AIF-C01 — AWS Certified AI Practitioner Exam Blueprint

Official domain weights

The AWS AIF-C01 exam guide assigns 20% to AI/ML fundamentals, 24% to GenAI fundamentals, 28% to foundation-model applications, 14% to responsible AI and 14% to security, compliance and governance. These are shares of scored content; they do not prescribe the number of questions in each IT Mastery topic.

The candidate uses AI/ML technologies but is not expected to implement models, pipelines or security protocols. Our code-like traces, configuration evidence and diagrams test conceptual interpretation and business decisions; they are not coding tasks or a claim that AWS uses the same exhibits.

How to Use This Exam Blueprint

Use this checklist as a practical study map for the AWS Certified AI Practitioner (AIF-C01) exam. It is organized around readiness areas rather than exact exam weights. For each area, confirm that you can recognize concepts, choose appropriate AWS services, interpret scenario cues, and avoid common distractors.

A strong candidate should be able to:

• Explain core AI, machine learning, and generative AI concepts in plain language.
• Match AWS AI/ML services to business use cases.
• Identify when to use managed AI services, foundation models, retrieval-augmented generation, or custom model training.
• Apply responsible AI, security, privacy, governance, and cost-aware thinking.
• Interpret scenario wording and select the safest, simplest, most appropriate AWS-native approach.

Use this page for final review after reading the AWS exam guide and doing hands-on practice. It is independent study support and is not affiliated with AWS.

Topic-Area Readiness Map

Core AI and Machine Learning Fundamentals

Concepts to Know

Can You Do This?

• Explain the difference between AI, ML, deep learning, and generative AI.
• Identify whether a scenario requires classification, regression, clustering, forecasting, recommendation, NLP, or computer vision.
• Distinguish model training from model inference.
• Recognize why training data quality affects model quality.
• Explain overfitting as performing well on training data but poorly on new data.
• Explain underfitting as failing to capture meaningful patterns.
• Identify data leakage, where information from the answer or future leaks into training.
• Recognize model drift, where real-world data changes after deployment.
• Explain why models need monitoring after deployment.

ML Task Selection Checklist

Generative AI Foundations

Concepts to Review

Prompt Engineering Readiness

You should be able to build and diagnose prompts that include:

• Clear role or task instruction.
• Relevant context.
• Specific constraints.
• Desired output format.
• Examples, when useful.
• Instructions for handling uncertainty.
• Boundaries for what the model should not do.
• A way to reduce ambiguity in the expected answer.

Example prompt structure:

Task: Summarize the customer case notes.
Context: Use only the notes provided below.
Constraints: Do not include personal data in the summary.
Output format: Three bullet points and one recommended next action.
If unsure: Say what information is missing.

Generative AI Scenario Cues

Amazon Bedrock Readiness

For AIF-C01, be ready to recognize Amazon Bedrock as an AWS-managed way to build and scale generative AI applications using foundation models.

Bedrock Topics to Check

Bedrock Decision Prompts

Ask yourself:

• Is the scenario asking for a generative AI application without managing ML infrastructure?
• Does the application need a foundation model rather than a traditional classifier or regressor?
• Does the model need access to private documents or current enterprise knowledge?
• Are hallucination, unsafe content, or sensitive output risks mentioned?
• Is there a need for a chatbot, summarizer, document assistant, code assistant, or agentic workflow?
• Would prompt engineering, RAG, or guardrails solve the problem before custom training?

Amazon SageMaker Readiness

Recognize Amazon SageMaker when the scenario involves building, training, tuning, deploying, or managing custom ML models.

Bedrock vs SageMaker vs Managed AI Services

AWS AI Service Selection Checklist

Purpose-Built AI Services

Service Selection Questions

• Is there a managed AI service that directly solves the problem?
• Is the task generative, predictive, extractive, conversational, or analytical?
• Does the company need to train a model, or only consume AI capability?
• Is operational overhead a key requirement?
• Are security, privacy, and data residency concerns part of the scenario?
• Is low-code/no-code or minimal ML expertise mentioned?
• Does the wording emphasize “custom,” “data scientists,” or “training pipeline”?

Data Readiness and Data Quality

AI systems are only as reliable as the data and context they use. Be prepared for scenario questions where the best answer is a data-quality, governance, or lifecycle action rather than a model change.

Data Pipeline and Storage Checks

• Know that Amazon S3 is commonly used as durable object storage for data lakes, training data, and AI artifacts.
• Recognize data preparation steps: ingestion, cleaning, transformation, labeling, splitting, and validation.
• Understand why training, validation, and test datasets should be separated.
• Know that metadata, lineage, and governance help teams understand where data came from and how it is used.
• Recognize when streaming data, batch data, or document repositories change the architecture pattern.
• Understand that private or regulated data requires access control, encryption, monitoring, and retention planning.

Model Evaluation and Metrics

You do not need to become a data scientist for this exam, but you should understand what common metrics mean and when they matter.

Classification Metrics

Key formulas:

Metric Scenario Cues

Responsible AI and Safety Checklist

Responsible AI is not a separate afterthought. It should influence data, model design, deployment, monitoring, and user experience.

Responsible AI “Can You Do This?” Checklist

• Identify bias risk in training data, prompts, retrieval sources, and model outputs.
• Explain why representative data matters.
• Recognize when human review is required for high-impact decisions.
• Identify hallucination risk in generative AI responses.
• Select grounding, guardrails, content moderation, and citations as mitigation patterns.
• Explain why explainability matters for regulated, financial, healthcare, or employment-related decisions.
• Recognize that security controls and responsible AI controls are complementary.
• Identify why monitoring should include quality, fairness, safety, and user feedback.

