MLS-C01 Machine Learning Implementation and Operations Practice Test 7 — 15 Questions

Question 1

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?

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

Accepted Answer

Use a SageMaker multi-model endpoint.. A SageMaker multi-model endpoint allows you to host multiple models on a single endpoint behind the same serving container, sharing resources and reducing costs while still providing low-latency real-time inference. This is ideal for a large deep learning model that needs low latency but must be cost-effective, as it avoids the expense of dedicated instances for each model.

Answer

Use a SageMaker batch transform job.

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Use a SageMaker Serverless Inference endpoint.

Answer

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

Question 2

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.. When a SageMaker endpoint returns 500 errors, it typically indicates an application-level failure in the inference container, such as an unhandled exception in the custom inference script. CloudWatch Logs capture the stdout and stderr of the container, including Python tracebacks, which directly reveal the root cause. Option D is correct because viewing these logs is the first and most effective step to diagnose the specific error.

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 3

A data scientist is using AWS Glue to prepare training data. The job reads from an S3 bucket, performs transformations, and writes to another S3 bucket. The job is failing due to insufficient memory. Which solution should the data scientist use to fix this?

Accepted Answer

Increase the number of DPU (Data Processing Units) for the job.. The job is failing due to insufficient memory, which is a resource constraint. Increasing the number of DPU (Data Processing Units) allocates more memory and compute capacity to the AWS Glue job, directly addressing the out-of-memory error. This is the standard approach to scale Glue ETL jobs when they hit memory limits.

Answer

Use AWS Glue's job bookmark feature.

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Use Amazon Athena instead of AWS Glue.

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Use a columnar file format like Parquet.

Question 4

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.. Setting the scale_pos_weight hyperparameter in XGBoost adjusts the weight of the positive (minority) class during training, effectively penalizing misclassifications of fraud cases more heavily. This directly addresses the class imbalance by forcing the model to focus on the minority class, which improves recall without altering the dataset distribution. The current 99% accuracy with only 10% fraud recall indicates the model is biased toward the majority class, and scale_pos_weight is the most direct and efficient fix within XGBoost.

Answer

Use random under-sampling of the majority class.

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Use mean squared error as the objective function.

Answer

Use SMOTE to oversample the minority class.

Question 5

A company uses Amazon SageMaker to train a model. The training job uses a custom Docker container. The job fails with the error 'CannotStartContainerError: API error (500).' Which of the following is the most likely cause?

Accepted Answer

The Docker image is built for a different CPU architecture.. The error 'CannotStartContainerError: API error (500)' occurs when the Docker daemon on the SageMaker training instance fails to start the container. The most common cause is a CPU architecture mismatch: if the Docker image is built for a different architecture (e.g., ARM64) than the SageMaker training instance (which uses x86_64), the container cannot execute. SageMaker training instances are x86_64-based, so an image built for ARM64 will trigger this error at container launch time.

Answer

The training script has a syntax error.

Answer

The S3 input data is missing.

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The output path is not writable.

Question 6

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. Amazon S3 with versioning enabled is the correct choice because it provides a simple, scalable, and cost-effective way to version control datasets. S3 versioning preserves every object version, allowing you to retrieve, restore, or compare previous dataset states, which is essential for reproducibility in ML experiments. This directly meets the requirement for dataset version control without additional overhead.

Answer

AWS Lake Formation

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Amazon SageMaker Feature Store

Answer

Amazon SageMaker Model Registry

Question 7

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.. Pipe mode streams data directly from S3 into the training algorithm without first downloading it to the local storage, eliminating the bottleneck of disk I/O and reducing data loading time. This is especially effective for large datasets where the time to copy data to EBS (File mode) dominates the training job duration.

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 8

A machine learning engineer is deploying a TensorFlow model to an Amazon SageMaker endpoint. The endpoint is behind an Application Load Balancer (ALB) for A/B testing. The engineer notices that the new variant is not receiving any traffic. What is the most likely cause?

Accepted Answer

The ALB target group weight for the new variant is set to 0.. If the ALB target group weight for the new variant is set to 0, the ALB will not route any traffic to that target group, even if the endpoint is healthy. In A/B testing setups, weights control the proportion of traffic sent to each variant; a weight of 0 effectively disables the variant.

Answer

The new variant's health checks are failing.

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The model is not compatible with the ALB's protocol.

Answer

The ALB is not configured to route to SageMaker endpoints.

Question 9

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.. A is correct because the SageMaker execution role must have the s3:GetObject permission to read objects from the S3 bucket. Without this IAM permission, SageMaker cannot retrieve the training data, even if the bucket is otherwise accessible.

