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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q277-Q282):

NEW QUESTION # 277
A company stores millions of PDF documents in an Amazon S3 bucket. The company needs to extract the text from the PDFs, generate summaries of the text, and index the summaries for fast searching.
Which combination of AWS services will meet these requirements? (Select TWO.)

Answer: B,D

Explanation:
* Amazon Textract (E) automatically extracts text and structured data from scanned documents, such as PDFs.
* Amazon Bedrock (B) offers access to LLMs (such as Amazon Titan or Anthropic Claude) for tasks like summarization and generating embeddings for search.
Workflow:
* Amazon Textract extracts text from PDFs in S3.
* Amazon Bedrock LLMs summarize the extracted text.
* (Optional: Summaries can be indexed using Amazon OpenSearch or another search solution.)
* A (Translate) is for language translation, not extraction or summarization.
* C (Transcribe) is for audio to text, not PDFs.
* D (Polly) is for text-to-speech.
"Amazon Textract extracts text, forms, and tables from scanned documents... Bedrock provides generative AI models to perform summarization and other text generation tasks." (Reference: Amazon Textract, Amazon Bedrock, AWS GenAI RAG Reference)


NEW QUESTION # 278
Which type of AI model makes numeric predictions?

Answer: A

Explanation:
The regression model is a fundamental type of supervised machine learning algorithm that is specifically designed to make numeric predictions. In regression tasks, the goal is to predict a continuous numerical value based on input features. This contrasts with classification, which predicts discrete labels.
According to AWS documentation:
"Regression models are used for predicting a continuous value. Examples include predicting house prices, stock market prices, or customer credit limits." (Reference: AWS Machine Learning Foundations: Regression, AWS AI Practitioner Study Guide)
"Regression models are used for predicting a continuous value. Examples include predicting house prices, stock market prices, or customer credit limits." (Reference: AWS Machine Learning Foundations: Regression, AWS AI Practitioner Study Guide) Option A (Diffusion) relates to generative models and is not primarily used for numeric prediction.
Option C (Transformer) is a neural network architecture, often used for sequence modeling tasks (e.g., NLP).
Option D (Multi-modal) describes a model handling multiple data types, not specifically numeric prediction.
Reference:
AWS AI/ML Learning Path - Regression Models
AWS Certified AI Practitioner Study Guide (Pearson)


NEW QUESTION # 279
An AI practitioner has trained a model on a training dataset. The model performs well on the training dat a. However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?

Answer: A

Explanation:
Comprehensive and Detailed
When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.
Overfitting: The model memorizes the training data patterns instead of generalizing.
Underfitting (A) means the model performs poorly on both training and test data.
Bias (C) refers to systemic errors in predictions, not this training/test mismatch.
Prompt engineering (B) applies to generative AI, not general ML training models.
Reference:
AWS ML Glossary - Overfitting and Underfitting


NEW QUESTION # 280
A company wants to learn about generative AI applications in an experimental environment.
Which solution will meet this requirement MOST cost-effectively?

Answer: C

Explanation:
The correct answer is Amazon Bedrock PartyRock, a playground for building and experimenting with generative AI apps in a low-cost, no-code environment. PartyRock is designed for innovation and learning. It enables users to try out prompts, LLM apps, and templates using Amazon Bedrock under a free-tier friendly setup. According to AWS, PartyRock abstracts infrastructure and allows rapid prototyping using models from Bedrock providers. This makes it ideal for early experimentation, especially for non-developers or those not ready to invest in full production pipelines. In contrast, Amazon Q Developer is for software engineering tasks, SageMaker JumpStart focuses on deploying ML models, and Q Business targets enterprise knowledge workers. None of those are as cost-effective and experimental-focused as PartyRock.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation - PartyRock Overview
AWS Generative AI Learning Path - Getting Started Tools


NEW QUESTION # 281
What does inference refer to in the context of AI?

Answer: B

Explanation:
* Inference = applying a trained ML model to new, unseen data to make predictions, classifications, or generate outputs.
* A is algorithm research, C refers to ensemble learning, D is data collection.
# Reference:
AWS ML Glossary - Inference


NEW QUESTION # 282
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