MLS-C01 Practice Test 26 — 15 Questions

Question 1

A company uses Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error: "Unable to write to /opt/ml/output/data". The data scientist checks the container and finds that the /opt/ml directory is not writable. What is the MOST likely cause?

Accepted Answer

The container runs as a non-root user that lacks write permissions to /opt/ml.. SageMaker expects the training container to write output to /opt/ml/model and /opt/ml/output. If the container does not have write permissions to these directories, the training fails. The most likely cause is that the Docker image was built with a non-root user that does not have write permissions to /opt/ml. The correct fix is to ensure the container runs as root or grants write permissions.

Answer

The Docker image is built from a base image that does not have the required libraries.

Answer

The SageMaker training job is configured with insufficient memory.

Answer

The training script is not copying the model to /opt/ml/model.

Question 2

A company wants to monitor a deployed model for data drift. Which AWS service should they use?

Accepted Answer

Amazon SageMaker Model Monitor. Option B is correct because SageMaker Model Monitor is designed for drift detection. Option A is wrong because CloudWatch is for metrics, not drift. Option C is wrong because SageMaker Clarify is for bias detection. Option D is wrong because SageMaker Ground Truth is for labeling.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon CloudWatch Logs

Answer

Amazon SageMaker Clarify

Question 3

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. Which action will the notebook be able to perform?

Accepted Answer

Invoke a SageMaker endpoint. Option C is correct because the policy allows sagemaker:InvokeEndpoint. Option A is wrong because sagemaker:CreateTrainingJob is not allowed. Option B is wrong because s3:GetObject is not allowed. Option D is wrong because sagemaker:CreateModel is not allowed.

Answer

Create a training job

Answer

Create a model

Answer

Read data from S3

Question 4

A company is using Amazon SageMaker to train a model and wants to track hyperparameter tuning jobs. Which AWS service is BEST suited to store and query metadata such as tuning job configurations and results?

Accepted Answer

Amazon SageMaker Experiments. Amazon SageMaker Experiment Management is the native service to track experiments, trials, and components. SageMaker automatically logs hyperparameters and metrics. Amazon DynamoDB could be used but requires custom integration. CloudWatch Logs stores logs, not structured metadata. Athena queries data in S3 but is not optimized for real-time tracking. S3 is storage, not a queryable metadata store.

Answer

Amazon CloudWatch Logs

Answer

Amazon S3 with Amazon Athena

Answer

Amazon DynamoDB

Question 5

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint. The model is a PyTorch model that requires a custom inference script. The engineer notices that the endpoint is returning 500 errors after deployment. Which step should the engineer take to debug the issue?

Accepted Answer

View the CloudWatch Logs for the endpoint.. Option D is correct because CloudWatch Logs contain detailed error messages. Option A is wrong because the endpoint is already deployed. Option B is wrong because it would overwrite the script. Option C is wrong because CloudWatch metrics show metrics, not errors.

Answer

Redeploy the endpoint with a different instance type.

Answer

Check the CloudWatch metrics for the endpoint.

Answer

Modify the inference script and update the endpoint.

Question 6

A machine learning team is deploying a model using Amazon SageMaker. The model inference code runs on GPUs and requires a custom container. The team wants to minimize cold start latency. Which SageMaker hosting option should they use?

Accepted Answer

Use a real-time endpoint with a single production variant using a GPU instance.. Multi-model endpoints are designed to host multiple models on the same endpoint and can reduce cold starts when models are loaded on demand, but for a single model with GPU requirement, multi-model endpoints do not support GPU. Real-time endpoints with a single variant and GPU instance are the standard choice for low-latency inference. Serverless inference does not support GPU. Multi-variant endpoints are for A/B testing. Batch transform is for offline inference.

Answer

Use a multi-model endpoint with GPU instances.

Answer

Use a serverless inference endpoint.

Answer

Use a real-time endpoint with multiple production variants for redundancy.

Question 7

A company is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires low latency. The team is concerned about cost. Which SageMaker hosting option should the team use?

Accepted Answer

Use a SageMaker multi-model endpoint.. Option C is correct because multi-model endpoints share resources and reduce cost. Option A is wrong because it's for testing. Option B is wrong because serverless can have cold starts. Option D is wrong because batch transform is not real-time.

Answer

Use a SageMaker batch transform job.

Answer

Use a SageMaker Serverless Inference endpoint.

Answer

Use a single-instance endpoint with a large instance type.

Question 8

A data scientist needs to version control datasets used for machine learning experiments. Which AWS service should the data scientist use?

Accepted Answer

Amazon S3 with versioning enabled. Option C is correct because AWS Lake Formation does not version datasets. Option D is correct because Amazon S3 can be used with versioning enabled. Option A is wrong because it's for feature stores. Option B is wrong because it's for model registry.

