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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q36-Q41):
NEW QUESTION # 36
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?
- A. Use SageMaker Model Monitor on the deployed model.
- B. Use SageMaker Clarify on the deployed model.
- C. Show the distribution of inferences from A/# testing in Amazon CloudWatch.
- D. Add a shadow endpoint. Analyze prediction differences on samples.
Answer: B
Explanation:
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.
NEW QUESTION # 37
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development.
An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
- B. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
- C. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
- D. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
Answer: D
Explanation:
Callback steps in Amazon SageMaker Pipelines allow you to integrate external processes, such as AWS Glue jobs, into the pipeline workflow. By using a Callback step, the SageMaker pipeline can trigger the AWS Glue workflow and pause execution until the Glue jobs complete. This approach provides seamless integration with minimal operational overhead, as it directly ties the pipeline's execution flow to the completion of the AWS Glue jobs without requiring additional orchestration tools or complex setups.
NEW QUESTION # 38
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?
- A. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
- B. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
- C. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
- D. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
Answer: C
Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.
NEW QUESTION # 39
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?
- A. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
- B. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
- C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
- D. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
Answer: C
Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.
NEW QUESTION # 40
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?
- A. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
- B. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
- C. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
- D. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
Answer: A
Explanation:
AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.
NEW QUESTION # 41
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