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Amazon Web Services Updated MLA-C01 Exam Questions and Answers by malcolm

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Amazon Web Services MLA-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning Engineer - Associate
Exam Code: MLA-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Associate
Questions: 207 Q&A's Shared By: malcolm
Question 40

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.

Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

Options:

A.

Use Amazon Rekognition to analyze sentiments of the chat conversations.

B.

Train a Naive Bayes classifier to analyze sentiments of the chat conversations.

C.

Use Amazon Comprehend to analyze sentiments of the chat conversations.

D.

Use random forests to classify sentiments of the chat conversations.

Discussion
Question 41

A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock.

The EC2 instances are in a private subnet and must remain private during the implementation. The EC2 instances have a security group that allows access to all IP addresses in the private subnet.

What should the ML engineer do to establish a connection between the EC2 instances and Amazon Bedrock?

Options:

A.

Modify the security group to allow inbound and outbound traffic to and from Amazon Bedrock.

B.

Use AWS PrivateLink to access Amazon Bedrock through an interface VPC endpoint.

C.

Configure Amazon Bedrock to use the private subnet where the EC2 instances are deployed.

D.

Use AWS Direct Connect to link the VPC to Amazon Bedrock.

Discussion
Question 42

An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.

The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.

Which solution will meet these requirements in the MOST operationally efficient way?

Options:

A.

Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.

B.

Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.

C.

Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.

D.

Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.

Discussion
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Question 43

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.

Which solution will meet this requirement?

Options:

A.

Configure the competitor's name as a blocked phrase in Amazon Q Business.

B.

Configure an Amazon Q Business retriever to exclude the competitor's name.

C.

Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

D.

Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Discussion
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