更新するMLA-C01技術試験試験-試験の準備方法-最高のMLA-C01模擬問題集

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効率的なAmazon MLA-C01技術試験は主要材料 & 検証するMLA-C01模擬問題集

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q38-Q43):

質問 # 38
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?

A. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
B. Configure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint.Monitor the number of invocations by using AWS CloudTrail.
C. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
D. Create a new SageMaker endpoint. Use production variants to add the new model to the new endpoint.
Monitor the number of invocations by using Amazon CloudWatch.

正解：C

解説：
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

質問 # 39
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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?

A. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
B. Use SageMaker Experiments to facilitate the approval process during model registration.
C. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
D. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

正解：C

解説：
To implement a manual approval-based workflow ensuring that only approved models are deployed to production endpoints, Amazon SageMaker provides integrated tools such asSageMaker Pipelinesand the SageMaker Model Registry.
SageMaker Pipelinesis a robust service for building, automating, and managing end-to-end machine learning workflows. It facilitates the orchestration of various steps in the ML lifecycle, including data preprocessing, model training, evaluation, and deployment. By integrating with theSageMaker Model Registry, it enables seamless tracking and management of model versions and their approval statuses.
Implementation Steps:
* Define the Pipeline:
* Create a SageMaker Pipeline encompassing steps for data preprocessing, model training, evaluation, and registration of the model in the Model Registry.
* Incorporate aCondition Stepto assess model performance metrics. If the model meets predefined criteria, proceed to the next step; otherwise, halt the process.
* Register the Model:
* Utilize theRegisterModelstep to add the trained model to the Model Registry.
* Set the ModelApprovalStatus parameter to PendingManualApproval during registration. This status indicates that the model awaits manual review before deployment.
* Manual Approval Process:
* Notify the designated approver upon model registration. This can be achieved by integrating Amazon EventBridge to monitor registration events and trigger notifications via AWS Lambda functions.
* The approver reviews the model's performance and, if satisfactory, updates the model's status to Approved using the AWS SDK or through the SageMaker Studio interface.
* Deploy the Approved Model:
* Configure the pipeline to automatically deploy models with an Approved status to the production endpoint. This can be managed by adding deployment steps conditioned on the model's approval status.
Advantages of This Approach:
* Automated Workflow:SageMaker Pipelines streamline the ML workflow, reducing manual interventions and potential errors.
* Governance and Compliance:The manual approval step ensures that only thoroughly evaluated models are deployed, aligning with organizational standards.
* Scalability:The solution supports complex ML workflows, making it adaptable to various project requirements.
By implementing this solution, the company can establish a controlled and efficient process for deploying models, ensuring that only approved versions reach production environments.
References:
* Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines
* Update the Approval Status of a Model - Amazon SageMaker

質問 # 40
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

A. Dedicated Instances
B. On-Demand Instances
C. Reserved Instances
D. Spot Instances

正解：D

解説：
Scenario:The company needs to run a batch job for 90 minutes every weekend over the next 6 months. The processing can handle interruptions, and cost-effectiveness is a priority.
Why Spot Instances?
* Cost-Effective:Spot Instances provide up to 90% savings compared to On-Demand Instances, making them the most cost-effective option for batch processing.
* Interruption Tolerance:Since the processing can tolerate interruptions, Spot Instances are suitable for this workload.
* Batch-Friendly:Spot Instances can be requested for specific durations or automatically re-requested in case of interruptions.
Steps to Implement:
* Create a Spot Instance Request:
* Use the EC2 console or CLI to request Spot Instances with desired instance type and duration.
* Use Auto Scaling:Configure Spot Instances with an Auto Scaling group to handle instance interruptions and ensure job completion.
* Run the Batch Job:Use tools like AWS Batch or custom scripts to manage the processing.
Comparison with Other Options:
* Reserved Instances:Suitable for predictable, continuous workloads, but less cost-effective for a job that runs only once a week.
* On-Demand Instances:More expensive and unnecessary given the tolerance for interruptions.
* Dedicated Instances:Best for isolation and compliance but significantly more costly.
References:
* Amazon EC2 Spot Instances
* Best Practices for Using Spot Instances
* AWS Batch for Spot Instances

質問 # 41
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 AWS CloudTrail. Configure a CloudTrail trail to send the email message.
B. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule 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 a CloudWatch alarm to send the email message.

正解：B

解説：
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.

質問 # 42
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?

A. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
B. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
C. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.
D. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.

正解：C

解説：
Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.

質問 # 43
......

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MLA-C01模擬問題集: https://www.jpexam.com/MLA-C01_exam.html

Amazon MLA-C01技術試験我々は心からあなたが首尾よく試験に合格することを願っています、MLA-C01模擬トレントの目的は、MLA-C01試験に合格することです、MLA-C01学習教材は多くの人から好評をもらいました、あなた学習がいどの要点を覚えていれば、MLA-C01本当試験にパスできます、常に実際のテストに不安がある場合、またはテストの終了時間を制御できない場合、Amazon MLA-C01試験ブレーンダンプのAPPテストエンジンは、時間指定テストを設定し、実際のテストシーンをシミュレートできます、MLA-C01試験に合格した顧客が非常に多くて、合格率は98〜100％と高くなっているからです、仮想試験環境では、クライアントはMLA-C01の質問に答えるために速度を調整し、実際の戦闘能力を訓練し、実際のテストのプレッシャーに調整することができます。

島には小作農もいるが、流人はその下という地位だった、使う食材も味付けも同じならそれは同じ料理だ、我々は心からあなたが首尾よく試験に合格することを願っています、MLA-C01模擬トレントの目的は、MLA-C01試験に合格することです。

試験の準備方法-便利なMLA-C01技術試験試験-ハイパスレートのMLA-C01模擬問題集

MLA-C01学習教材は多くの人から好評をもらいました、あなた学習がいどの要点を覚えていれば、MLA-C01本当試験にパスできます、常に実際のテストに不安がある場合、またはテストの終了時間を制御できない場合、Amazon MLA-C01試験ブレーンダンプのAPPテストエンジンは、時間指定テストを設定し、実際のテストシーンをシミュレートできます。

P.S.JpexamがGoogle Driveで共有している無料の2025 Amazon MLA-C01ダンプ：https://drive.google.com/open?id=18x-z97zIsXLwmZ1bgHacJh4iotwhBRzi

Glen Reed Glen Reed

Biography

更新するMLA-C01技術試験試験-試験の準備方法-最高のMLA-C01模擬問題集

効率的なAmazon MLA-C01技術試験は主要材料 & 検証するMLA-C01模擬問題集

Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q38-Q43):

試験の準備方法-便利なMLA-C01技術試験試験-ハイパスレートのMLA-C01模擬問題集

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Glen Reed Glen Reed

Biography

更新するMLA-C01技術試験試験-試験の準備方法-最高のMLA-C01模擬問題集

効率的なAmazon MLA-C01技術試験 は主要材料 & 検証するMLA-C01模擬問題集

Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q38-Q43):

試験の準備方法-便利なMLA-C01技術試験試験-ハイパスレートのMLA-C01模擬問題集

Quick Links

Resources

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効率的なAmazon MLA-C01技術試験は主要材料 & 検証するMLA-C01模擬問題集