MLS-C01 Practice Test 28 — 15 Questions

Question 1

Refer to the exhibit. An ML engineer creates a CloudFormation stack with this template. The stack creation succeeds, but when the engineer tries to invoke the endpoint, it returns a ModelError. The CloudWatch logs show that the container exited with error. What is the MOST likely cause?

Accepted Answer

The execution role does not have permissions to pull the Docker image from ECR.. The template does not specify a VPC configuration for the endpoint. By default, SageMaker endpoints are not in a VPC and cannot access resources in a VPC unless configured. However, the model artifact is in S3 (s3://my-bucket/model.tar.gz), which is accessible without VPC. The most common cause of ModelError is that the container image is not compatible with the instance type or the model file is missing. But given the template, a likely issue is that the execution role (SageMakerRole) does not have permissions to access the ECR image or S3 bucket. The error is not about VPC (A) or instance count (B) or endpoint config (D).

Answer

The initial instance count is set to 2, which is insufficient for the model size.

Answer

The endpoint is not deployed in a VPC and cannot access the S3 bucket.

Answer

The EndpointConfig references the model but the model is not yet created.

Question 2

A machine learning team is using Amazon SageMaker Experiments to track multiple training runs. They need to compare the performance of different models based on metrics like accuracy and F1 score. However, when they view the experiment list in SageMaker Studio, the metrics are not displayed. What is the MOST likely cause?

Accepted Answer

The training script did not use the SageMaker SDK to log the metrics.. Option C is correct because metrics must be explicitly logged via the SageMaker SDK's log_metric() or in training job definition to appear in Experiments. Option A is wrong because permission to view is separate from metric capture. Option B is wrong because the metric definitions in the training job are optional but not required for logging. Option D is wrong because Experiments run on any instance type.

Answer

The training job did not define metric definitions in the algorithm specification.

Answer

The training job is running on an instance type that does not support Experiments.

Answer

The IAM role used by SageMaker does not have permission to write to the Experiments table.

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

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 5

A company is using SageMaker Autopilot to automatically build ML models. They want to ensure that the generated models are reproducible. Which TWO settings should they configure?

Accepted Answer

Set a random seed.. Options B and D are correct. Setting a random seed ensures that random processes (like train/test split) are deterministic. Disabling automatic data splitting and manually providing a validation split gives control over the split. Option A is incorrect because enabling early stopping can vary based on training dynamics. Option C is incorrect because using multiple trials introduces randomness. Option E is incorrect because automatic feature engineering may introduce non-deterministic transforms.

Answer

Use multiple trials.

Answer

Enable early stopping.

Answer

Enable automatic feature engineering.

Question 6

A company is deploying a machine learning model for real-time fraud detection using Amazon SageMaker. The model must have a p99 inference latency under 50ms. Which TWO actions should the ML team take to meet the latency requirement?

Accepted Answer

Use SageMaker Neo to compile and optimize the model for the target instance type.. Using SageMaker Neo (A) optimizes the model for the target hardware, reducing latency. Using GPU instances (B) can accelerate inference for compute-intensive models. SageMaker Batch Transform (C) is for offline inference. Automatic scaling (D) handles throughput, not latency. Multi-model endpoint (E) can help with many models but not single-model latency.

Answer

Use a multi-model endpoint to reduce cold starts.

Answer

Use SageMaker Batch Transform for near-real-time inference.

Answer

Configure automatic scaling to add instances based on CPU utilization.

Question 7

An ML team wants to perform batch inference on a large dataset stored in Amazon S3 using a pre-trained model. The team needs to process the data in parallel across multiple instances to reduce processing time. Which approach should they use?

Accepted Answer

Use SageMaker Batch Transform with multiple instances.. SageMaker Batch Transform is designed for batch inference, automatically distributing the dataset across instances for parallel processing. SageMaker Processing (B) is for data preprocessing, not inference. SageMaker Training (C) is for training models. SageMaker Ground Truth (D) is for labeling.

Answer

Use SageMaker Processing to run a custom inference script.

Answer

Use SageMaker Training to run inference as a training job.

