MLS-C01 Machine Learning Implementation and Operations Practice Test 4 — 15 Questions

Question 1

Which TWO configuration steps are necessary to deploy a custom Docker container for training in Amazon SageMaker? (Choose two.)

Accepted Answer

Implement the train function in the container that saves model artifacts to /opt/ml/model. Amazon SageMaker expects the training container to save model artifacts to the `/opt/ml/model` directory, which SageMaker automatically copies to Amazon S3 after training completes. This is a required contract for any custom training container used with SageMaker.

Answer

Expose a REST API endpoint for inference

Answer

Define a Docker Compose file to manage multi-container training

Answer

Push the container image to Docker Hub

Question 2

Which THREE actions can help reduce the inference latency of a SageMaker endpoint? (Choose three.)

Accepted Answer

Use a larger instance type with more CPU/GPU. Using a larger instance type with more CPU or GPU resources directly increases the computational capacity available for inference. This reduces the time required to process each prediction request, thereby lowering inference latency. SageMaker endpoints scale horizontally and vertically, and vertical scaling (larger instances) is a straightforward way to improve per-request performance.

Answer

Use a multi-model endpoint to share instances across models

Answer

Increase the number of instances in the endpoint

Question 3

Which TWO services can be used to perform hyperparameter tuning in Amazon SageMaker? (Choose two.)

Accepted Answer

Amazon SageMaker Automatic Model Tuning. Amazon SageMaker Automatic Model Tuning (option A) is the native hyperparameter tuning service in SageMaker, which automatically searches for the best hyperparameter values by launching training jobs with different combinations and evaluating them against a specified objective metric. Amazon SageMaker Experiments (option B) is used to organize, track, and compare machine learning experiments, including hyperparameter tuning runs, by capturing parameters, metrics, and artifacts for reproducibility and analysis.

Answer

AWS Glue

Answer

Amazon SageMaker Ground Truth

Answer

Amazon EMR

Question 4

An IAM policy is attached to a SageMaker notebook instance. The data scientist wants to use the notebook to train a model using data from S3 bucket 'my-bucket'. However, the training job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The notebook instance role does not have iam:PassRole permission to pass the SageMaker execution role. The most likely cause is that the notebook instance role lacks the iam:PassRole permission, which is required to pass the SageMaker execution role to the training job. When a SageMaker notebook instance creates a training job, it must pass an execution role that the training job will assume to access resources like S3. Without iam:PassRole on the notebook's role, the API call fails with an access denied error, even if all other permissions are correctly configured.

Answer

The sagemaker:CreateTrainingJob permission is not allowed on the specific resource

Answer

The S3 bucket resource ARN is incorrectly formatted

Answer

The s3:GetObject permission is missing for the bucket

Question 5

A SageMaker endpoint creation fails with the above CloudWatch Logs excerpt. What is the MOST likely cause?

Accepted Answer

The model file is too large and takes longer than 300 seconds to load. The CloudWatch Logs excerpt shows a timeout error during model loading. SageMaker has a default 300-second timeout for downloading and loading model artifacts from S3 into the inference container. If the model file is too large or the network is slow, the container fails to start within this window, causing the endpoint creation to fail with a timeout error.

Answer

The S3 bucket containing the model artifacts has incorrect permissions

Answer

The inference script has a syntax error

Answer

The instance type does not have enough memory to load the model

Question 6

A data scientist runs the AWS CLI command shown in the exhibit. The output shows that job-2 failed. Which action should the data scientist take to diagnose the failure?

Accepted Answer

Run `aws sagemaker describe-training-job --training-job-name job-2` to see the failure reason. The `describe-training-job` API call returns a `FailureReason` field that provides the specific error message for a failed SageMaker training job. This is the most direct and efficient way to diagnose why job-2 failed, as it retrieves the exact failure reason from the SageMaker service without requiring additional log parsing or bucket inspection.

Answer

Check the CloudWatch Logs log group for job-2

Answer

Check the S3 bucket for any error logs uploaded by the training job

Answer

Run `aws sagemaker list-training-jobs --name-contains job-2` to get more details

Question 7

A data science team is deploying a machine learning model to production using Amazon SageMaker. The model requires real-time inference with low latency. Which SageMaker feature should they use to deploy the model?

Accepted Answer

SageMaker Realtime Endpoint. SageMaker Realtime Endpoints are designed for low-latency, synchronous inference, making them the correct choice for serving predictions in real time. They keep the model loaded and ready to respond to individual requests, which is essential for applications requiring immediate responses.

Answer

SageMaker Notebook Instance

Answer

SageMaker Batch Transform

Answer

SageMaker Autopilot

Question 8

During training of a deep learning model on a GPU instance in SageMaker, the training job fails with an insufficient memory error. Which step should be taken first to resolve this issue?

Accepted Answer

Reduce the batch size. The most direct cause of an out-of-memory (OOM) error during GPU training is that the combined size of the model parameters, activations, and gradients exceeds the GPU's VRAM. Reducing the batch size immediately decreases the memory footprint of activations stored for backpropagation, which is the largest and most tunable memory consumer. This is the first and simplest step to resolve the error without altering the model architecture or training dynamics.

