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Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 3

Questions 21

A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.

Which Amazon SageMaker inference option will meet these requirements?

Options:
A.

Batch transform

B.

Real-time inference

C.

Serverless inference

D.

Asynchronous inference

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Questions 22

A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.

Which business objective should the company use to evaluate the effect of the LLM chatbot?

Options:
A.

Website engagement rate

B.

Average call duration

C.

Corporate social responsibility

D.

Regulatory compliance

Questions 23

A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base.

Which technique should the company use to optimize the generated responses?

Options:
A.

Use Retrieval Augmented Generation (RAG).

B.

Use few-shot prompting.

C.

Set the temperature to 1.

D.

Decrease the token size.

Questions 24

A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?

Options:
A.

Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.

B.

Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.

C.

Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.

D.

Use Amazon SageMaker AI to build the application by training a new ML model.

Questions 25

Which option is an example of unsupervised learning?

Options:
A.

A model that groups customers based on their purchase history

B.

A model that classifies images as dogs or cats

C.

A model that predicts a house's price based on various features

D.

A model that learns to play chess by using trial and error

Questions 26

A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.

Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

Options:
A.

Amazon EC2 C series

B.

Amazon EC2 G series

C.

Amazon EC2 P series

D.

Amazon EC2 Trn series

Questions 27

An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?

Options:
A.

Amazon Comprehend

B.

Amazon Polly

C.

Amazon Transcribe

D.

Amazon Translate

Questions 28

A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.

Which solution will meet this requirement?

Options:
A.

Use a different FM

B.

Choose a lower temperature value

C.

Create an Amazon Bedrock knowledge base

D.

Enable model invocation logging

Questions 29

A company has a generative AI application that uses a pre-trained foundation model (FM) on Amazon Bedrock. The company wants the FM to include more context by using company information.

Which solution meets these requirements MOST cost-effectively?

Options:
A.

Use Amazon Bedrock Knowledge Bases.

B.

Choose a different FM on Amazon Bedrock.

C.

Use Amazon Bedrock Agents.

D.

Deploy a custom model on Amazon Bedrock.

Questions 30

A company wants to create an application to summarize meetings by using meeting audio recordings.

Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)

• Convert meeting audio recordings to meeting text files by using Amazon Polly.

• Convert meeting audio recordings to meeting text files by using Amazon Transcribe.

• Store meeting audio recordings in an Amazon S3 bucket.

• Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.

• Summarize meeting text files by using Amazon Bedrock.

• Summarize meeting text files by using Amazon Lex.

AIF-C01 Question 30

Options: