Why Study with PlanetCert?
The Latest Questions
Practice questions and exam topics aligned with the current exam objectives.
Detailed Explanations
Go beyond the answer. Master the material with comprehensive learning and professional explanations for every concept.
AI-Powered Insights
Personalized preparation guidance that adapts to your performance and identifies weak spots automatically.
Exam Information
Official specifications published by Amazon
Exam Format
90 min
65
700
Foundational
Registration
$100 USD
Pearson VUE or online proctoring
Validity
3 years
Pass the latest version of AWS Certified AI Practitioner exam; Earn AWS Certified Machine Learning Engineer - Associate (automatically recertifies this certification)
AIF-C01 Exam Topics and Domains
AIF-C01 is organized into 5 weighted domains. Expect to work with Amazon Bedrock, Amazon SageMaker Clarify, Amazon Macie, Amazon Q, and more.
1
Fundamentals of AI and ML
20%
Explain basic AI concepts and terminologies
AI/ML Core TerminologyInferencing Types and Data
- Define basic AI terms (AI, ML, deep learning, neural networks, computer vision, NLP, model, algorithm, training, inferencing, bias, fairness, fit, LLM)
- Describe similarities and differences between AI, ML, and deep learning
- Describe various types of inferencing (batch, real-time)
- Describe different types of data in AI models
- Describe supervised, unsupervised, and reinforcement learning
Identify practical use cases for AI
AI Use Case IdentificationML Technique Selection
- Recognize applications where AI/ML can provide value
- Determine when AI/ML solutions are not appropriate
- Select appropriate ML techniques for specific use cases
- Identify real-world AI applications
- Explain capabilities of AWS managed AI/ML services
Describe the ML development lifecycle
ML Pipeline ComponentsModel Sources and DeploymentMLOps and Model Performance
- Describe components of an ML pipeline
- Understand sources of ML models
- Describe methods to use a model in production
- Identify relevant AWS services for each stage of ML pipeline
- Understand MLOps concepts
- Understand model performance and business metrics
2
Fundamentals of Generative AI
24%
Explain the basic concepts of generative AI
Generative AI Core ConceptsGenerative AI Use CasesFoundation Model Lifecycle
- Understand foundational generative AI concepts (tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models)
- Identify potential use cases for generative AI models
- Describe the foundation model lifecycle
Understand capabilities and limitations of generative AI
Generative AI AdvantagesGenerative AI LimitationsModel Selection Criteria
- Describe advantages of generative AI
- Identify disadvantages of generative AI solutions
- Understand factors to select appropriate generative AI models
- Determine business value and metrics for generative AI applications
Describe AWS infrastructure and technologies for building generative AI applications
AWS Generative AI ServicesAWS Infrastructure BenefitsCost Tradeoffs
- Identify AWS services for developing generative AI applications
- Describe advantages of using AWS generative AI services
- Understand benefits of AWS infrastructure for generative AI
- Understand cost tradeoffs of AWS generative AI services
3
Applications of Foundation Models
28%
Describe design considerations for applications that use foundation models
Pre-trained Model SelectionInference ParametersRetrieval Augmented Generation (RAG)Vector DatabasesModel Customization Cost TradeoffsAI Agents
- Identify selection criteria to choose pre-trained models
- Understand effect of inference parameters on model responses
- Define Retrieval Augmented Generation (RAG)
- Identify AWS services for vector databases
- Explain cost tradeoffs of foundation model customization
- Understand role of agents in multi-step tasks
Choose effective prompt engineering techniques
Prompt Engineering FundamentalsAdvanced Prompt TechniquesPrompt Best PracticesPrompt Limitations
- Describe concepts and constructs of prompt engineering
- Understand techniques for prompt engineering
- Understand benefits and best practices for prompt engineering
- Define potential risks and limitations of prompt engineering
Describe the training and fine-tuning process for foundation models
Foundation Model TrainingFine-tuning MethodsData Preparation for Fine-tuning
- Describe key elements of training a foundation model
- Define methods for fine-tuning a foundation model
- Describe how to prepare data to fine-tune a foundation model
Describe methods to evaluate foundation model performance
Evaluation ApproachesPerformance MetricsBusiness Objective Alignment
- Understand approaches to evaluate foundation model performance
- Identify relevant metrics to assess foundation model performance
- Determine whether a foundation model effectively meets business objectives
4
Guidelines for Responsible AI
14%
Explain the development of AI systems that are responsible
Responsible AI FeaturesModel Selection for Responsible AILegal and Compliance RisksDataset Characteristics and BiasBias Detection and Monitoring
- Identify features of responsible AI
- Understand how to use tools to identify features of responsible AI
- Understand responsible practices to select a model
- Identify legal risks of working with generative AI
- Identify characteristics of datasets
- Understand effects of bias and variance
- Describe tools to detect and monitor bias, trustworthiness, and truthfulness
Recognize the importance of transparent and explainable models
Model Transparency vs OpacityExplainability ToolsSafety vs Transparency TradeoffsHuman-Centered Design
- Understand differences between transparent/explainable and non-transparent models
- Understand tools to identify transparent and explainable models
- Identify tradeoffs between model safety and transparency
- Understand principles of human-centered design for explainable AI
5
Security, Compliance, and Governance for AI Solutions
14%
Explain methods to secure AI systems
AWS Security Services for AISource Citation and Data ProvenanceSecure Data EngineeringSecurity and Privacy Considerations
- Identify AWS services and features to secure AI systems
- Understand concept of source citation and documenting data origins
- Describe best practices for secure data engineering
- Understand security and privacy considerations for AI systems
Recognize governance and compliance regulations for AI systems
Regulatory ComplianceAWS Governance ServicesData Governance StrategiesGovernance Protocol Processes
- Identify regulatory compliance standards for AI systems
- Identify AWS services to assist with governance and compliance
- Describe data governance strategies
- Describe processes to follow governance protocols
How do I earn this certification?
Passing AIF-C01 earns the AWS Certified AI Practitioner certification. It sits in the AI/ML and Data track.
Current Level Exams
Next Level Options
Alternative Paths
- DAS-C01 - AWS Certified Data Analytics - SpecialtyComplementary skills in data analysis for AI applications
- SOA-C02 - AWS Certified SysOps Administrator - AssociateManaging and monitoring AI infrastructure
- ANS-C01 - AWS Certified Advanced Networking - SpecialtyFor edge AI and distributed ML workloads
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for AIF-C01 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2024-12-12
Updates
- Amazon Bedrock Latest Core service - 28% of exam (Domain 3) heavily focused on Bedrock • Release date: 2023-09-28
- Amazon Q Latest Covered in exam as business AI assistant • Release date: 2023-11-28
- Amazon SageMaker Clarify Latest Critical for Domain 4 (Responsible AI) - bias detection and model explainability
- Prompt Engineering Domain 3.2 dedicated to prompt engineering techniques
Who should take this exam?
- Up to 6 months exposure to AI/ML technologies on AWS
- Basic understanding of AWS Cloud fundamentals
- Familiarity with AI/ML concepts and use cases