CERTIFICATION MLA-C01 TEST QUESTIONS | MLA-C01 VALID EXAM EXPERIENCE

Certification MLA-C01 Test Questions | MLA-C01 Valid Exam Experience

Certification MLA-C01 Test Questions | MLA-C01 Valid Exam Experience

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q59-Q64):

NEW QUESTION # 59
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. On-Demand Instances
  • B. Reserved Instances
  • C. Spot Instances
  • D. Dedicated Instances

Answer: C

Explanation:
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


NEW QUESTION # 60
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. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
  • B. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
  • C. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
  • D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.

Answer: A

Explanation:
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.


NEW QUESTION # 61
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

  • A. Set up shadow testing with a shadow variant of the new model.
  • B. Set up SageMaker Debugger and create a custom rule.
  • C. Set up blue/green deployments with canary traffic shifting.
  • D. Set up blue/green deployments with all-at-once traffic shifting.

Answer: A

Explanation:
Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.


NEW QUESTION # 62
A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.
The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.
How should the company deploy the model into production to meet these requirements?

  • A. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.
  • B. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
  • C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
  • D. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.

Answer: B

Explanation:
Amazon SageMaker real-time inference endpoints are designed to provide low-latency predictions in production environments. They offer built-in auto scaling to handle unpredictable bursts of requests, ensuring high availability and responsiveness. This approach is fully managed, reduces operational complexity, and is optimized for the range of request sizes (1 KB to 3 MB) specified in the requirements.


NEW QUESTION # 63
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

  • A. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
  • B. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
  • C. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
  • D. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

Answer: B


NEW QUESTION # 64
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