ExamPrep AIF-C01 AWS AI

The exam has the following content domains and weightings::

• Domain 1: Fundamentals of AI and ML (20% of scored content)
• Domain 2: Fundamentals of Generative AI (24% of scored content)
• Domain 3: Applications of Foundation Models (28% of scored content)
• Domain 4: Guidelines for Responsible AI (14% of scored content)
• Domain 5: Security, Compliance, and Governance for AI Solutions (14% of scored content)

What You'll Learn

01

Domain 1: Fundamentals of AI and ML

• Task Statement 1.1: Explain basic AI concepts and terminologies
  • Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language model [LLM]).
  • Describe the similarities and differences between AI, ML, and deep learning.
  • Describe various types of inferencing (for example, batch, real-time).
  • Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).
  • Describe supervised learning, unsupervised learning, and reinforcement learning.




• Task Statement 1.2: Identify practical use cases for AI.
  • Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).
  • Determine when AI/ML solutions are not appropriate (for example, costbenefit analyses, situations when a specific outcome is needed instead of a prediction).
  • Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering).
  • Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting).
  • Explain the capabilities of AWS managed AI/ML services (for example, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).




• Task Statement 1.3: Describe the ML development lifecycle.
  • Describe components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring).
  • Understand sources
  of ML models (for example, open source pre-trained models, training custom models).
  • Describe methods to use a model in production (for example, managed API service, self-hosted API).
  • Identify relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, Amazon SageMaker Model Monitor).
  • Understand fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training).
  • Understand model performance metrics (for example, accuracy, Area Under the ROC Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.

02

Domain 2: Fundamentals of Generative AI

• Task Statement 2.1: Explain the basic concepts of generative AI.
  • Understand foundational generative AI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, multi-modal models, diffusion models).
  • Identify potential use cases for generative AI models (for example, image, video, and audio generation; summarization; chatbots; translation; code generation; customer service agents; search; recommendation engines).
  • Describe the foundation model lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).




• Task Statement 2.2: Understand the capabilities and limitations of generative AI for solving business problems.
  • Describe the advantages of generative AI (for example, adaptability, responsiveness, simplicity).
  • Identify disadvantages of generative AI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism).
  • Understand various factors to select appropriate generative AI models (for example, model types, performance requirements, capabilities, constraints, compliance).
  • Determine business value and metrics for generative AI applications (for example, cross-domain performance, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value).




• Task Statement 2.3: Describe AWS infrastructure and technologies for building generative AI applications.
  • Identify AWS services and features to develop generative AI applications (for example, Amazon SageMaker JumpStart; Amazon Bedrock; PartyRock, an Amazon Bedrock Playground; Amazon Q).
  • Describe the advantages of using AWS generative AI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives).
  • Understand the benefits of AWS infrastructure for generative AI applications (for example, security, compliance, responsibility, safety).
  • Understand cost tradeoffs of AWS generative AI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models).

03

Domain 3: Applications of Foundation Models

• Task Statement 3.1: Describe design considerations for applications that use foundation models.
  • Identify selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length).
  • Understand the effect of inference parameters on model responses (for example, temperature, input/output length).
  • Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock, knowledge base).
  • Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB [with MongoDB compatibility], Amazon RDS for PostgreSQL).
  • Explain the cost tradeoffs of various approaches to foundation model customization (for example, pre-training, fine-tuning, in-context learning, RAG).
  • Understand the role of agents in multi-step tasks (for example, Agents for Amazon Bedrock).




• Task Statement 3.2: Choose effective prompt engineering techniques.
  • Describe the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts, model latent space).
  • Understand techniques for prompt engineering (for example, chain-ofthought, zero-shot, single-shot, few-shot, prompt templates).
  • Understand the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments).
  • Define potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking).




• Task Statement 3.3: Describe the training and fine-tuning process for foundation models.
  • Describe the key elements of training a foundation model (for example, pre-training, fine-tuning, continuous pre-training).
  • Define methods for fine-tuning a foundation model (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training).
  • Describe how to prepare data to fine-tune a foundation model (for example, data curation, governance, size, labeling, representativeness, reinforcement learning from human feedback [RLHF]).




• Task Statement 3.4: Describe methods to evaluate foundation model performance.
  • Understand approaches to evaluate foundation model performance (for example, human evaluation, benchmark datasets).
  • Identify relevant metrics to assess foundation model performance (for example, Recall-Oriented Understudy for Gisting Evaluation [ROUGE], Bilingual Evaluation Understudy [BLEU], BERTScore).
  • Determine whether a foundation model effectively meets business objectives (for example, productivity, user engagement, task engineering).

04

Domain 4: Guidelines for Responsible AI

• Task Statement 4.1: Explain the development of AI systems that are responsible.
  • Identify features of responsible AI (for example, bias, fairness, inclusivity, robustness, safety, veracity).
  • Understand how to use tools to identify features of responsible AI (for example, Guardrails for Amazon Bedrock).
  • Understand responsible practices to select a model (for example, environmental considerations, sustainability).
  • Identify legal risks of working with generative AI (for example, intellectual property infringement claims, biased model outputs, loss of customer trust, end user risk, hallucinations).
  • Identify characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets).
  • Understand effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting).
  • Describe tools to detect and monitor bias, trustworthiness, and truthfulness (for example, analyzing label quality, human audits, subgroup analysis, Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon Augmented AI [Amazon A2I]).




• Task Statement 4.2: Recognize the importance of transparent and explainable models.
  • Understand the differences between models that are transparent and explainable and models that are not transparent and explainable.
  • Understand the tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, open source models, data, licensing).
  • Identify tradeoffs between model safety and transparency (for example, measure interpretability and performance).
  • Understand principles of human-centered design for explainable AI.

05

Domain 5: Security, Compliance, and Governance for AI Solutions

• Task Statement 5.1: Explain methods to secure AI systems.
  • Identify AWS services and features to secure AI systems (for example, IAM roles, policies, and permissions; encryption; Amazon Macie; AWS PrivateLink; AWS shared responsibility model).
  • Understand the concept of source citation and documenting data origins (for example, data lineage, data cataloging, SageMaker Model Cards).
  • Describe best practices for secure data engineering (for example, assessing data quality, implementing privacy-enhancing technologies, data access control, data integrity).
  • Understand security and privacy considerations for AI systems (for example, application security, threat detection, vulnerability management, infrastructure protection, prompt injection, encryption at rest and in transit).




• Task Statement 5.2: Recognize governance and compliance regulations for AI systems.
  • Identify regulatory compliance standards for AI systems (for example, International Organization for Standardization [ISO], System and Organization Controls [SOC], algorithm accountability laws).
  • Identify AWS services and features to assist with governance and regulation compliance (for example, AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor).
  • Describe data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention).
  • Describe processes to follow governance protocols (for example, policies, review cadence, review strategies, governance frameworks such as the Generative AI Security Scoping Matrix, transparency standards, team training requirements).