Security, Privacy, and Governance Readiness

For AWS AI workloads, apply standard cloud security principles: least privilege, encryption, network control, logging, monitoring, and governance.

AI-Specific Security Checks

• Do not put sensitive data into prompts unless the architecture and controls allow it.
• Restrict who can access AI services, models, datasets, embeddings, logs, and generated outputs.
• Protect vector stores and knowledge bases because they may contain sensitive semantic representations.
• Log and monitor AI application activity, but avoid creating logs that expose secrets or regulated data.
• Use least privilege for agents or tool-calling workflows.
• Validate actions before an AI agent performs changes in external systems.
• Consider prompt injection risk when users or retrieved documents can influence model behavior.
• Apply content filtering, input validation, and output validation where appropriate.
• Include incident response and rollback planning for AI-enabled applications.

RAG, Embeddings, and Knowledge Base Readiness

Retrieval-augmented generation is a frequent practical pattern for enterprise generative AI.

RAG Workflow

    flowchart LR
	    A[User question] --> B[Create query embedding]
	    B --> C[Search vector store or knowledge base]
	    C --> D[Retrieve relevant chunks]
	    D --> E[Send prompt plus retrieved context to model]
	    E --> F[Generate grounded response]
	    F --> G[Apply guardrails and validation]
	    G --> H[Return answer with optional citations]

RAG Checklist

• Explain why embeddings enable semantic similarity search.
• Explain why documents are often split into chunks before indexing.
• Know that chunk size can affect retrieval quality.
• Recognize that RAG can improve relevance but does not guarantee correctness.
• Identify when citations or source references improve trust.
• Understand that retrieved context must be access-controlled.
• Recognize prompt injection risk from retrieved documents.
• Know that stale source data can lead to stale answers.
• Distinguish RAG from fine-tuning: RAG retrieves knowledge at response time; fine-tuning changes model behavior through additional training.
• Distinguish RAG from simple keyword search: embeddings support meaning-based retrieval.

RAG Decision Table

Prompt Injection and Guardrail Readiness

Generative AI scenarios may test whether you recognize unsafe user input or malicious retrieved content.

Operations, Monitoring, and Cost Awareness

Operational Readiness Table

Cost and Tradeoff Prompts

• Could a managed AI service solve the use case more simply than custom training?
• Is a smaller or more specialized model sufficient for the task?
• Can prompt quality reduce the need for more expensive model customization?
• Does the workload need real-time inference, or would batch processing work?
• Are unused datasets, endpoints, logs, or artifacts being retained unnecessarily?
• Is monitoring in place to identify usage spikes and quality issues?
• Does the selected architecture reduce operational overhead where requested?

Scenario Decision Checks

Service Choice Decision Path

    flowchart TD
	    A[Start with the business problem] --> B{Is the task generative?}
	    B -->|Yes| C{Need private or current knowledge?}
	    C -->|Yes| D[Consider Amazon Bedrock with RAG / Knowledge Bases]
	    C -->|No| E[Consider Amazon Bedrock prompt-based application]
	    B -->|No| F{Is there a purpose-built AWS AI service?}
	    F -->|Yes| G[Use managed AI service]
	    F -->|No| H{Need custom model training?}
	    H -->|Yes| I[Consider Amazon SageMaker]
	    H -->|No| J[Re-check requirements and simplest managed option]

Judgment Questions to Practice

Common Weak Areas and Traps

Final-Week Review Checklist

Concept Review

• I can explain AI, ML, deep learning, and generative AI differences.
• I can identify supervised, unsupervised, and reinforcement learning scenarios.
• I can match classification, regression, clustering, anomaly detection, recommendation, forecasting, NLP, and computer vision to business problems.
• I can explain training, validation, testing, deployment, inference, monitoring, and retraining.
• I can describe overfitting, underfitting, drift, leakage, and class imbalance.

AWS Service Review

• I can identify when Amazon Bedrock is the best fit.
• I can identify when Amazon SageMaker is the best fit.
• I can match Amazon Comprehend, Rekognition, Textract, Transcribe, Translate, Polly, Lex, Kendra, Personalize, and Forecast to use cases.
• I can explain the difference between managed AI services and custom ML model development.
• I can choose simpler managed services when the scenario asks for low operational overhead.

Generative AI Review

• I can explain prompts, tokens, embeddings, foundation models, context windows, and inference parameters.
• I can improve a prompt using context, examples, constraints, and output format.
• I can explain RAG and when it is useful.
• I can distinguish RAG from fine-tuning.
• I can identify hallucination risk and mitigation options.
• I can recognize when guardrails, citations, or human review are needed.

Security and Responsible AI Review

• I can apply IAM least privilege to AI services and data access.
• I can identify when encryption, logging, monitoring, and audit trails are needed.
• I can recognize risks from sensitive data in prompts, logs, datasets, and outputs.
• I can identify fairness, bias, explainability, transparency, privacy, and safety concerns.
• I can select responsible AI controls appropriate to the risk level.

Scenario Practice Review

• For each question, I identify the task before choosing a service.
• I look for words such as “custom training,” “foundation model,” “documents,” “sensitive data,” “low operational overhead,” and “human review.”
• I eliminate answers that overengineer the solution.
• I eliminate answers that ignore security or governance requirements.
• I check whether a purpose-built AWS AI service solves the problem directly.
• I can justify why the correct answer is better than the closest distractor.

Practical Next Step

Pick one weak area from each category: AI/ML fundamentals, generative AI, AWS service selection, security/governance, and responsible AI. Review those notes, then answer mixed scenario questions without looking at explanations. After each miss, write down the clue you overlooked and the decision rule you should apply next time.