Answer

Enable S3 Transfer Acceleration.

Answer

Set up a VPC endpoint for S3.

Answer

Add a bucket policy allowing SageMaker access.

Question 10

A company is deploying a machine learning model using Amazon SageMaker. The model needs to be updated frequently. Which THREE practices should the company implement for model versioning and deployment?

Accepted Answer

Use AWS CodePipeline to automate the training and deployment pipeline.. AWS CodePipeline is correct because it enables continuous integration and continuous delivery (CI/CD) for machine learning models, automating the build, train, test, and deploy stages. By integrating with SageMaker, CodePipeline can trigger retraining on new data, run evaluation steps, and automatically update the endpoint, ensuring frequent model updates are reliable and repeatable.

Answer

Manually update the endpoint configuration each time.

Answer

Store all training datasets in a single S3 bucket without versioning.

Question 11

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.. The 'ResourceLimitExceeded' error indicates that the AWS account has reached its service quota for SageMaker training instances. Requesting a service quota increase through the AWS Service Quotas console or API allows the account to launch additional instances of the required type, directly resolving the limit issue.

Answer

Use Amazon EFS for training data.

Answer

Reduce the size of the training dataset.

Answer

Use a smaller instance type.

Question 12

A data scientist is trying to create a SageMaker training job using an execution role with the attached IAM policy. The training job fails with an access denied error when trying to read training data from the S3 bucket 'my-bucket'. What is the most likely cause?

Accepted Answer

The S3 bucket policy explicitly denies access to the role.. The most likely cause is that the S3 bucket policy explicitly denies access to the SageMaker execution role. Even if the IAM policy grants the necessary permissions, an explicit deny in the bucket policy overrides any allow, resulting in an access denied error when the training job attempts to read training data from the S3 bucket.

Answer

The IAM policy does not include s3:ListBucket permission.

Answer

The S3 bucket is in a different AWS account.

Answer

The sagemaker:CreateTrainingJob action is not allowed.

Question 13

A data scientist submits a SageMaker training job with the provided configuration. The job fails immediately with the error 'Algorithm not found: 382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.2-1'. What is the most likely cause?

Accepted Answer

The ECR repository URI is incorrect or the image does not exist.. The error 'Algorithm not found' indicates that the ECR repository URI specified in the training job configuration does not point to an existing image. The URI '382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.2-1' is the standard SageMaker built-in XGBoost image for the us-west-2 region, but the version '1.2-1' may not exist or the URI is malformed. SageMaker training jobs require a valid ECR image URI to launch the container; if the image is not found in the registry, the job fails immediately with this error.

Answer

The training region is different from the image region.

Answer

The input data format is incorrect.

Answer

The IAM role does not have permission to pull the image.

Question 14

A data scientist is using Amazon SageMaker to train a custom image classification model using a PyTorch script. The training job runs successfully but the model accuracy is lower than expected. The scientist wants to debug the training process by inspecting gradients and layer outputs. Which SageMaker feature should be used to capture this internal state during training?

Accepted Answer

Use SageMaker Debugger to capture tensors and gradients.. SageMaker Debugger is specifically designed to capture internal model state such as tensors, gradients, and weights during training. It allows you to set rules to monitor for issues like vanishing gradients or overfitting, and to save these tensors for later analysis. This directly addresses the need to inspect gradients and layer outputs to diagnose low accuracy.

Answer

Use SageMaker Experiments to track hyperparameters and metrics.

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Use SageMaker Profiler to profile system bottlenecks.

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Use SageMaker Model Monitor to detect data drift.

Question 15

A company deploys a real-time inference endpoint using Amazon SageMaker with an ML model that has strict latency requirements. The endpoint currently uses a single ml.c5.xlarge instance. During a load test, the p99 latency exceeds the 100ms threshold. The team adds more instances but latency does not improve because the model is heavily CPU-bound. What is the MOST cost-effective change to meet the latency requirement?

Accepted Answer

Change the instance type to a GPU instance such as ml.g4dn.xlarge.. The model is CPU-bound, meaning the bottleneck is compute capacity, not throughput. GPU instances like ml.g4dn.xlarge offload parallel computation from the CPU, significantly reducing per-inference latency. This directly addresses the root cause without over-provisioning, making it the most cost-effective solution.

Answer

Use a multi-model endpoint to serve multiple models on the same instance.

Answer

Enable automatic scaling based on inference latency.

Answer

Increase the number of instances and use a target tracking scaling policy.