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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Responsible AI20%- Understand governance and compliance requirements
  • 1. Explain data privacy regulations affecting AI
  • 2. Recognize regulatory and ethical considerations
  • 3. Describe model interpretability and explainability
- Explain the principles of responsible AI
  • 1. Identify potential biases in AI/ML models
  • 2. Define fairness, transparency, and privacy in AI systems
  • 3. Describe strategies for bias mitigation
- Implement responsible AI best practices
  • 1. Evaluate AI outputs for quality and safety
  • 2. Implement appropriate guardrails for AI applications
  • 3. Apply human oversight in AI decision-making
Topic 2: AI Application Development30%- Evaluate and optimize AI applications
  • 1. Implement caching and cost optimization techniques
  • 2. Identify performance bottlenecks and optimization strategies
  • 3. Apply evaluation frameworks for AI applications
  • 4. Assess application outputs for relevance and accuracy
- Understand foundational concepts for building AI applications
  • 1. Explain prompts, tokens, and context windows
  • 2. Identify retrieval-augmented generation (RAG) concepts
  • 3. Explain function calling and tool use in AI applications
  • 4. Describe prompt engineering techniques
- Implement AI applications using AWS services
  • 1. Configure model parameters (temperature, top-p, top-k)
  • 2. Implement prompt templates and chain-of-thought reasoning
  • 3. Build applications using Amazon Bedrock APIs and SDKs
  • 4. Use knowledge bases for context-aware responses
Topic 3: AI/ML Fundamentals24%- Identify foundational terminology and definitions
  • 1. Explain training, inference, and fine-tuning
  • 2. Describe underfitting and overfitting
  • 3. Define common ML metrics (accuracy, precision, recall, F1 score)
  • 4. Define models, algorithms, and parameters
- Explain the fundamental concepts of AI and ML
  • 1. Define generative AI and its key concepts
  • 2. Identify common use cases for AI/ML
  • 3. Explain supervised and unsupervised learning
  • 4. Differentiate between AI, ML, and deep learning
- Understand the AI/ML lifecycle
  • 1. Recognize data collection, preprocessing, and feature engineering stages
  • 2. Identify phases of the ML lifecycle
  • 3. Describe model evaluation and deployment considerations
Topic 4: AWS AI Services Overview26%- Identify appropriate AWS AI services for given scenarios
  • 1. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings
  • 2. Compare AWS AI services by capability and use case
  • 3. Determine when to use pre-trained models vs. custom models
- Describe AWS AI services for specific use cases
  • 1. Describe Amazon Polly, Rekognition, Transcribe, and Translate
  • 2. Identify services for NLP, computer vision, and recommendations
  • 3. Explain AWS HealthScribe and other domain-specific services
- Describe Amazon SageMaker capabilities
  • 1. Describe built-in algorithms and SageMaker JumpStart
  • 2. Recognize MLOps capabilities in SageMaker
  • 3. Identify SageMaker features for ML workflows
  • 4. Explain model training and deployment options
- Describe Amazon Bedrock capabilities
  • 1. Identify Bedrock features (agents, knowledge bases, guardrails)
  • 2. Explain security, privacy, and compliance features
  • 3. Describe model invocation, prompts, and responses
  • 4. Explain foundation models (FMs) available in Bedrock

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Amazon AWS Certified AI Practitioner Sample Questions (Q356-Q361):

NEW QUESTION # 356
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?

Answer: B


NEW QUESTION # 357
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning

Answer:

Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning


NEW QUESTION # 358
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?

Answer: A

Explanation:
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide) A (JumpStart) provides prebuilt solutions and models, not monitoring.
B (HyperPod) is for large-scale training, not model monitoring.
C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.


NEW QUESTION # 359
An AI practitioner is using Amazon Bedrock Prompt Management to create a reusable prompt. The prompt must be able to interact with external services by calling an external API. Which solution will meet this requirement?

Answer: D

Explanation:
The correct answer is B because Amazon Bedrock Prompt Management supports tool use via tools configuration, which enables a prompt to define tools that can invoke external APIs or services.
According to the AWS documentation:
"You can use the tools configuration in Amazon Bedrock to specify external APIs that a foundation model can call during inference. This enables the model to interact with external services, such as invoking functions, retrieving real-time data, or executing workflows." The tools configuration allows a prompt to describe which external functions (APIs) are available, their parameters, and how they should be invoked, similar to OpenAI's function calling or tool use pattern.


NEW QUESTION # 360
A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?

Answer: A

Explanation:
The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language. AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Transcribe:
"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe) Detailed Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.
Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.
Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.
Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.
Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) Amazon Polly Developer Guide: Text-to-Speech Overview (https://docs.aws.amazon.com/polly/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Speech Recognition and Synthesis


NEW QUESTION # 361
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