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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

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 3

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.

Topic 4

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 5

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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q100-Q105):

NEW QUESTION # 100
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. Hallucinations
B. Plagiarism
C. Privacy
D. Toxicity

Answer: B

Explanation:
The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.
* Plagiarism:
* Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.
* This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.
* Why Option C is Correct:
* Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.
* Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.
* Why Other Options are Incorrect:
* A. Toxicity: Refers to harmful or offensive content generation, not content copying.
* B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.
* D. Privacy: Involves the misuse or exposure of personal information, not copying content.

NEW QUESTION # 101
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

A. Tree of thoughts
B. One-shot prompting
C. Retrieval Augmented Generation (RAG)
D. Prompt chaining

Answer: D

Explanation:
Prompt chaining is a technique where a complex task is broken into smaller subtasks, and the outputs of one subtask are used as inputs for the next, sequentially guiding a large language model (LLM) to solve the problem step-by-step. This method is particularly useful for complex tasks that require multiple reasoning steps.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt chaining involves breaking a complex task into smaller subtasks and sequentially passing the output of one subtask as input to the next, enabling large language models to handle intricate problems by solving them step-by-step." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
* Option A: One-shot promptingOne-shot prompting provides a single example to guide the LLM, but it does not break tasks into smaller subtasks or handle sequential processing.
* Option B: Prompt chainingThis is the correct answer. Prompt chaining divides a complex task into smaller, manageable subtasks, solving them sequentially with the LLM, as described.
* Option C: Tree of thoughtsTree of thoughts involves exploring multiple reasoning paths simultaneously, not breaking tasks into sequential subtasks.
* Option D: Retrieval Augmented Generation (RAG)RAG retrieves external information to augment LLM responses but does not specifically break tasks into sequential subtasks.
References:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest
/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Prompting
Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/)

NEW QUESTION # 102
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. Run an application without provisioning or managing servers.
C. Enable voice commands for coding and providing natural language search.
D. Create software snippets, reference tracking, and open-source license tracking.

Answer: D

Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
* Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
* Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
* Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
* Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner References:
* Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

NEW QUESTION # 103
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?

A. Amazon Transcribe
B. Amazon Translate
C. Amazon Lex
D. Amazon Comprehend

Answer: D

Explanation:
Amazon Comprehend is the correct service to analyze customer support interactions and identify frequently asked questions and insights.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Capable of extracting key phrases, detecting entities, analyzing sentiment, and identifying topics from text data, making it ideal for analyzing customer support interactions.
* Why Option B is Correct:
* Text Analysis Capabilities: Can process large volumes of text to identify common topics, phrases, and sentiment, providing valuable insights.
* Suitable for Customer Support Analysis: Specifically designed to understand the content and meaning of text, which is key for identifying frequently asked questions.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces, not for text analysis.
* C. Amazon Transcribe: Converts speech to text but does not perform text analysis.
* D. Amazon Translate: Used for translating text between languages, not for analyzing content.

NEW QUESTION # 104
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?

A. Real-time inference
B. Serverless inference
C. Asynchronous inference
D. Batch transform

Answer: A

Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

NEW QUESTION # 105
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