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It is well known that certificates are not versatile, but without a Amazon MLA-C01 certification you are a little inferior to the same competitors in many ways. Compared with the people who have the same experience, you will have the different result and treatment if you have a AWS Certified Machine Learning Engineer - Associate MLA-C01 Certification.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q50-Q55):

NEW QUESTION # 50
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Deploy models by using AWS Lambda functions.
B. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
C. Use PyTorch or TensorFlow with the distributed training option.
D. Use Amazon SageMaker Ground Truth for data labeling.
E. Use AWS Trainium instances for training.

Answer: B,E

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 51
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?

A. Run AWS Compute Optimizer.
B. Check AWS CloudTrail event history for the creation of the resources.
C. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
D. Create code to evaluate each instance's memory and compute usage.

Answer: A

Explanation:
AWS Compute Optimizer analyzes the resource usage of Amazon EC2 instances, ECS services, Lambda functions, and Amazon EBS volumes. It provides actionable recommendations to optimize resource utilization and reduce costs, such as resizing instances, moving workloads to Spot Instances, or changing volume types. This solution requires the least development effort because Compute Optimizer is a managed service that automatically generates insights and recommendations based on historical usage data.

NEW QUESTION # 52
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.
C. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
D. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.

Answer: D

Explanation:
UsingAmazon EventBridgewith an event pattern that matches S3 upload events provides an automated, low- effort solution. When new data is uploaded to the S3 bucket, the EventBridge rule triggers the SageMaker pipeline. This approach minimizes operational overhead by eliminating the need for custom scripts or external orchestration tools while seamlessly integrating with the existing S3 and SageMaker setup.

NEW QUESTION # 53
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

A. Low precision
B. High recall
C. High precision
D. Low recall

Answer: B

Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.

NEW QUESTION # 54
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 Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
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 batch transform job to process all the images in the S3 bucket.

Answer: B

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 # 55
......

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Accurate Test MLA-C01 Study Guide | 100% Free MLA-C01 Real Exam Answers

MLA-C01 Real Exam Answers & Latest MLA-C01 Test Preparation

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q50-Q55):

Quiz 2025 MLA-C01: AWS Certified Machine Learning Engineer - Associate Pass-Sure Test Study Guide

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