MLS-C01 Machine Learning Implementation and Operations Practice Test 23 — 15 Questions

Question 1

A data science team is using Amazon SageMaker to train a model. The training job is failing with an 'OutOfMemory' error. The team is using a p3.2xlarge instance with 61 GB of memory. They need to resolve this issue as quickly as possible. Which action should they take?

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

Accepted Answer

Use a larger instance type, such as p3.8xlarge. The 'OutOfMemory' error indicates the training job requires more memory than the 61 GB available on the p3.2xlarge instance. The fastest resolution is to scale vertically by using a larger instance type, such as the p3.8xlarge, which provides 244 GB of memory. This directly addresses the memory shortage without requiring code changes or architectural modifications, minimizing downtime.

Answer

Reduce the batch size in the training script

Answer

Use a spot instance to save costs

Answer

Enable distributed training across multiple instances

Question 2

A company uses Amazon SageMaker to deploy a model for real-time predictions. The model is updated weekly. The company wants to ensure that the new model version is gradually rolled out to a small percentage of traffic before full deployment, and that it can be rolled back quickly if issues are detected. Which deployment strategy should be used?

Accepted Answer

Canary deployment using SageMaker endpoint variants. Amazon SageMaker endpoint variants support canary deployments, where you can shift a small percentage of traffic to a new model version (e.g., 5%) while the majority remains on the old version. This allows gradual rollout and immediate rollback by simply adjusting the traffic distribution weights or deleting the new variant, meeting the requirement for quick rollback without redeploying.

Answer

Blue/green deployment

Answer

A/B testing with a holdout group

Answer

Rolling deployment across multiple endpoints

Question 3

A machine learning engineer is building a pipeline to preprocess data and train a model using Amazon SageMaker. The data is stored in Amazon S3 and the preprocessing step is computationally intensive. The engineer wants to minimize costs while ensuring that the preprocessing step does not fail due to instance termination. Which instance type should be used for the preprocessing step?

Accepted Answer

On-demand instances. On-demand instances (Option B) are the correct choice because they provide reliable, non-interruptible compute capacity for the preprocessing step. Spot instances can be terminated at any time (with a 2-minute warning) when AWS reclaims capacity, which would cause the computationally intensive preprocessing to fail. Reserved instances require a 1- or 3-year commitment and are not cost-effective for a single preprocessing job that may not run continuously.

Answer

Reserved instances

Answer

A larger instance type to speed up processing

Answer

Spot instances

Question 4

A company is using Amazon SageMaker to train and deploy machine learning models. The data science team wants to track and compare model versions, hyperparameters, and metrics across multiple training jobs. Which TWO AWS services should they use together to achieve this? (Choose TWO.)

Accepted Answer

Amazon S3. Amazon S3 is correct because it serves as the central repository for storing model artifacts, training data, and output files from SageMaker training jobs. By default, SageMaker saves model artifacts and training results to S3, enabling version tracking and reproducibility across experiments.

Answer

Amazon RDS

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Amazon CloudWatch Logs

Answer

AWS Glue

Question 5

A company is deploying a machine learning model to an Amazon SageMaker endpoint. The model receives requests with sensitive data that must be encrypted in transit and at rest. Additionally, the company needs to control access to the endpoint using AWS IAM. Which THREE steps should the company take to meet these requirements? (Choose THREE.)

Accepted Answer

Enable HTTPS for the endpoint. Enabling HTTPS for the SageMaker endpoint encrypts data in transit using TLS/SSL. SageMaker endpoints support HTTPS by default when using the InvokeEndpoint API, and you can enforce HTTPS by configuring a custom VPC and security group to only allow HTTPS traffic. Option D is correct because encryption at rest for the endpoint's ML storage volume can be enabled by specifying an AWS KMS key when creating the endpoint configuration. This encrypts the temporary storage used during inference. Option E is correct by attaching a resource-based IAM policy to the endpoint to control which IAM principals (users, roles) can invoke the endpoint, meeting access control requirements. Option B is incorrect because using a VPC is not required for encryption or access control; it provides network isolation but does not directly enforce HTTPS or IAM access. Option C is incorrect because SSE-S3 encrypts model artifacts in S3, not the endpoint's ML storage volume; encryption at rest for the endpoint must be configured at the endpoint level.

Answer

Configure the endpoint to use a VPC

Answer

Store the model artifacts in Amazon S3 with SSE-S3 encryption

Question 6

A data scientist is using Amazon SageMaker to build a custom training algorithm. The algorithm requires a specific library that is not included in the default SageMaker containers. The scientist wants to create a custom container that includes this library. Which TWO steps are required? (Choose TWO.)

Accepted Answer

Build a Docker image with the required library. Building a Docker image with the required library is the foundational step to create a custom container that includes dependencies not present in the default SageMaker containers. Option E is correct because the Docker image must be pushed to Amazon Elastic Container Registry (ECR) so that SageMaker can pull it when training jobs are launched. SageMaker does not directly use images stored in S3; it requires the image to be hosted in ECR.

