AWS Certified AI Practitioner
AIF-C01
AWS's foundational AI certification. Validates understanding of AI, machine learning, and generative AI concepts, foundation model applications, prompt engineering, responsible AI, and the security and governance of AI solutions on AWS. No coding required.
390+
問題数
6
練習テスト
無期限
アップデート
含まれる内容
模擬試験
本番形式の模擬試験6セット
A team is explaining the difference between an algorithm and a model. Which description is correct?
フラッシュカード
間隔反復デッキ
間隔反復
利用できる内容
- フル模擬試験6セット
- 本番形式の問題390問以上
- 全問題に詳しい解説
- 無期限アクセス・30日間返金保証
学習を始めますか？
1回の購入で上記のすべてを利用できます。無期限アクセス、30日間返金保証。
なぜこの認定を取得するのか？
検証されるスキル
- Explaining AI, ML, deep learning, and generative AI concepts and terminology
- Identifying practical use cases where AI adds value, and when it does not
- Describing the AI/ML development lifecycle and MLOps fundamentals
- Explaining foundation model concepts including tokens, embeddings, and context engineering
- Describing agentic AI concepts including MCP, memory, tool use, and orchestration
- +5個のスキル
キャリアの利点
対象職種
給与範囲
$95,000 - $150,000+
AWS's foundational AI certification and the most common entry point into the AWS AI certification track
試験トピックとドメイン
認定試験は、5つの主要なコンピテンシー領域にわたってあなたの知識を評価します：
- Basic AI terminology: AI, ML, deep learning, neural networks, NLP, computer vision, LLMs, agentic AI
- Types of inferencing: batch, real-time, asynchronous, serverless
- Data types: labeled and unlabeled, tabular, time-series, image, text, structured and unstructured
- Learning types: supervised, unsupervised, reinforcement learning
- Practical use cases, and when AI is not appropriate
- AWS managed AI services and the AI/ML development lifecycle
- Model performance metrics and business metrics
- Tokens, chunking, embeddings, vectors, transformers, foundation models
- Multi-modal and diffusion models
- GenAI use cases and the foundation model lifecycle
- Token-based pricing and its effect on cost and performance
- Context engineering in foundation model applications
- Agentic AI: multi-agent patterns, Model Context Protocol, memory, tool use, orchestration
- Advantages and limitations of GenAI, and model selection factors
- AWS GenAI services and their cost tradeoffs
- Foundation model selection criteria and inference parameters
- Retrieval Augmented Generation and Bedrock Knowledge Bases
- Vector database options on AWS
- Cost tradeoffs across pre-training, fine-tuning, in-context learning, RAG, and distillation
- AI agents and their business applications
- Prompt engineering techniques: zero-shot, few-shot, chain-of-thought, templates
- Prompt engineering risks: exposure, poisoning, hijacking, jailbreaking
- Foundation model training, fine-tuning methods, and data preparation
- Evaluating model performance: ROUGE, BLEU, BERTScore, LLM-as-a-judge
- Features of responsible AI: bias, fairness, inclusivity, robustness, safety, veracity
- Amazon Bedrock Guardrails
- Legal risks of generative AI including IP infringement and hallucinations
- Dataset characteristics: inclusivity, diversity, curation, balance
- Effects of bias and variance, overfitting and underfitting
- Transparent and explainable models, SageMaker Model Cards
- Tradeoffs between model safety and transparency
- Human-centered design for explainable AI
- Securing AI systems: IAM, encryption, Amazon Macie, AWS PrivateLink, AgentCore Identity
- Source citation, data lineage, and data cataloging
- Secure data engineering and privacy-enhancing technologies
- Prompt injection, data leakage, output filtering, audit trails
- Hallucination detection and grounding techniques
- AWS Config, Amazon Inspector, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor
- Data governance: lifecycles, logging, residency, retention
- Governance protocols and the Generative AI Security Scoping Matrix
カバーされる技術とツール
できるようになること
この認定を取得した後、あなたは次のことができるようになります：
- Explain AI, ML, and generative AI concepts accurately in a business setting
- Match an AWS AI service to a use case and know when AI is the wrong tool
- Design foundation model applications using RAG, prompting, and agents
- Choose the right customization approach for a cost and accuracy target
- Evaluate foundation model performance with appropriate metrics
- Apply responsible AI principles and detect bias in datasets and outputs
- Secure and govern AI workloads on AWS
この資格が重要な理由
検証されるスキル
- Explaining AI, ML, deep learning, and generative AI concepts and terminology
- Identifying practical use cases where AI adds value, and when it does not
- Describing the AI/ML development lifecycle and MLOps fundamentals
- Explaining foundation model concepts including tokens, embeddings, and context engineering
- Describing agentic AI concepts including MCP, memory, tool use, and orchestration
- Choosing effective prompt engineering techniques and recognizing their risks
- +4個のスキル
キャリアの利点
対象職種
給与範囲
$95,000 - $150,000+
AWS's foundational AI certification and the most common entry point into the AWS AI certification track
積極的に採用している業界
よくある質問
65 questions in 90 minutes, of which 50 are scored and 15 are unscored. Question types include multiple choice, multiple response, ordering, and matching. Scores are reported on a 100 to 1,000 scale and 700 passes.
No. AWS explicitly lists developing or coding AI/ML models, data engineering, hyperparameter tuning, pipeline building, and implementing security protocols as out of scope. The target candidate uses AI/ML solutions rather than building them.
No, there are no formal prerequisites. AWS recommends up to six months of exposure to AI/ML technologies on AWS plus familiarity with core services, the shared responsibility model, IAM, and pricing models. Those new to AWS often take Cloud Practitioner first, but it is not required.
Fundamentals of AI and ML is 20 percent, Fundamentals of GenAI is 24 percent, Applications of Foundation Models is 28 percent, Guidelines for Responsible AI is 14 percent, and Security, Compliance, and Governance is 14 percent. Domain 3 is the largest single section.
100 USD at standard pricing, which places it in AWS's foundational tier alongside Cloud Practitioner.
AI Practitioner (AIF-C01) covers AI concepts and AWS AI services for a broad technical-adjacent audience, with real service breadth. AI Business Strategist (AIB-C01) is a business-track exam covering AI strategy, value measurement, governance, readiness and enterprise scaling. AIF-C01 tests recognition; AIB-C01 tests judgment.
Yes. The current exam guide includes agentic AI concepts such as the Model Context Protocol, multi-agent patterns, memory management and orchestration, plus context engineering, prompt caching, model distillation, and newer AWS offerings including Amazon Bedrock AgentCore, Amazon Nova, Amazon Quick, Kiro and Strands Agents. Older study material predates these additions.
Yes. Alongside multiple choice and multiple response, the exam includes ordering questions, where you place 3 to 5 steps in sequence, and matching questions, where you pair 3 to 7 prompts with responses. All pairs or positions must be correct to earn credit.
合格に向けて始めましょう
すべての模擬試験にすぐアクセスして、今日から対策を始められます
模擬試験のみ
無期限アクセス。一度の購入でずっと使えます
地域により別途税金がかかります
- フル模擬試験6セット
- 本番形式の問題390問以上
- フラッシュカード500枚
- 全問題に詳しい解説
- 試験モードと学習モード
- 再受験は無制限
- どの端末からでも利用可能
Preporatoは独立した試験対策サービスで、Amazon Web Services (AWS)とは提携していません。資格の名称とロゴは各所有者に帰属します。