MLS-C01 Machine Learning Implementation and Operations Practice Test 20 — 15 Questions

Question 1

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.. Option C is correct because the error indicates the training data is missing at the expected path, which typically occurs when the S3 data source configuration is incorrect or the data is not uploaded. Option A is wrong because the error is about missing data, not the container entry point. Option B is wrong because the error is not related to training script syntax. Option D is wrong because the error does not mention model artifacts.

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 2

A company uses SageMaker to host a real-time inference endpoint. The endpoint is receiving a large number of requests, but the latency is higher than expected. The data scientist observes that the CPU utilization is low but memory utilization is high. Which action should be taken to reduce latency?

Accepted Answer

Switch to an instance type with more memory or optimize the model to reduce memory footprint.. Option A is correct because high memory utilization suggests the model is memory-bound; increasing instance memory or using a model with lower memory footprint can reduce latency. Option B is wrong because CPU utilization is low, so more CPU cores won't help. Option C is wrong because increasing instance count can help throughput but not necessarily latency per request; also it may increase cost. Option D is wrong because the issue is memory, not network.

Answer

Enable VPC traffic mirroring to diagnose network issues.

Answer

Use an instance type with more vCPUs.

Answer

Increase the number of instances in the endpoint.

Question 3

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.. Option D is correct because scaling policies require a cooldown period (default 300 seconds) to prevent rapid scaling. Without it, the policy may not activate. Option A is wrong because the metric is valid. Option B is wrong because the metric is emitted by default. Option C is wrong because scaling policy can be defined without a scheduled action.

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 4

A data scientist is using SageMaker Ground Truth to create a labeled dataset for object detection. After the labeling job completes, the scientist notices that the output manifest file contains incorrect labels. What is the most efficient way to correct these labels?

Accepted Answer

Create an incremental labeling job that includes only the mislabeled items.. Option B is correct because Ground Truth allows creating an incremental labeling job to correct only the mislabeled items, avoiding re-labeling all data. Option A is wrong because it would require re-labeling everything, which is inefficient. Option C is wrong because it does not address labeling errors. Option D is wrong because it would lose the correctly labeled data.

Answer

Delete the labeling job and start over with a different set of workers.

Answer

Use the SageMaker console to edit the incorrect labels directly in the manifest file.

Answer

Create a new labeling job with the same dataset and manually verify all labels.

Question 5

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.. Option A is correct because SageMaker's managed training can automatically distribute the data across multiple instances for parallel training, reducing time. Option B is wrong because increasing instance size might help but may not be as cost-effective as distributed training. Option C is wrong because it would increase time. Option D is wrong because it would lose optimization benefits.

Answer

Disable automatic model tuning.

Answer

Use a larger instance type with more vCPUs.

Answer

Reduce the number of epochs.

Question 6

A company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?

Accepted Answer

Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.. A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.

Answer

Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.

Answer

Manually adjust the instance count based on monitoring dashboards.

Answer

Use a scheduled scaling action to add instances during peak hours.

Question 7

A data scientist is using SageMaker to train a model using the built-in XGBoost algorithm. The training job fails with the error 'AlgorithmError: Framework error: No module named 'xgboost''. What is the most likely cause?

Accepted Answer

The training job is using a custom container that does not have XGBoost installed.. Option B is correct because the built-in XGBoost algorithm requires the 'xgboost' Python package; SageMaker's built-in algorithms provide the necessary environment, but if the container is overridden or the wrong image is used, the module may be missing. Option A is wrong because the error is about missing module, not data format. Option C is wrong because the error is not about permissions. Option D is wrong because the error is not about output path.

Answer

The training data is not in CSV format.

Answer

The IAM role does not have permission to access SageMaker.

Answer

The S3 output path is incorrect.

Question 8

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.. Option D is correct because SageMaker provides a built-in load testing tool that simulates traffic to test endpoint performance. Option A is wrong because waiting for production traffic does not allow pre-production validation. Option B is wrong because CloudWatch does not generate traffic. Option C is wrong because offline evaluation does not test real-time inference.

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 9

A data scientist is using SageMaker to train a model and wants to track experiments, including hyperparameters and metrics. Which TWO actions should the scientist take to set up experiment tracking? (Choose TWO.)

