuring low latency. Which THREE actions should the team take? (Choose THREE.)

Accepted Answer

Use a multi-model endpoint to host multiple models on the same instance.. Option A is correct because a multi-model endpoint (MME) hosts multiple models behind a single endpoint on shared instances, which reduces the number of running instances and thus cost when serving several models. Option B is correct because enabling auto-scaling on the endpoint using the InvocationsPerInstance metric (target-tracking) lets capacity follow variable traffic, scaling in during low demand to save cost and scaling out to preserve low latency. Option E is correct because choosing a smaller instance type for the production variant lowers the hourly cost per instance while still meeting latency requirements if the model fits. Option C is not appropriate because setting variant weight to 1 and increasing instances statically over-provisions and raises cost rather than minimizing it. Option D is not appropriate because a single large instance handles all traffic without elasticity, causing either high cost or latency degradation under variable load.

Answer

Set the initial variant weight to 1 and increase the number of instances.

Answer

Use a single large instance to handle all traffic.

Question 10

A machine learning team is using Amazon SageMaker to train a model. The training job uses spot instances to reduce cost. However, the training job is frequently interrupted. Which TWO actions can help mitigate the impact of spot interruptions? (Choose TWO.)

Accepted Answer

Use managed spot training with SageMaker's 'ManagedSpotTraining' parameter set to True.. Option C is correct because SageMaker's managed spot training (enabled via the ManagedSpotTraining parameter, i.e., EnableManagedSpotTraining=True in the API) automatically handles spot interruptions by pausing the job and waiting for capacity to return, then resuming from the last checkpoint, so the job continues without manual intervention. Option D is correct because checkpointing writes intermediate model artifacts and training state to Amazon S3 (configured via CheckpointConfig with a S3Uri), allowing the job to resume from the most recent checkpoint after an interruption instead of restarting from scratch, which directly mitigates the impact of spot interruptions. Option A does not help because adding training instances increases cost and parallelism but does nothing to preserve progress or reduce the effect of an interruption. Option B is not a reliable mitigation because spot interruption likelihood depends on instance capacity and bid dynamics, not simply on instance size, and a larger instance type does not guarantee fewer interruptions. Option E would avoid interruptions but it eliminates the cost savings of spot instances, so it does not mitigate the impact of spot interruptions as asked.

Answer

Increase the number of training instances.

Answer

Use a larger instance type that is less likely to be interrupted.

Answer

Switch to on-demand instances.

Question 11

A data scientist is training a deep learning model on SageMaker using a custom Docker container. The training job fails with an error indicating that the container exited with a non-zero status. The CloudWatch logs show 'FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/input/data/training/data.csv''. What is the most likely cause?

Accepted Answer

The S3 data source path is incorrect or the data has not been uploaded.. The error indicates the training script tried to open '/opt/ml/input/data/training/data.csv' but the file was not there. In SageMaker, the training data is downloaded from S3 to that path based on the 'training' input channel configuration. If the S3 URI is wrong, the bucket/key does not exist, or the data was never uploaded, the file will be missing. This is the most likely cause.

Answer

The container's Docker entry point is misconfigured.

Answer

The training script has a syntax error.

Answer

The model output path in the training job configuration is wrong.

Question 12

A machine learning engineer is deploying a model using SageMaker and wants to use automatic scaling for the endpoint based on the number of concurrent requests. The engineer has defined a scaling policy using the SageMakerVariantInvocationsPerInstance metric. However, the scaling is not triggering as expected. What could be the issue?

Accepted Answer

The scaling policy does not have a cooldown period configured, or the cooldown period is too long.. A missing or excessively long cooldown period can prevent the scaling policy from triggering. Cooldown periods (default 300 seconds) allow metrics to stabilize before initiating another scaling activity. Option A is incorrect because scheduled scaling actions are separate from dynamic scaling policies and are not required. Option C is incorrect because the SageMakerVariantInvocationsPerInstance metric is automatically published to CloudWatch. Option D is incorrect because this metric is specifically designed for automatic scaling.

Answer

A scheduled scaling action must be created first.

Answer

The metric must be published to CloudWatch manually.

Answer

The metric is not available for automatic scaling.

Question 13

A company is using SageMaker to train a linear regression model on a dataset that fits into memory on a single instance. The training job is taking longer than expected. The data scientist wants to reduce training time without changing the algorithm. Which approach is most effective?

Accepted Answer

Use SageMaker's distributed training with multiple instances.. SageMaker's distributed training with multiple instances performs data parallelism, splitting the dataset across instances to train concurrently, which reduces training time for data that fits in memory. Option A is wrong because disabling automatic model tuning (hyperparameter tuning) does not speed up training; it only stops searching for optimal hyperparameters. Option B is wrong because while a larger instance with more vCPUs can help, distributed training scales better and is more cost-effective for this scenario. Option D is wrong because reducing the number of epochs would likely underfit the model, hurting accuracy.

Answer

Disable automatic model tuning.

Answer

Use a larger instance type with more vCPUs.

Answer

Reduce the number of epochs.

Question 14

A company is using SageMaker to deploy a model for real-time inference. The model requires low latency, and the company wants to test the endpoint before production. Which approach should be used to validate endpoint performance?

Accepted Answer

Use SageMaker's built-in shadow testing or load testing features.. SageMaker provides features like shadow testing, which allows you to test a new variant alongside the existing production variant without impacting live traffic, and integration with load testing tools to simulate traffic and measure latency before full production deployment. Option A is incorrect because CloudWatch Synthetics is used for monitoring endpoint health and availability, not for pre-production load or performance testing. Option B is incorrect because offline batch evaluation assesses model accuracy on a static dataset, but does not test real-time inference performance metrics such as latency and throughput. Option C is incorrect because deploying directly to production and monitoring exposes users to potential performance issues; pre-production validation should be conducted first.

Answer

Use CloudWatch Synthetics to create a canary.

Answer

Perform offline batch evaluation on a test dataset.

Answer

Deploy to production and monitor using CloudWatch.

Question 15

A company is deploying a machine learning model to a SageMaker endpoint and wants to ensure that the endpoint is resilient to instance failures. Which THREE steps should the company take to achieve high availability? (Choose THREE.)

Accepted Answer

Deploy the endpoint in a VPC with subnets in at least two Availability Zones.. Option A is correct because deploying the SageMaker endpoint in a VPC with subnets spanning at least two Availability Zones allows SageMaker to place instances across physically separate data centers, so an AZ-level failure does not take down the entire endpoint. Option C is correct because configuring an initial instance count of at least 2 ensures there is more than one instance behind the endpoint, eliminating a single point of failure and enabling continued inference if one instance fails. Option E is correct because enabling auto-scaling lets SageMaker automatically detect and replace unhealthy instances and adjust capacity, maintaining availability without manual intervention. Option B is not correct because using a single, largest instance type concentrates capacity on one instance and does not provide redundancy across instances or AZs. Option D is not correct because confining the endpoint to a single Availability Zone creates a single point of failure, which directly contradicts the goal of high availability.

Answer

Use a single instance type with the largest size to handle capacity.

Answer

Use a single Availability Zone for simplicity.