MLA-C01最新試験情報 & MLA-C01受験体験AmazonのMLA-C01試験を準備するのは残念ですが、合格してからあなたはITに関する仕事から美しい未来を持っています。だから、我々のすべきのことはあなたの努力を無駄にしないということです。弊社のJpshikenの提供するAmazonのMLA-C01試験ソフトのメリットがみんなに認められています。我々のデモから感じられます。我々は力の限りにあなたにAmazonのMLA-C01試験に合格します。 Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q137-Q142):質問 # 137
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?
A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform.
Specify the Gamma contrast option.
D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
正解：C
質問 # 138
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?
A. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
B. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.
C. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
D. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
正解：C
解説：
SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.
質問 # 139
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
A. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
B. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
C. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
D. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
正解：D
解説：
Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.
質問 # 140
A company wants to improve its customer retention ML model. The current model has 85% accuracy and a new model shows 87% accuracy in testing. The company wants to validate the new model's performance in production.
Which solution will meet these requirements?
A. Run both models in parallel for 4 weeks. Analyze offline predictions weekly by using historical customer data analysis.
B. Run A/B testing on both models for 4 weeks. Route 20% of traffic to the new model. Monitor customer retention rates across both variants.
C. Deploy the new model for 4 weeks across all production traffic. Monitor performance metrics and validate improvements.
D. Implement alternating deployments for 4 weeks between the current model and the new model. Track performance metrics for comparison.
正解：B
解説：
AWS ML best practices recommend A/B testing to validate model improvements in production while minimizing risk. By routing a controlled portion of live traffic (for example, 20%) to the new model and keeping the majority of traffic on the existing model, the company can directly compare real-world performance using the same data distribution.
This approach allows statistically meaningful comparison of business metrics such as customer retention, rather than relying solely on offline accuracy. It also limits potential negative impact if the new model underperforms in production.
Deploying the new model to 100% of traffic (Option A) introduces unnecessary risk. Offline analysis (Option C) does not reflect live user behavior. Alternating deployments (Option D) introduces confounding factors such as time-based effects.
Therefore, A/B testing is the correct solution.
質問 # 141
A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and display the results on a dashboard. An ML engineer creates a bias monitoring job.
How should the ML engineer capture bias metrics to display on the dashboard?
A. Capture Amazon CloudWatch metrics from SageMaker Clarify.
B. Capture SageMaker Model Monitor metrics from Amazon SNS.
C. Capture AWS CloudTrail metrics from SageMaker Clarify.
D. Capture SageMaker Model Monitor metrics from Amazon EventBridge.
正解：A
解説：
Amazon SageMaker Clarify is the AWS service used to detect and quantify bias and fairness metrics in ML models. When bias monitoring jobs run, Clarify publishes bias metrics directly to Amazon CloudWatch.
CloudWatch metrics can be visualized using CloudWatch dashboards or integrated into other monitoring tools, making them ideal for real-time or periodic bias reporting.
CloudTrail logs API activity and does not capture ML metrics. EventBridge and SNS are used for event routing and notifications, not metric visualization.
AWS documentation explicitly states that Clarify bias metrics are emitted to Amazon CloudWatch, which is the correct source for dashboards.
Therefore, Option B is the correct and AWS-verified answer.