MLA-C01 VALID EXAM PATTERN, EXAM MLA-C01 INTRODUCTION

MLA-C01 Valid Exam Pattern, Exam MLA-C01 Introduction

MLA-C01 Valid Exam Pattern, Exam MLA-C01 Introduction

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST effort?

  • A. Build a container on AWS that includes custom packages and a choice of ML frameworks.
  • B. Use SageMaker script mode and premade images for ML frameworks.
  • C. Use SageMaker built-in algorithms to train the proprietary datasets.
  • D. Purchase similar production models through AWS Marketplace.

Answer: B

Explanation:
SageMaker script mode allows you to bring existing custom Python scripts and run them on AWS with minimal changes. SageMaker provides prebuilt containers for ML frameworks like PyTorch, simplifying the migration process. This approach enables the company to leverage their existing Python scripts and domain knowledge while benefiting from the scalability and managed environment of SageMaker. It requires the least effort compared to building custom containers or retraining models from scratch.


NEW QUESTION # 34
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?

  • A. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
  • B. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
  • C. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
  • D. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.

Answer: A

Explanation:
SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.


NEW QUESTION # 35
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

  • A. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
  • B. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
  • C. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
  • D. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.

Answer: B

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.


NEW QUESTION # 36
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 SageMaker batch transform job to process all the images in the S3 bucket.
  • D. 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.

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 # 37
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?

  • A. Use SageMaker Model Monitor on the deployed model.
  • B. Add a shadow endpoint. Analyze prediction differences on samples.
  • C. Show the distribution of inferences from A/# testing in Amazon CloudWatch.
  • D. Use SageMaker Clarify on the deployed model.

Answer: D

Explanation:
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.


NEW QUESTION # 38
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Both practice exams (web-based & desktop) give a Amazon MLA-C01 real exam feeling and identify your mistakes so you can overcome your weaknesses before the MLA-C01 final test. The desktop Amazon MLA-C01 Practice Test software works on Windows after software installation. You can take the web-based AWS Certified Machine Learning Engineer - Associate MLA-C01 practice exam via any operating system.

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