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Amazon AIF-C01 Practice Test - Questions Answers, Page 6

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List of questions

Question 51

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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

Question 52

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A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. Th

Question 53

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An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms. What should the firm do when developi

Question 54

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A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluati

Question 55

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A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to impleme

Question 56

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A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can a

Question 57

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A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time

Question 58

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A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible. Which solution will meet these requirements?

Question 59

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A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer cal

Question 60

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A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requ

Question 51

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?

Website engagement rate

Average call duration

Corporate social responsibility

Regulatory compliance

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A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.

Which solution will meet these requirements?

Customize the model by using fine-tuning.

Decrease the number of tokens in the prompt.

Increase the number of tokens in the prompt.

Use Provisioned Throughput.

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Question 53

An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.

What should the firm do when developing and deploying the LLM? (Select TWO.)

Include fairness metrics for model evaluation.

Adjust the temperature parameter of the model.

Modify the training data to mitigate bias.

Avoid overfitting on the training data.

Apply prompt engineering techniques.

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Question 54

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.

Which evaluation metric should the company use to measure the model's performance?

R-squared score

Accuracy

Root mean squared error (RMSE)

Learning rate

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Question 55

A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.

What are the key benefits of using Amazon Bedrock agents that could help this retailer?

Generation of custom foundation models (FMs) to predict customer needs

Automation of repetitive tasks and orchestration of complex workflows

Automatically calling multiple foundation models (FMs) and consolidating the results

Selecting the foundation model (FM) based on predefined criteria and metrics

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Question 56

A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.

Which solution will meet these requirements?

Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

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Question 57

A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.

Which SageMaker inference option meets these requirements?

Real-time inference

Serverless inference

Asynchronous inference

Batch transform

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Question 58

A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.

Which solution will meet these requirements?

Deploy optimized small language models (SLMs) on edge devices.

Deploy optimized large language models (LLMs) on edge devices.

Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.

Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

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Question 59

A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.

Which solution meets these requirements?

Build a conversational chatbot by using Amazon Lex.

Transcribe call recordings by using Amazon Transcribe.

Extract information from call recordings by using Amazon SageMaker Model Monitor.

Create classification labels by using Amazon Comprehend.

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Question 60

A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.

Which SageMaker feature meets these requirements?

Amazon SageMaker Feature Store

Amazon SageMaker Data Wrangler

Amazon SageMaker Clarify

Amazon SageMaker Model Cards

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Total 87 questions