AWS AI Practitioner (AIF-C01)

AIF-C01 65 items · 90 min Pass 700/1000 $100 Foundational

AI vocabulary plus the AWS AI service catalogue. AIF-C01 validates a foundational understanding of AI, ML and generative AI and the ability to name the AWS service that fits a use case. You are not expected to code a model, tune hyperparameters or build a pipeline — but you are expected to know what Bedrock is for, what SageMaker AI is for, which pre-trained service handles which modality, and what responsible AI requires of you.

The blueprint

#	Domain	Weight	Where it is taught here
1	Fundamentals of AI and ML	20%	AI & ML foundations
2	Fundamentals of GenAI	24%	Generative AI
3	Applications of Foundation Models	28%	Generative AI, AWS AI services
4	Guidelines for Responsible AI	14%	Responsible AI & security
5	Security, Compliance, and Governance for AI Solutions	14%	Responsible AI & security

Generative AI and its applications are 52% of the paper. Prompting, inference parameters, model customisation, RAG and agents are where the marks concentrate — not in classical ML theory.

Exam mechanics

• 65 items, 90 minutes. 50 scored, 15 unscored pretest items mixed in unmarked.
• Scaled 100–1,000, pass at 700, compensatory across domains.
• Four item types, and this is the only exam on this site with the last two:
  • Multiple choice — one of four.
  • Multiple response — two or more of five-plus, all correct answers required.
  • Ordering — arrange 3–5 responses in the correct sequence; the whole sequence must be right.
  • Matching — pair a list of responses against 3–7 prompts; every pair must be right.
• Ordering and matching are all-or-nothing, so practise lifecycle sequences (data → training → evaluation → deployment → monitoring) until the order is automatic.
• Unanswered items score as incorrect; there is no guessing penalty.

The four note pages

#	Page	Study points	Covers
1	AI & ML foundations	20	Learning paradigms, deterministic vs probabilistic models, train/validation/test splits, bias and variance, evaluation metrics, embeddings, transfer learning
2	Generative AI	21	Foundation models and LLMs, inference parameters, prompt engineering, model customisation, RAG, agents
3	AWS AI services	14	The pre-trained Amazon AI services, SageMaker AI, Bedrock, SageMaker Clarify and explainability
4	Responsible AI & security	15	Dimensions of responsible AI, interpretability vs explainability, AI security threats, audit, compliance and governance

70 study points in total, split evenly between the concept pages and the service pages — which is how the exam splits too.

The service map

The fastest way to lose marks here is to know the concept and not the service. Know which box each item belongs in:

Need	Service
Foundation models from multiple providers, managed, no infrastructure	Amazon Bedrock
Build, train, tune and deploy your own models	Amazon SageMaker AI
Bias detection and model explainability	SageMaker Clarify
Grounding a model in your own documents, managed	Bedrock Knowledge Bases (managed RAG)
Content filters, denied topics, PII filtering, grounding checks	Bedrock Guardrails
Text extraction from documents and forms	Amazon Textract
Image and video analysis	Amazon Rekognition
Speech to text / text to speech	Amazon Transcribe / Amazon Polly
Translation, sentiment and entity extraction	Amazon Translate / Amazon Comprehend
Conversational bots	Amazon Lex
Personalised recommendations	Amazon Personalize

Choosing between prompting, RAG and fine-tuning

This trade-off is asked repeatedly, in several disguises:

Approach	Use when	Cost and effort
Prompt engineering	The model already knows enough; you need format, tone or reasoning structure	Lowest — no training, instant iteration
RAG	The answer depends on your own or on fresh data	Moderate — retrieval infrastructure, no training; content stays current
Fine-tuning	You need consistent style, domain vocabulary or behaviour the prompt cannot reach	Highest — labelled data, training cost, and it goes stale as the domain moves

The exam’s default preference runs left to right: try the cheapest approach that meets the requirement, and reach for fine-tuning only when the stem rules the others out.

AIF-C01 versus AIB-C01

Both are AI credentials and they are routinely confused. The dividing line is whether AWS services are assessed.

	AI Practitioner (AIF-C01)	AI Business Strategist (AIB-C01)
Category	Foundational	Business
Assesses AWS services?	Yes	No — strategic familiarity only
Validates	AI/ML and GenAI concepts applied on AWS	Investment judgment, business cases, governance, scaling
Items · time · pass	65 · 90 min · 700	85 · 170 min · 700 (beta)
Price	$100	$50 during beta

If your question is “which service”, this exam. If it is “should we fund this and how do we govern it”, AIB-C01.

A two-week plan

Where to go next

• Compare all five tracks on the certification tracks page.
• Cloud Practitioner if the AWS-service half of this exam was the hard part.
• The AWS appendix carries the responsible-AI frameworks, the governance toolkit and the service landscape.