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DumpTOP를 검색을 통해 클릭하게된 지금 이 순간 IT인증자격증취득Amazon MLA-C01시험은 더는 힘든 일이 아닙니다. 다른 분들이Amazon MLA-C01시험준비로 수없는 고민을 할때 고객님은 저희 Amazon MLA-C01덤프로 제일 빠른 시일내에 시험을 패스하여 자격증을 손에 넣을수 있습니다.
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Amazon인증 MLA-C01시험을 패스하여 자격증을 취득하여 승진이나 이직을 꿈구고 있는 분이신가요? 이 글을 읽게 된다면Amazon인증 MLA-C01시험패스를 위해 공부자료를 마련하고 싶은 마음이 크다는것을 알고 있어 시장에서 가장 저렴하고 가장 최신버전의 Amazon인증 MLA-C01덤프자료를 강추해드립니다. 높은 시험패스율을 자랑하고 있는Amazon인증 MLA-C01덤프는 여러분이 승진으로 향해 달리는 길에 날개를 펼쳐드립니다.자격증을 하루 빨리 취득하여 승진꿈을 이루세요.
질문 # 92
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?
정답：D
설명：
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.
질문 # 93
A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?
정답：B
설명：
Scenario: The company wants to perform online validation of a new ML model on 10% of the traffic before fully deploying the model in production. The setup must have minimal operational overhead.
Why Use SageMaker Production Variants?
* Built-In Traffic Splitting: Amazon SageMaker endpoints support production variants, allowing multiple models to run on a single endpoint. You can direct a percentage of incoming traffic to each variant by adjusting the variant weights.
* Ease of Management: Using production variants eliminates the need for additional infrastructure like separate endpoints or custom ALB configurations.
* Monitoring with CloudWatch: SageMaker automatically integrates with CloudWatch, enabling real- time monitoring of model performance and invocation metrics.
Steps to Implement:
* Deploy the New Model as a Production Variant:
* Update the existing SageMaker endpoint to include the new model as a production variant. This can be done via the SageMaker console, CLI, or SDK.
Example SDK Code:
import boto3
sm_client = boto3.client('sagemaker')
response = sm_client.update_endpoint_weights_and_capacities(
EndpointName='existing-endpoint-name',
DesiredWeightsAndCapacities=[
{'VariantName': 'current-model', 'DesiredWeight': 0.9},
{'VariantName': 'new-model', 'DesiredWeight': 0.1}
]
)
* Set the Variant Weight:
* Assign a weight of 0.1 to the new model and 0.9 to the existing model. This ensures 10% of traffic goes to the new model while the remaining 90% continues to use the current model.
* Monitor the Performance:
* Use Amazon CloudWatch metrics, such as InvocationCount and ModelLatency, to monitor the traffic and performance of each variant.
* Validate the Results:
* Analyze the performance of the new model based on metrics like accuracy, latency, and failure rates.
Why Not the Other Options?
* Option B: Setting the weight to 1 directs all traffic to the new model, which does not meet the requirement of splitting traffic for validation.
* Option C: Creating a new endpoint introduces additional operational overhead for traffic routing and monitoring, which is unnecessary given SageMaker's built-in production variant capability.
* Option D: Configuring the ALB to route traffic requires manual setup and lacks SageMaker's seamless variant monitoring and traffic splitting features.
Conclusion:
Using production variants with a weight of 0.1 for the new model on the existing SageMaker endpoint provides the required traffic split for online validation with minimal operational overhead.
References:
Amazon SageMaker Endpoints
SageMaker Production Variants
Monitoring SageMaker Endpoints with CloudWatch
질문 # 94
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?
정답：D
설명：
Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.
Key Features of SageMaker Model Registry:
* Centralized Cataloging: Organizes models intoModel Groups, each containing multiple versions.
* Version Control: Maintains a history of model iterations, making it easier to track changes.
* Metadata Association: Attach metadata such as training metrics and performance evaluations to models.
* Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.
* Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.
Implementation Steps:
* Create a Model Group: Organize related models into groups to simplify management and versioning.
* Register Model Versions: Each model iteration is registered as a version within a specific Model Group.
* Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.
* Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.
Benefits:
* Centralized Management: Provides a unified platform to manage models efficiently.
* Streamlined Deployment: Facilitates smooth transitions from development to production.
* Governance and Compliance: Supports metadata association and approval processes.
By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.
References:
* AWS Documentation: SageMaker Model Registry
* AWS Blog: Model Registry Features and Usage
질문 # 95
A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features.
Which solution will meet this requirement with the LEAST operational overhead?
정답：A
설명：
Option A is correct because the requirement is to detect changes in the input data distribution of model features , which is a data quality / data drift monitoring problem. AWS documentation states that Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens. The documented workflow is to enable data capture, create a baseline from training data, and then run monitoring jobs that compare incoming inference data against that baseline. That directly matches the need to automatically detect changes in feature distributions.
AWS also documents that Model Monitor can emit metrics to Amazon CloudWatch , and those metrics can be used with CloudWatch alarms to notify teams when data quality drifts beyond acceptable thresholds.
That makes Option A the lowest-operational-overhead solution because it uses SageMaker's built-in monitoring capability plus managed alerting, rather than requiring custom drift logic. The inclusion of emit_metrics and CloudWatch alarming is consistent with the SageMaker monitoring pattern for automated notification.
The other options are weaker. Option B is for model quality monitoring, which focuses on prediction performance against ground truth, not shifts in the input feature distribution. Option C uses SageMaker Debugger, which is aimed at training-time debugging and custom rule analysis rather than managed production data drift monitoring. Option D relies on manual log analysis and endpoint performance metrics, which does not directly solve feature-distribution drift detection and adds more operational effort. Therefore, the best AWS-documented answer is A .
질문 # 96
A company needs to deploy a custom-trained classification ML model on AWS. The model must make near real-time predictions with low latency and must handle variable request volumes.
Which solution will meet these requirements?
정답：B
설명：
For near real-time inference with low latency and variable traffic, AWS recommends deploying models to managed SageMaker endpoints. By enabling auto scaling, the endpoint automatically adjusts the number of instances based on request volume, ensuring consistent performance while optimizing cost.
Amazon SageMaker Endpoints abstracts infrastructure management, health checks, scaling, and model deployment. This provides lower operational overhead than managing EC2 instances manually.
Batch transform is for offline inference. API Gateway with S3 cannot serve ML models. EC2-based deployments require manual scaling and monitoring.
Therefore, a SageMaker endpoint with auto scaling is the correct solution.
질문 # 97
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