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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q83-Q88):
NEW QUESTION # 83
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?
- A. AWS::SageMaker::Endpoint
- B. AWS::SageMaker::Model
- C. AWS::SageMaker::Pipeline
- D. AWS::SageMaker::NotebookInstance
Answer: B
Explanation:
The AWS::SageMaker::Model resource in AWS CloudFormation is used to create an ML model in Amazon SageMaker. This model can then be hosted on an endpoint by using the AWS::SageMaker::Endpoint resource. The model resource defines the container or algorithm to use for hosting and the S3 location of the model artifacts.
NEW QUESTION # 84
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?
- A. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
- B. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
- C. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
- D. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.
Answer: D
Explanation:
Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.
CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.
The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.
This is the standard and most efficient way to achieve the desired functionality.
NEW QUESTION # 85
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
- B. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.
- C. Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
- D. Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
Answer: B
Explanation:
SageMaker Debugger provides built-in rules to automatically detect issues like vanishing gradients, underutilized GPU, and overfitting during training jobs. It generates real-time metrics and allows users to define predefined actions that are triggered when specific issues occur. This solution minimizes operational overhead by leveraging the managed monitoring capabilities of SageMaker Debugger without requiring custom setups or extensive manual intervention.
NEW QUESTION # 86
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster.Specify a list of images to process for each AWS Batch job.
- B. Create an Amazon SageMaker batch transform job to process all the images in the S3 bucket.
- C. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling. Host the model on the EKS cluster. Run a script to make an inference request for each image.
- D. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
Answer: D
Explanation:
SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.
It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.
NEW QUESTION # 87
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?
- A. Use the AWS Glue FindMatches transform to detect duplicates.
- B. Use Amazon QuickSight ML Insights to build a custom deduplication model.
- C. Use Amazon Mechanical Turk jobs to detect duplicates.
- D. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
Answer: A
Explanation:
Scenario:The dataset contains duplicate records that need to be detected with minimal code development.
Why FindMatches in AWS Glue?
* Purpose-Built for Deduplication:The FindMatches transform in AWS Glue is specifically designed to identify duplicate records in structured or semi-structured datasets.
* Machine Learning-Based:It uses ML to identify duplicates based on configurable thresholds and provides flexibility for tuning accuracy.
* Low Code Overhead:Minimal development effort is required as Glue provides an interactive console for configuring and running FindMatches transforms.
Steps to Implement:
* Prepare the Data:Upload the unstructured dataset to an S3 bucket and define a schema if needed.
* Create a Glue Job:
* Use the AWS Glue Studio to create a job and select the FindMatches transform.
* Specify key attributes for deduplication.
* Run and Evaluate:Execute the Glue job, and review the results for duplicates.
* Resolve Duplicates:Export results to an S3 bucket or process them as needed.
References:
* AWS Glue FindMatches Documentation
* FindMatches Transform Example
NEW QUESTION # 88
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