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Francisco Escobar
Francisco Escobar

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AWS Certified AI Practitioner (AIF-C01) Study Guide

#aws #ai #certification #learning

Recommended Prerequisites:

  • Familiarity with key AWS services like Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker
  • Understanding of the AWS shared responsibility model
  • Familiarity with AWS Identity and Access Management (IAM) for security
  • Knowledge of AWS global infrastructure (Regions, Availability Zones)
  • Familiarity with AWS service pricing models

Exam Details

  • Code: AIF-C01
  • Duration: 90 minutes
  • Cost: $100 USD
  • Number of Questions: 65 total questions (50 scored, 15 unscored for future evaluation)
  • Scoring: Scale of 100-1000, minimum passing score: 700
  • Result: Pass/Fail

Question Types

  • Multiple choice: One correct answer and three incorrect distractors
  • Multiple response: Two or more correct answers from five or more options
  • Ordering: Place 3-5 answers in the correct order to complete a task
  • Matching: Match answers to a list of 3-7 requests
  • Case study: A scenario with two or more related questions

Exam Domains

Domain 1: AI and ML Fundamentals (20% of exam)

1.1 Explain basic AI concepts and terminology

Key Definitions:

  • Artificial Intelligence (AI): A field of computer science focused on creating systems capable of performing tasks that mimic human intelligence
  • Machine Learning (ML): A subset of AI that teaches computers to learn from data to improve performance without explicit programming
  • Deep Learning: A subset of ML that uses artificial neural networks (ANNs) with multiple layers
  • Generative AI: A subset of deep learning that produces new data (text, images, audio, synthetic data)
  • Large Language Model (LLM): Deep learning models trained on massive volumes of text data

Types of Inference:

  • Batch inference: Processing large volumes of data at once, prioritizing efficiency over speed
  • Real-time inference: Fast processing for applications requiring immediate responses

Data Types:

  • Labeled and unlabeled: With or without assigned categories
  • Structured: Tabular data
  • Semi-structured: With metadata like JSON or XML
  • Unstructured: Without defined model (text, images, videos)

Machine Learning Paradigms:

  • Supervised Learning: Model trained with labeled data (classification and regression)
  • Unsupervised Learning: Finds patterns in unlabeled data (clustering, dimensionality reduction)
  • Reinforcement Learning: Agent learns through trial and error with rewards/penalties

1.2 Identify practical AI use cases

When to use AI/ML:

  • Assist human decision-making
  • Scale solutions
  • Automate repetitive tasks

When NOT to use AI/ML:

  • Unfavorable cost-benefit analysis
  • Deterministic solutions required
  • Highly regulated areas requiring strict explainability

AWS Managed AI/ML Services:

  • Amazon SageMaker: Comprehensive platform for building, training, and deploying ML models
  • Amazon Transcribe: Speech-to-text (ASR)
  • Amazon Translate: Neural machine translation
  • Amazon Comprehend: NLP for extracting insights from text
  • Amazon Lex: Conversational interfaces (chatbots)
  • Amazon Polly: Text-to-speech (TTS)

1.3 Describe the ML development lifecycle

ML Pipeline Components:

  1. Data collection
  2. Exploratory data analysis (EDA)
  3. Data preprocessing
  4. Feature engineering
  5. Model training
  6. Hyperparameter tuning
  7. Model evaluation
  8. Deployment
  9. Monitoring

Deployment Methods:

  • Managed API service: Amazon SageMaker endpoints
  • Self-hosted API: Deploy on own servers (Amazon EC2)

AWS Services by Stage:

  • Data preparation: Amazon SageMaker Data Wrangler, Feature Store
  • Build and train: Amazon SageMaker Notebooks, Model Training
  • Deploy and monitor: Amazon SageMaker Pipelines, Model Monitor

Domain 2: Generative AI Fundamentals (24% of exam)

2.1 Explain basic generative AI concepts

Fundamental Concepts:

  • Tokens: Smallest units of text a model can process
  • Embeddings: Numerical representations that capture semantic meaning
  • Prompt Engineering: Designing inputs to guide the model
  • Foundation Models: Large-scale ML models pre-trained on massive datasets
  • Multimodal Models: Process multiple data types (text, images, audio)
  • Diffusion Models: Create realistic data by reversing a noise-adding process

Use Cases:

  • Image, video, and audio generation
  • Text summarization
  • Chatbots
  • Translation
  • Code generation
  • Search and recommendation engines

2.2 Understand capabilities and limitations of generative AI

Advantages:

  • Adaptability to diverse tasks
  • Real-time response capability
  • Simplicity in automating content generation

Disadvantages and Challenges:

  • Hallucinations: Incorrect information that appears plausible
  • Interpretability: "Black box" models
  • Inaccuracy and lack of determinism
  • Toxicity: Potential for offensive content

2.3 Describe AWS infrastructure and technologies for generative AI

AWS Services:

  • Amazon Bedrock: Access to foundation models through API
  • Amazon SageMaker JumpStart: ML hub with pre-trained models
  • Amazon Q: Generative AI assistant for businesses
  • PartyRock: Amazon Bedrock playground

Domain 3: Foundation Model Applications (28% of exam)

3.1 Design considerations for foundation model applications

Model Selection Criteria:

  • Cost: Subscription prices, compute resources
  • Modality: Supported data types
  • Latency: Response speed
  • Model size and complexity
  • Input/output length

Inference Parameters:

  • Temperature: Controls randomness (high = creative, low = deterministic)

Retrieval-Augmented Generation (RAG):

  • Optimizes LLM output by referencing external knowledge base
  • Combines information retrieval with text generation
  • Knowledge Bases for Amazon Bedrock is a RAG implementation

Vector Databases:

  • Store embeddings for fast similarity searches
  • AWS services: OpenSearch Service, Aurora, Neptune, DocumentDB, RDS PostgreSQL

Foundation Model Customization:

  • Pre-training: Extremely expensive, from scratch
  • Fine-tuning: Adapt pre-trained model, less expensive
  • In-context learning: Guide with examples in prompt, cost-effective
  • RAG: Balanced approach, updates knowledge without retraining

3.2 Choose effective prompt engineering techniques

Prompting Techniques:

  • Zero-shot: No prior examples
  • One-shot / Few-shot: With one or several examples
  • Chain-of-thought: Step-by-step reasoning

Risks:

  • Prompt injection: Malicious prompt manipulation
  • Jailbreak: Bypassing security filters
  • Poisoning: Malicious data degrading performance

3.3 Describe foundation model training and fine-tuning process

Fine-tuning Methods:

  • Instruction tuning: With instruction-response examples
  • Domain-specific adaptation: Data from particular field
  • RLHF (Reinforcement Learning from Human Feedback): Align with human preferences

3.4 Describe methods for evaluating foundation model performance

Evaluation Approaches:

  • Human evaluation: Gold standard but expensive
  • Benchmark datasets: GLUE, SuperGLUE

Metrics:

  • ROUGE: For text summarization
  • BLEU: For machine translation
  • BERTScore: Semantic similarity with BERT embeddings

Domain 4: Guidelines for Responsible AI (14% of exam)

4.1 Explain responsible AI system development

Responsible AI Characteristics:

  • Fairness
  • Inclusivity
  • Robustness
  • Security
  • Veracity
  • Transparency
  • Governance

Bias and Fairness:

  • Types: algorithmic, data, sampling, prejudice bias
  • Effects: unfair results, overfitting/underfitting

AWS Tools:

  • Amazon SageMaker Clarify: Detects bias and explains predictions
  • Amazon SageMaker Model Monitor: Monitors models in production
  • Amazon Augmented AI (A2I): Human reviews
  • Guardrails for Amazon Bedrock: Security policies for generative AI

4.2 Recognize the importance of transparent and explainable models

Transparency vs. "Black Box" Models:

  • Transparent: easy to interpret (decision trees)
  • Opaque: difficult to understand (deep neural networks)

Tools:

  • Amazon SageMaker Model Cards: Document model information
  • AWS AI Service Cards: Information about responsible use

Domain 5: Security, Compliance, and Governance for AI Solutions (14% of exam)

5.1 Explain methods to secure AI systems

AWS Security Services:

  • AWS IAM: Access management with principle of least privilege
  • Encryption: At rest and in transit with AWS KMS
  • Amazon Macie: Discovers and protects sensitive data (PII)
  • AWS PrivateLink: Private connectivity between VPCs
  • Shared responsibility model: AWS secures the cloud, customer secures in the cloud

Data Citation and Lineage:

  • Data lineage: Track origin and transformations
  • Data cataloging: AWS Glue Data Catalog for organizing metadata

5.2 Recognize governance and compliance regulations for AI systems

Standards:

  • ISO: ISO/IEC 27001 (information security), ISO/IEC 42001 (AI management)
  • SOC: Service Organization Controls

AWS Compliance Services:

  • AWS Artifact: Access to compliance reports
  • AWS Config: Evaluates and monitors configurations
  • AWS Audit Manager: Helps with continuous audits
  • AWS CloudTrail: Logs account activity and API calls
  • AWS Trusted Advisor: Optimization recommendations

Governance Strategies:

  • Data lifecycle policies
  • Logging
  • Data residency
  • Monitoring
  • Retention

Exam Tips

  1. Practice with real use cases for each mentioned AWS service
  2. Understand the differences between machine learning types
  3. Get familiar with evaluation metrics and when to use each
  4. Study ethical aspects and responsible AI
  5. Know AWS security and compliance features
  6. Practice prompt engineering and understand RAG
  7. Review pricing models and model selection factors

Good luck with your certification!

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