Answer

Use SageMaker Ground Truth to process the data.

Question 8

A data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. After the Autopilot job completes, the best model has an accuracy of 0.85 on the validation set. However, the data scientist notices a class imbalance (90% negative, 10% positive). Which metric should the data scientist use to evaluate the model's performance on the positive class?

Accepted Answer

Area Under the ROC Curve (AUC). Option D is correct because AUC measures the model's ability to distinguish between classes irrespective of threshold, and is suitable for imbalanced datasets. Option A is wrong because accuracy is misleading when classes are imbalanced. Option B is wrong because precision only considers positive predictions. Option C is wrong because recall only considers actual positives.

Answer

Recall

Answer

Accuracy

Answer

Precision

Question 9

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 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 company is using SageMaker Ground Truth to label images for a computer vision model. After launching the labeling job, they notice that the labeling throughput is lower than expected. What should they do to increase throughput?

Accepted Answer

Use a private workforce with more workers.. Option D is correct because using a private workforce with more workers increases parallelism. Option A is incorrect because reducing the number of workers decreases throughput. Option B is incorrect because using a single annotator per task may not speed up labeling. Option C is incorrect because increasing task time per image slows down throughput.

Answer

Change the labeling task to use a single annotator per image.

Answer

Reduce the number of workers assigned to each task.

Answer

Increase the time allowed for each labeling task.

Question 12

A team is using SageMaker to train a model. They want to track hyperparameters, metrics, and model artifacts. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. Option D is correct: SageMaker Experiments tracks parameters, metrics, and artifacts. Option A (Model Registry) manages model versions. Option B (Pipelines) orchestrates workflows. Option C (Debugger) monitors training.

Answer

SageMaker Pipelines

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Question 13

A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The team has multiple models that need to be served, but usage patterns are unpredictable and traffic spikes occur several times a day. The team wants to minimize costs while maintaining low latency. Which THREE actions should the team take?

Accepted Answer

Use SageMaker inference with Spot Instances to reduce cost.. SageMaker multi-model endpoints (A) allow serving multiple models on a single instance, reducing cost. SageMaker automatic scaling (B) adjusts capacity based on demand, handling spikes. Using Spot Instances (C) for inference can reduce cost but may cause interruptions; for real-time, On-Demand is safer. Provisioned concurrency (D) is for Lambda, not SageMaker. Batch Transform (E) is for offline inference.

Answer

Enable provisioned concurrency on the endpoint to reduce cold starts.

Answer

Use SageMaker Batch Transform for all inference requests.

Question 14

A company wants to use Amazon SageMaker to host a model that was trained using a custom algorithm. The model artifact is stored in Amazon S3. The company wants to ensure that the endpoint can automatically scale based on the number of incoming requests. Which configuration should the company use?

Accepted Answer

Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.. SageMaker real-time endpoints support automatic scaling using Application Auto Scaling, which can adjust the number of instances based on metrics like request count. Multi-model endpoints (A) are for serving multiple models. Batch Transform (B) is for offline inference. Serverless Inference (D) scales automatically but has limitations; the question asks for endpoint scaling, and real-time endpoints with auto-scaling is the standard approach.

Answer

Create a SageMaker multi-model endpoint with automatic scaling.

Answer

Use SageMaker Serverless Inference which scales automatically.

Answer

Use SageMaker Batch Transform with a scheduled job.

Question 15

A machine learning team is using SageMaker Processing jobs to run feature engineering on large datasets. The job takes a long time to complete. Which change would most likely reduce the processing time?

Accepted Answer

Increase the number of instances in the processing cluster. Option B is correct: Increasing the instance count allows distributed processing, reducing time. Option A (larger instance) helps but is less scalable. Option C (local mode) is for testing, not production. Option D (changing framework) may not improve performance.

Answer

Switch to local mode to avoid network overhead

Answer

Change the processing script from Python to PySpark

Answer

Use a larger instance type, e.g., from r5.xlarge to r5.24xlarge

MLS-C01 Practice Test 28 — 15 Questions

Exhibit

MLS-C01 Practice Test 28 — 15 Questions

Exhibit