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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 2	Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 3	Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 4	Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 5	Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

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Amazon AWS Certified AI Practitioner Sample Questions (Q31-Q36):

NEW QUESTION # 31
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?

A. Generation of custom foundation models (FMs) to predict customer needs
B. Selecting the foundation model (FM) based on predefined criteria and metrics
C. Automation of repetitive tasks and orchestration of complex workflows
D. Automatically calling multiple foundation models (FMs) and consolidating the results

Answer: C

Explanation:
Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.
* Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows":
This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.
* Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.
* Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.
* Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.
AWS AI Practitioner References:
* Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.

NEW QUESTION # 32
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?

A. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
B. Summarize the response text depending on the age of the user so that younger users receive shorter responses.
C. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
D. Add a role description to the prompt context that instructs the model of the age range that the response should target.

Answer: D

Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
* Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
* Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
* Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
* Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner References:
* Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.

NEW QUESTION # 33
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?

A. Toxicity
B. Privacy
C. Plagiarism
D. Hallucinations

Answer: C

NEW QUESTION # 34
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

A. Decrease the length of output tokens
B. Decrease the temperature value
C. Increase the maximum generation length
D. Increase the temperature value

Answer: B

Explanation:
The temperature parameter in a large language model (LLM) controls the randomness of the model's output.
A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.
* Option A (Correct): "Decrease the temperature value": This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.
* Option B: "Increase the temperature value" is incorrect because it would make the output more random and less consistent.
* Option C: "Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.
* Option D: "Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model's responses.
AWS AI Practitioner References:
* Understanding Temperature in Generative AI Models: AWS documentation explains that adjusting the temperature parameter affects the model's output randomness, with lower values providing more consistent outputs.

NEW QUESTION # 35
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

A. Convert audio files to text documents by using ML models.
B. Enable voice commands for coding and providing natural language search.
C. Run an application without provisioning or managing servers.
D. Create software snippets, reference tracking, and open-source license tracking.

Answer: B

NEW QUESTION # 36
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Latest AIF-C01 Exam Price, Study AIF-C01 Tool

Amazon AIF-C01 Exam Syllabus Topics:

AIF-C01 best Amazon certification exam questions and answers free download

Amazon AWS Certified AI Practitioner Sample Questions (Q31-Q36):

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