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Amazon AIF-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Responsible AI	20%	- Understand governance and compliance requirements 1. Explain data privacy regulations affecting AI 2. Describe model interpretability and explainability 3. Recognize regulatory and ethical considerations - Implement responsible AI best practices 1. Implement appropriate guardrails for AI applications 2. Apply human oversight in AI decision-making 3. Evaluate AI outputs for quality and safety - Explain the principles of responsible AI 1. Define fairness, transparency, and privacy in AI systems 2. Identify potential biases in AI/ML models 3. Describe strategies for bias mitigation
AWS AI Services Overview	26%	- Describe Amazon Bedrock capabilities 1. Explain security, privacy, and compliance features 2. Describe model invocation, prompts, and responses 3. Explain foundation models (FMs) available in Bedrock 4. Identify Bedrock features (agents, knowledge bases, guardrails) - Identify appropriate AWS AI services for given scenarios 1. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings 2. Determine when to use pre-trained models vs. custom models 3. Compare AWS AI services by capability and use case - Describe AWS AI services for specific use cases 1. Describe Amazon Polly, Rekognition, Transcribe, and Translate 2. Explain AWS HealthScribe and other domain-specific services 3. Identify services for NLP, computer vision, and recommendations - Describe Amazon SageMaker capabilities 1. Identify SageMaker features for ML workflows 2. Describe built-in algorithms and SageMaker JumpStart 3. Explain model training and deployment options 4. Recognize MLOps capabilities in SageMaker
AI Application Development	30%	- Implement AI applications using AWS services 1. Configure model parameters (temperature, top-p, top-k) 2. Use knowledge bases for context-aware responses 3. Implement prompt templates and chain-of-thought reasoning 4. Build applications using Amazon Bedrock APIs and SDKs - Understand foundational concepts for building AI applications 1. Explain function calling and tool use in AI applications 2. Describe prompt engineering techniques 3. Identify retrieval-augmented generation (RAG) concepts 4. Explain prompts, tokens, and context windows - Evaluate and optimize AI applications 1. Apply evaluation frameworks for AI applications 2. Identify performance bottlenecks and optimization strategies 3. Implement caching and cost optimization techniques 4. Assess application outputs for relevance and accuracy
AI/ML Fundamentals	24%	- Understand the AI/ML lifecycle 1. Identify phases of the ML lifecycle 2. Recognize data collection, preprocessing, and feature engineering stages 3. Describe model evaluation and deployment considerations - Identify foundational terminology and definitions 1. Explain training, inference, and fine-tuning 2. Describe underfitting and overfitting 3. Define models, algorithms, and parameters 4. Define common ML metrics (accuracy, precision, recall, F1 score) - Explain the fundamental concepts of AI and ML 1. Define generative AI and its key concepts 2. Differentiate between AI, ML, and deep learning 3. Explain supervised and unsupervised learning 4. Identify common use cases for AI/ML

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Amazon AWS Certified AI Practitioner Sample Questions (Q404-Q409):

NEW QUESTION # 404
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters

Answer:

Explanation:

Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Configuring Guardrails (https://aws.amazon.com/bedrock/)

NEW QUESTION # 405
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network.
Which solution will meet these requirements?

A. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.
B. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.
C. Use AWS Key Management Service (AWS KMS) keys to encrypt the data.
D. Use AWS PrivateLink to connect the VPC and Amazon Bedrock.

Answer: D

Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking) Detailed Explanation:
* Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.
* Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
* Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
* Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
References:
AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide
/security.html)
AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/) AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services

NEW QUESTION # 406
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions. The company wants to measure the direct impact of the AI assistant on sales performance.

A. The number of customer interactions with the AI assistant
B. Sentiment analysis scores from customer feedback after AI assistant interactions
C. The conversion rate of customers who purchase products after AI assistant interactions
D. Natural language understanding accuracy rates

Answer: C

Explanation:
* The most direct business KPI for sales performance is conversion rate (percentage of users who purchase after AI assistant interaction).
* Number of interactions (B) shows engagement, not sales impact.
* Sentiment analysis (C) shows customer satisfaction but not revenue impact.
* NLU accuracy (D) is a technical metric, not a business outcome.
# Reference:
AWS Generative AI Use Cases - Measuring Business Value

NEW QUESTION # 407
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?

A. The Top P value is too high.
B. The selected model does not support fine-tuning.
C. The temperature is set too high.
D. The input tokens exceed the model's context size.

Answer: D

Explanation:
* Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
* If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
* Temperature (A) and Top P (C) control randomness, not input size.
* Fine-tuning (B) is irrelevant to input truncation failures.
# Reference:
AWS Documentation - Amazon Bedrock Model Parameters (context size limits)

NEW QUESTION # 408
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

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

Answer: A

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 # 409
......

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