Answer

Add dropout layers

Answer

Use a smaller learning rate

Answer

Use gradient clipping

Question 9

A company uses SageMaker to train a model each night. The training data is stored in an S3 bucket with SSE-S3 encryption. The training job fails with an access denied error. Which configuration is needed?

Accepted Answer

Create an IAM role with S3 read access and assign it to the SageMaker training job. The training job fails because SageMaker does not have the necessary permissions to read the data from S3. The solution is to create an IAM role with S3 read access (s3:GetObject) and assign it to the SageMaker training job. Option A is incorrect because VPC endpoints are not required for this error and are unrelated to encryption. Option C is incorrect because SSE-S3 encryption is already enabled and does not require KMS. Option D is incorrect because a bucket policy allowing all principals is insecure and not the proper way to grant permissions to SageMaker.

Answer

Configure the training job to run in a VPC with S3 VPC Endpoint

Answer

Enable SSE-KMS on the S3 bucket

Answer

Add a bucket policy allowing s3:GetObject for all principals

Question 10

A data scientist is deploying a model to a SageMaker endpoint and needs to optimize for cost while maintaining low latency. Which TWO actions should the data scientist take?

Accepted Answer

Use SageMaker Serverless Inference. SageMaker Serverless Inference (Option D) automatically scales compute resources based on request volume, charging only for the compute time used during inference. This eliminates the cost of idle provisioned instances, making it ideal for optimizing cost while maintaining low latency for variable or intermittent traffic patterns.

Answer

Use a larger instance type

Answer

Deploy to a single instance

Answer

Switch to batch transform

Question 11

You are deploying a custom Docker container for a SageMaker model that requires a specific NVIDIA CUDA version. Which THREE steps must you take to ensure the container runs correctly on SageMaker?

Accepted Answer

Include the SageMaker inference toolkit in the container. The SageMaker inference toolkit provides the necessary SageMaker-compatible HTTP server and lifecycle management (e.g., model loading, serving, and health checks) that SageMaker expects from a custom container. Without it, the container would not properly integrate with SageMaker's invocation and scaling mechanisms, even if the CUDA dependencies are correct.

Answer

Define a health check endpoint

Answer

Use SageMaker Batch Transform

Question 12

A machine learning pipeline uses SageMaker Processing jobs for feature engineering. Which TWO are benefits of using SageMaker Processing over running a custom script on an EC2 instance?

Accepted Answer

Automatically manages the compute resources. SageMaker Processing automatically manages the underlying compute resources, including provisioning, scaling, and terminating instances. This eliminates the need for manual infrastructure management, which is required when running a custom script on an EC2 instance. Option B is correct because SageMaker Processing jobs natively integrate with SageMaker Experiments, allowing automatic tracking of parameters, metrics, and artifacts for reproducibility and comparison.

Answer

Provides a built-in VPC for network isolation

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Allows use of custom Docker images from any registry

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Supports multiple programming languages

Question 13

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The training job fails because the input data is not accessible. Which step should the data scientist take to fix the issue?

Accepted Answer

Attach an IAM policy to SageMakerRole that grants s3:GetObject on the bucket. The training job fails because the SageMaker execution role (SageMakerRole) lacks the necessary IAM permissions to read the input data from the S3 bucket. By attaching an IAM policy that grants s3:GetObject on the specific bucket, the role gains the required access. SageMaker uses the execution role to access S3 data, so the role's permissions must explicitly allow the GetObject action.

Answer

Add a VpcConfig to the training job

Answer

Modify the bucket policy to allow s3:GetObject for any principal

Answer

Increase VolumeSizeInGB to 50

Question 14

Refer to the exhibit. A SageMaker training job uses an IAM role with this policy. The training job writes output to s3://my-bucket/output/. Which statement about the policy is true?

Accepted Answer

The training job can write output objects only if server-side encryption with SSE-S3 is used. The policy includes a Deny statement that explicitly denies PutObject requests unless the request includes the `x-amz-server-side-encryption` header set to `AES256`, which corresponds to SSE-S3. The Allow statement grants PutObject permission, but the Deny statement overrides it for any request that does not meet the encryption condition. Therefore, the training job can only write output objects if server-side encryption with SSE-S3 is used.

Answer

The Allow statement allows all PutObject requests regardless of encryption

Answer

The Deny statement blocks all PutObject requests

Answer

The GetObject permission requires the object to be encrypted with SSE-S3

Question 15

Refer to the exhibit. A SageMaker endpoint is returning 5xx errors. The logs show the above error. Which change will most likely resolve the issue?

Accepted Answer

Use a larger instance type with more memory. 5xx errors from a SageMaker endpoint typically indicate that the inference container is running out of memory (OOM) or crashing under load. The error log suggests the model or inference process requires more memory than the current instance type provides. Upgrading to a larger instance type with more memory directly addresses the resource exhaustion, allowing the model to load and inference to complete without failure.

Answer

Reduce the batch size in the inference script

Answer

Enable Auto Scaling on the endpoint

Answer

Compress the model artifact