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MLA-C01 Dumps - AWS Certified Machine Learning Engineer - Associate Practice Exam Questions

Amazon Web Services MLA-C01 - AWS Certified Machine Learning Engineer - Associate Braindumps

Amazon Web Services MLA-C01 - AWS Certified Associate Practice Exam

  • Certification Provider:Amazon Web Services
  • Exam Code:MLA-C01
  • Exam Name:AWS Certified Machine Learning Engineer - Associate
  • Certification Name:AWS Certified Associate
  • Total Questions:149 Questions and Answers With Detailed Explanations
  • Updated on:Based on the current MLA-C01 exam blueprint. Updated on Feb 15, 2026
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Amazon Web Services MLA-C01 This Week Results

MLA-C01 Question and Answers

Question # 1

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?

Options:

A.  

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.

B.  

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.

C.  

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.

D.  

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.

Discussion 0
Question # 2

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.

Which solutions will mitigate this problem? (Choose two.)

Options:

A.  

Enable early stopping on the model.

B.  

Increase dropout in the layers.

C.  

Increase the number of layers.

D.  

Increase the number of neurons.

E.  

Investigate and reduce the sources of model bias.

Discussion 0
Question # 3

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

Options:

A.  

Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

B.  

Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

C.  

Use AWS Glue DataBrew built-in features to oversample the minority class.

D.  

Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Discussion 0

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