Free Access to Amazon.AIF-C01.v2025-08-07.q87 with Valid Practice Test (Page 2)

Question 1

A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A.Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
B.Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
C.Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
D.Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

Question 2

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?

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.

Correct Answer: B

The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Explanation:
Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
References:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest
/userguide/agents.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)

Question 3

A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times.
Which solution gives the LLM the ability to use content from previous customer messages?

A.Turn on model invocation logging to collect messages.
B.Add messages to the model prompt.
C.Use Amazon Personalize to save conversation history.
D.Use Provisioned Throughput for the LLM.

Correct Answer: B

The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain context across interactions, allowing it to respond coherently based on the ongoing conversation.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"To enable a large language model (LLM) to maintain context in a conversation, you can include previous messages in the model prompt. This approach, often referred to as providing conversation history, allows the LLM to generate responses that are contextually relevant toprior interactions." (Source: AWS Bedrock User Guide, Building Conversational Applications) Detailed Explanation:
* Option A: Turn on model invocation logging to collect messages.Model invocation logging records interactions for auditing or debugging but does not provide the LLM with access to previous messages during inference to maintain conversation context.
* Option B: Add messages to the model prompt.This is the correct answer. Including previous messages in the prompt gives the LLM the conversation history it needs to respond appropriately, a common practice for chatbots on Amazon Bedrock.
* Option C: Use Amazon Personalize to save conversation history.Amazon Personalize is for building recommendation systems, not for managing conversation history in a chatbot. This option is irrelevant.
* Option D: Use Provisioned Throughput for the LLM.Provisioned Throughput in Amazon Bedrock ensures consistent performance for model inference but does not address the need to use previous messages in the conversation.
References:
AWS Bedrock User Guide: Building Conversational Applications (https://docs.aws.amazon.com/bedrock
/latest/userguide/conversational-apps.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Chatbots Amazon Bedrock Developer Guide: Managing Conversation Context (https://aws.amazon.com/bedrock/)

Question 4

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?

A.Choose a lower temperature value
B.Enable model invocation logging
C.Use a different FM
D.Create an Amazon Bedrock knowledge base

Question 5

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?

A.Accuracy
B.Root mean squared error (RMSE)
C.R-squared score
D.Learning rate

Question 1

Question 2

Question 3

Question 4

Question 5

Download PDF File