Answer

Upload the Docker image to an Amazon S3 bucket

Answer

Create an AWS Lambda layer with the library

Answer

Register the container in the SageMaker Model Registry

Question 7

Refer to the exhibit. An IAM policy is attached to an IAM role used by a SageMaker training job. The training job fails with an access denied error when trying to write model artifacts to an S3 bucket. What is the most likely cause?

Accepted Answer

The IAM role does not have permission to write to the S3 bucket. The IAM role attached to the SageMaker training job must have an IAM policy that grants s3:PutObject permission on the S3 bucket where model artifacts are written. The access denied error indicates that the role lacks the necessary write permissions for that specific bucket, causing the training job to fail when it attempts to upload the model output.

Answer

The training job is trying to write to a different S3 bucket

Answer

The IAM role does not have permission to read the training data

Answer

The IAM role does not have permission to create training jobs

Question 8

A company has deployed a machine learning model on Amazon SageMaker for real-time inference. The endpoint uses a single ml.c5.xlarge instance. Recently, the traffic has increased, and the endpoint is returning HTTP 503 (Service Unavailable) errors during peak hours. The CloudWatch metrics show that the CPU utilization is consistently above 90% during peak times, and the Invocations metric shows that requests are being throttled. The data science team has already optimized the model to reduce inference time by 20%, but the errors persist. The company needs to resolve the issue without increasing costs significantly. Which course of action should be taken?

Accepted Answer

Configure auto-scaling for the endpoint to add instances based on CPU utilization. Configuring auto-scaling for the endpoint based on CPU utilization dynamically adjusts the number of instances to handle increased traffic, reducing HTTP 503 errors without incurring high costs during low traffic. Option A is wrong because upgrading to a larger instance type (e.g., ml.c5.2xlarge) would increase costs even during low-traffic periods, which does not align with the goal of minimizing cost increases. Option B is wrong because batch transform is designed for offline, asynchronous processing, not real-time inference as required here. Option C is wrong because spot instances can be interrupted and reclaimed by AWS, leading to potential service disruptions, and merely adding more instances without scaling logic does not solve the capacity issue efficiently.

Answer

Change the instance type to a larger size, such as ml.c5.2xlarge

Answer

Switch to batch transform to process requests in batches

Answer

Use spot instances to reduce costs and add more instances

Question 9

A machine learning engineer is responsible for deploying a model that was trained using a custom algorithm in Amazon SageMaker. The engineer has built a Docker container that includes the inference code and has tested it locally. The engineer now wants to deploy the container to a SageMaker endpoint for real-time inference. The engineer has already created the model in SageMaker by specifying the image URI and the model artifacts location in S3. However, when the engineer tries to create an endpoint configuration, the operation fails with an error indicating that the model is not in an 'Active' state. What should the engineer do to resolve this issue?

Accepted Answer

Check the CloudWatch logs for the container to ensure the inference server starts correctly. When a model is not in 'Active' state after creation, it typically indicates that the container's health check failed. Checking CloudWatch logs for the container helps identify why the inference server is not starting correctly. Option B is incorrect because the issue is with the model's state, not its name. Option C is incorrect because deleting and recreating the model would not resolve the underlying health check problem without fixing the container. Option D is incorrect because using a different image URI would change the container but not address the health check failure if the root cause is in the inference code or configuration.

Answer

Create the endpoint configuration with a different model name

Answer

Delete and re-create the model, then wait for a few minutes

Answer

Re-create the model using a different image URI

Question 10

A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job uses a large dataset stored in Amazon S3, and the scientist wants to use pipe mode to stream the data directly from S3 to the training instance, reducing the time needed to download the data. The training job is configured with 'InputMode' set to 'Pipe'. However, the training job fails with an error indicating that the algorithm does not support pipe mode. What should the scientist do to resolve this issue?

Accepted Answer

Change the 'InputMode' to 'File'. When a built-in algorithm does not support pipe mode, the simplest solution is to change the InputMode to 'File', which downloads the entire dataset before training. Option B is incorrect because pipe mode support depends on the algorithm, not the instance type. Option C is incorrect because AWS Glue is used for ETL and cannot directly stream data to a SageMaker training job. Option D is incorrect because while switching to an algorithm that supports pipe mode is possible, it may be unnecessary if the current algorithm works well with file mode, and changing the input mode is a simpler fix without altering the algorithm.

Answer

Use a different instance type that supports pipe mode

Answer

Use AWS Glue to stream the data to the training instance

Answer

Switch to a different built-in algorithm that supports pipe mode

Question 11

A company has a SageMaker endpoint that serves predictions for a mobile app. The endpoint is deployed on a single ml.m5.large instance. Recently, users have reported that the app sometimes returns outdated predictions. The data science team has confirmed that the model is updated daily by retraining with new data and creating a new endpoint configuration. However, the endpoint still returns predictions from the old model for some requests. The team has verified that the new endpoint configuration is associated with the endpoint and that the endpoint is in service. What is the most likely cause of this issue?