Accepted Answer

Use the SageMaker Experiments Python SDK to create an experiment and log runs.. Option A is correct because the SageMaker Experiments Python SDK provides the primary interface for creating and managing experiments, allowing the data scientist to log runs, hyperparameters, and metrics in a structured way. This SDK directly integrates with SageMaker training jobs and notebook executions to capture experiment metadata.

Answer

Enable SageMaker Model Monitor to track training metrics.

Answer

Configure CloudWatch Logs to store experiment data.

Answer

Enable SageMaker Studio to automatically capture experiments.

Question 10

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 across multiple Availability Zones provides zone redundancy. Option C is correct because using multiple instances in the endpoint configuration ensures that if one instance fails, others handle traffic. Option D is correct because enabling auto-scaling can replace failed instances. Option B is wrong because a single instance in one AZ is not resilient. Option E is wrong because a single AZ does not protect against AZ failure.

Answer

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

Answer

Use a single Availability Zone for simplicity.

Question 11

A data scientist is training a model using SageMaker and wants to use spot instances to reduce costs. Which THREE considerations should the scientist evaluate? (Choose THREE.)

Accepted Answer

The training job must support checkpointing to save progress.. Option A is correct because spot instances can be interrupted, so the training job must be checkpointed to resume. Option C is correct because spot instances are typically cheaper, but they can be reclaimed, affecting cost savings if interruptions are frequent. Option D is correct because model training is often fault-tolerant and can handle interruptions. Option B is wrong because spot instances are dynamically priced, not fixed. Option E is wrong because spot instances are available for training, not just inference.

Answer

Spot instances have a fixed, lower price than on-demand.

Answer

Spot instances are only available for inference, not training.

Question 12

A data scientist wants to deploy a PyTorch model for real-time inference with latency under 100 ms. Which AWS service is most suitable?

Accepted Answer

Amazon SageMaker real-time endpoint. Option B is correct because Amazon SageMaker real-time endpoints provide low-latency inference. Option A (SageMaker Batch Transform) is for batch predictions, not real-time. Option C (Lambda) has limited runtime and scalability for large models. Option D (SageMaker Processing) is for data processing, not inference.

Answer

Amazon SageMaker Processing

Answer

AWS Lambda with container image

Answer

Amazon SageMaker Batch Transform

Question 13

A team is training an XGBoost model using SageMaker with a large dataset in S3 (100 GB). Training is taking too long. Which change will most likely reduce training time without sacrificing accuracy?

Accepted Answer

Configure Pipe mode for data input. Option D is correct: enabling SageMaker Managed Spot Training reduces cost but does not accelerate training. Option A (increasing instance count) can reduce training time via distributed training. Option B (using Pipe mode) streams data for faster I/O. Option C (reducing instance count) would increase time. Option D is about cost, not speed.

Answer

Reduce the number of training instances

Answer

Enable SageMaker Managed Spot Training

Answer

Use a larger instance type with more vCPUs

Question 14

A company deploys a SageMaker model for inference. After a few days, response times increase significantly. CloudWatch metrics show high CPU utilization and memory usage. The model is a large ensemble. What is the most cost-effective solution?

Accepted Answer

Configure SageMaker automatic scaling based on CPU utilization. Option C is correct: automatic scaling adds instances based on demand, handling spikes cost-effectively. Option A (ad hoc monitoring) does not automatically adjust. Option B (migrate to Lambda) may not support large models. Option D (increase instance size) is less cost-effective than scaling out.

Answer

Use CloudWatch alarms to notify the team, who manually launch additional endpoints

Answer

Migrate the model to AWS Lambda with provisioned concurrency

Answer

Replace the current instance type with a larger one

Question 15

A data scientist needs to run a one-time SQL query on a large dataset in S3 to create a training dataset. The query involves aggregations and joins. Which service is most suitable?

Accepted Answer

Amazon Athena. Option C (Amazon Athena) is correct for serverless SQL queries on S3. Option A (Amazon EMR) is overkill for a one-time query. Option B (AWS Glue) is for ETL jobs, not ad-hoc queries. Option D (Amazon RDS) requires moving data.

Answer

AWS Glue ETL job

Answer

Amazon EMR with Spark SQL

Answer

Amazon RDS with data loaded into it