Answer

AWS Lake Formation

Answer

Amazon SageMaker Feature Store

Answer

Amazon SageMaker Model Registry

Question 9

A company is using Amazon SageMaker to build a custom model. The training job is failing with a 'ResourceLimitExceeded' error. Which TWO actions should the company take to resolve this issue?

Accepted Answer

Request a service quota increase for SageMaker training instances.. Option A is correct because it increases the limit. Option D is correct because spot instances can help. Option B is wrong because it doesn't address limit. Option C is wrong because it's for storage. Option E is wrong because it doesn't help with compute limits.

Answer

Use Amazon EFS for training data.

Answer

Reduce the size of the training dataset.

Answer

Use a smaller instance type.

Question 10

A data scientist is using SageMaker Autopilot to automatically build a classification model. The dataset is highly imbalanced (1% positive class). Which configuration should the scientist set to handle the class imbalance?

Accepted Answer

Use the 'AutoMLJobObjective' with 'F1' metric.. SageMaker Autopilot does not directly handle class imbalance automatically. The user can specify a 'problem_type' and 'target_attribute_name', but to address imbalance, they should enable oversampling or use custom recipes. However, among the options, setting the objective metric to 'F1' or 'AUC' is a common technique, but Autopilot allows setting 'balance_class_weights' to 'True' or using 'AutoMLJobObjective' with 'F1'. The correct answer is to use the 'AutoMLJobObjective' with 'F1' metric, as Autopilot will focus on optimizing F1, which is sensitive to imbalance.

Answer

Set the problem_type to 'BinaryClassification' and enable 'balance_class_weights'.

Answer

Set the 'sample_weight' attribute in the input data.

Answer

Manually downsample the majority class before training.

Question 11

Which TWO factors should be considered when choosing between Amazon SageMaker's real-time endpoints and serverless inference? (Select TWO.)

Accepted Answer

GPU requirement. Serverless inference is ideal for intermittent workloads with no cold start tolerance. Real-time endpoints are better for predictable, low-latency, and high-throughput requirements. GPU support is only available on real-time endpoints. Memory limits apply to both but serverless has a maximum of 6 GB. Concurrent requests can be handled by both, but serverless scales to zero.

Answer

Integration with AWS Lambda

Answer

Availability of built-in algorithms

Answer

Model size in GB

Question 12

A company is using Amazon SageMaker to train a model on a large dataset stored in S3. The training job is taking a long time due to slow data loading. Which action can the data scientist take to reduce data loading time?

Accepted Answer

Use Pipe mode to stream data from S3.. Option D is correct because Pipe mode streams data directly. Option A is wrong because it doesn't change data transfer. Option B is wrong because it may be slower. Option C is wrong because it doesn't help.

Answer

Use File mode and copy data to Amazon EBS.

Answer

Use a larger instance type with more memory.

Answer

Enable data augmentation during training.

Question 13

Which TWO actions are best practices for securing a SageMaker notebook instance? (Select TWO.)

Accepted Answer

Disable direct internet access for the notebook instance.. Best practices include using VPC to isolate the notebook, enabling encryption at rest, using IAM roles with least privilege, and disabling direct internet access. Direct internet access should be disabled to prevent data exfiltration. Root access should be disabled for notebook instances.

Answer

Enable root access for users to install packages.

Answer

Store data in the notebook's local storage for performance.

Answer

Use a shared IAM user for all data scientists.

Question 14

A company is building a fraud detection model. The dataset is highly imbalanced (99% legitimate, 1% fraud). The data scientist trains a model using Amazon SageMaker's built-in XGBoost algorithm. The model achieves 99% accuracy but only catches 10% of fraud cases. Which technique should the data scientist apply to improve recall for the minority class?

Accepted Answer

Set the scale_pos_weight hyperparameter in XGBoost.. Option B is correct because setting scale_pos_weight balances class weights. Option A is wrong because SMOTE creates synthetic samples, but XGBoost has built-in handling. Option C is wrong because under-sampling loses data. Option D is wrong because it's for regression.

Answer

Use random under-sampling of the majority class.

Answer

Use mean squared error as the objective function.

Answer

Use SMOTE to oversample the minority class.

Question 15

A data scientist is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. Which TWO actions are necessary to allow SageMaker to access the data?

Accepted Answer

Ensure the SageMaker execution role has s3:GetObject permission.. Option A is correct because SageMaker needs permission to decrypt. Option C is correct because the execution role needs permission. Option B is wrong because bucket policy is separate. Option D is wrong because it's not required. Option E is wrong because VPC endpoint is not required.

Answer

Enable S3 Transfer Acceleration.

Answer

Set up a VPC endpoint for S3.

Answer

Add a bucket policy allowing SageMaker access.