Accepted Answer

The endpoint has multiple variants and the old variant still has a weight assigned. When a SageMaker endpoint has multiple variants with assigned weights, traffic is distributed proportionally. If the old variant still has a weight greater than zero, some requests will continue to be served by the old model, causing outdated predictions. Option A is incorrect because SageMaker endpoints do not cache model artifacts; they load the model from S3 into memory. Option C is incorrect because the mobile app's CDN caching is unrelated to the SageMaker endpoint's model selection. Option D is incorrect because the team confirmed that the new endpoint configuration is associated with the endpoint and the endpoint is in service, meaning the configuration is deployed.

Answer

The old model artifacts are still being cached by the endpoint

Answer

The mobile app is using a CDN that caches the predictions

Answer

The new endpoint configuration has not been deployed to the endpoint

Question 12

A data science team is deploying a machine learning model using Amazon SageMaker. The model requires GPU inference and must handle variable traffic with low latency. Which TWO options should the team implement to meet these requirements? (Choose TWO.)

Accepted Answer

Use a SageMaker multi-model endpoint with a GPU instance to serve multiple models.. A is correct because a SageMaker multi-model endpoint with a GPU instance allows you to host multiple models on a single endpoint, dynamically loading and unloading them based on traffic, while providing GPU acceleration for low-latency inference. This approach efficiently handles variable traffic patterns by scaling the endpoint and leveraging GPU compute for deep learning models.

Answer

Deploy to a SageMaker real-time endpoint using a CPU instance and attach an Elastic Inference accelerator.

Answer

Use AWS Lambda with an attached GPU function for inference.

Answer

Host the model on a SageMaker batch transform job with GPU instances.

Question 13

A machine learning engineer is designing an automated ML pipeline for training and deploying models. The pipeline must include data validation, model training, hyperparameter tuning, and model deployment. The engineer wants to use AWS services that integrate well and provide version control. Which THREE services should be combined to achieve this? (Choose THREE.)

Accepted Answer

AWS Step Functions. AWS Step Functions orchestrates the ML pipeline by coordinating data validation, model training, hyperparameter tuning, and deployment as a state machine. It integrates natively with Amazon SageMaker for training and tuning jobs, and with AWS CodePipeline for CI/CD, enabling version control and automated transitions between pipeline stages.

Answer

AWS Glue

Answer

Amazon EMR

Question 14

A company uses Amazon SageMaker to train machine learning models. The data science team has developed a training script that uses TensorFlow. They want to run the training job on a GPU instance (ml.p3.2xlarge) and store the model artifact in Amazon S3. The training job completes successfully, but the model artifact is not saved to S3. The team has confirmed that the S3 bucket policy allows write access from the SageMaker execution role. The training script uses the TensorFlow estimator with the following configuration:

```
tensorflow_estimator = TensorFlow(
    entry_point='train.py',
    role='arn:aws:iam::123456789012:role/SageMakerExecutionRole',
    instance_count=1,
    instance_type='ml.p3.2xlarge',
    output_path='s3://my-bucket/output',
    framework_version='2.3',
    py_version='py37',
)
```

The train.py script saves the model using `model.save('/opt/ml/model')`. What is the MOST likely reason the model artifact is not being saved to S3?

Accepted Answer

The SageMaker execution role does not have the s3:PutObject permission for the S3 bucket.. The training script correctly saves the model to /opt/ml/model, which is the default directory that SageMaker automatically uploads to the S3 output path at the end of training. Since the job completes successfully, the script ran without errors. The most likely cause is that the SageMaker execution role lacks the s3:PutObject permission on the S3 bucket. Although the bucket policy allows write access from the role, the role itself must have the appropriate IAM permission. Option B is correct. Option A is incorrect because saving to /opt/ml/model is correct. Option C is incorrect because output_path does not require a trailing slash and is correctly formatted. Option D is incorrect because TensorFlow estimator does not have a model_dir parameter that overrides the default; the default is /opt/ml/model.

Answer

The training script must save the model to /opt/ml/model/saved_model instead of /opt/ml/model.

Answer

The output_path parameter is incorrectly formatted; it should include a trailing slash.

Answer

The TensorFlow estimator requires the model_dir parameter to be set to the S3 output path.

Question 15

A company is using Amazon SageMaker to host a real-time inference endpoint for a natural language processing model. The endpoint is configured with an ml.m5.large instance. After deployment, the company observes that the inference latency is higher than expected, and the endpoint is experiencing CPU utilization near 100% during peak hours. The model is a PyTorch model that uses a transformer architecture. The company wants to reduce latency without increasing cost significantly. Which approach should the company take?

Accepted Answer

Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration.. The issue is high CPU utilization causing latency for a PyTorch transformer model. GPU instances like ml.g4dn.xlarge can significantly accelerate inference through parallel processing, reducing latency. Option D is correct. Elastic Inference (C) may provide some acceleration but is less effective for transformer models and adds complexity. Auto Scaling (A) helps with traffic spikes but does not reduce per-request latency. Batch Transform (B) is for offline inference, not real-time.

Answer

Configure the endpoint with Auto Scaling to add more instances during peak hours.

Answer

Switch to batch transform for inference.

Answer

Attach an Elastic Inference accelerator to the existing instance.