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?

Accepted Answer

Use a larger instance type, such as p3.8xlarge. Option A is correct because switching to a larger instance type with more memory will immediately resolve the out-of-memory error. Option B is wrong because reducing batch size may help but requires code changes and might still not be enough. Option C is wrong because using spot instances does not affect memory. Option D is wrong because using distributed training adds complexity and may not resolve memory issues on a single instance.

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. Option D is correct because SageMaker supports canary deployments using endpoint variants, allowing a small percentage of traffic to be directed to the new model version. Option A is wrong because blue/green deployment switches all traffic at once. Option B is wrong because A/B testing is for comparing models, not gradual rollout. Option C is wrong because rolling deployment is not natively supported by SageMaker endpoints.

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. Option C is correct because using on-demand instances guarantees that the instance will not be terminated during the preprocessing step. Option A is wrong because spot instances can be terminated, causing failures. Option B is wrong because reserved instances require a long-term commitment. Option D is wrong because a larger instance type increases costs unnecessarily.

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. Options A and C are correct. SageMaker Experiments provides tracking of training jobs, metrics, and parameters. Amazon S3 stores the experiment outputs and artifacts. Option B is wrong because AWS Glue is for ETL, not for experiment tracking. Option D is wrong because Amazon RDS is a relational database. Option E is wrong because CloudWatch Logs is for logs, not for experiment tracking.

Answer

Amazon RDS

Answer

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. Options A, B, and D are correct. Enabling encryption in transit using HTTPS, enabling encryption at rest for the endpoint's attached storage, and attaching an IAM policy to the endpoint to allow only authorized users are necessary. Option C is wrong because VPC settings do not encrypt data at rest. Option E is wrong because the model data in S3 should be encrypted with SSE-KMS, not SSE-S3, to meet stricter security requirements.

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. Options B and D are correct. The custom container must be built using Docker, and it must be pushed to Amazon ECR to be used by SageMaker. Option A is wrong because the container does not need to be registered in SageMaker Model Registry. Option C is wrong because the container image is stored in ECR, not S3. Option E is wrong because AWS Lambda is for serverless functions, not for running training containers.

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. Option D is correct because the policy only allows s3:GetObject, not s3:PutObject, so the training job cannot write artifacts. Option A is wrong because the policy allows sagemaker:CreateTrainingJob. Option B is wrong because the policy allows s3:GetObject for the training data. Option C is wrong because the policy does not restrict the S3 bucket; it allows GetObject on a specific path.

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. Option A is correct because adding auto-scaling based on CPU utilization or invocations will dynamically adjust the number of instances to handle the load, reducing errors without incurring costs during low traffic. Option B is wrong because increasing to a larger instance type will increase costs even during low traffic. Option C is wrong because switching to batch transform is for offline processing, not real-time. Option D is wrong because using spot instances could lead to interruptions and does not solve the capacity issue.

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. Option C is correct because the model must be in an 'Active' state before it can be deployed, and this requires the container to pass SageMaker's health check. The engineer should check the CloudWatch logs for the container to diagnose the health check failure. Option A is wrong because re-creating the model with the same image will not fix the health check issue. Option B is wrong because the model is already created; the issue is the state. Option D is wrong because the endpoint configuration cannot be created if the model is not active.

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'. Option A is correct because not all built-in algorithms support pipe mode; the scientist should use a file mode instead. Option B is wrong because the issue is not with the instance type. Option C is wrong because changing to a different algorithm may not be necessary if the current algorithm works with file mode. Option D is wrong because SageMaker does not support using Glue to stream data directly to training jobs.

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. Option D is correct because if the old endpoint variant still has a weight greater than zero, some traffic will continue to be routed to the old model. Option A is wrong because if the new configuration were not active, the endpoint would not be in service. Option B is wrong because the model artifacts are stored in S3 and are not deleted during updates. Option C is wrong because CloudFront does not cache SageMaker endpoint responses by default.

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.. Option A (SageMaker real-time endpoint with GPU instance) ensures GPU inference and low latency for real-time predictions. Option E (SageMaker multi-model endpoint) reduces cost by sharing a GPU instance across multiple models, which is efficient for variable traffic. The other options either do not support GPU (B, D) or are not suitable for real-time low-latency inference (C).

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. Option A (AWS Step Functions) orchestrates the pipeline steps. Option C (Amazon SageMaker) provides training, tuning, and deployment. Option D (AWS CodePipeline) manages version control and automation of the entire CI/CD workflow. Option B (AWS Glue) is for ETL but not pipeline orchestration. Option E (Amazon EMR) is for big data processing, not ML pipeline management.

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 TensorFlow estimator's output_path specifies where the model artifact should be uploaded after training. However, SageMaker automatically uploads the contents of /opt/ml/model to S3 at the end of training. The script is saving to the correct directory. The issue is likely that the training script is not saving the model correctly or the training fails before saving. But given the job completes successfully, the most common cause is that the SageMaker execution role does not have permission to write to the S3 bucket. The bucket policy allows write access, but the IAM role may lack the necessary S3 permissions. Option C is correct because the role needs s3:PutObject permission on the bucket. Option A is incorrect because the output_path is correctly specified. Option B is incorrect because the script saves to the right directory. Option D is incorrect because the 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. Using a GPU instance (ml.g4dn.xlarge) can accelerate inference for transformer models due to parallel processing, reducing latency. Option C is correct. Option A (Elastic Inference) may help but is less effective than a full GPU for transformer models; also, it adds complexity. Option B (Auto Scaling) helps with traffic but does not reduce per-request latency. Option D (batch transform) 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.