FAQ

How should I use this FAQ?

What is AIP-C01 testing?

AIP-C01 is a professional-level GenAI developer exam. AWS describes it as validating whether you can integrate foundation models into applications and business workflows and implement production GenAI solutions with AWS technologies.

That means the exam is not a generic AI vocabulary test. It is closer to a production architecture and implementation exam for:

• vector stores, RAG, and knowledge bases
• FM integration into applications and workflows
• prompt engineering and prompt governance
• agentic AI solutions
• cost, performance, monitoring, and troubleshooting
• security, governance, responsible AI, and evaluation

Who is the target candidate?

AWS describes the target candidate as someone with production application experience, general AI/ML or data engineering exposure, and hands-on GenAI implementation experience. The AWS knowledge expectation includes compute, storage, networking, security, identity, deployment, infrastructure as code, monitoring, observability, and cost optimization.

If you only know prompt-writing terminology, you will likely feel underprepared. AIP-C01 expects you to reason like a developer shipping GenAI into production.

What is out of scope?

AWS lists advanced model development and training, advanced ML techniques, and data engineering/feature engineering as out of scope for the target candidate. That does not mean data and ML concepts disappear. It means you should focus on using, integrating, governing, evaluating, and operating FMs rather than building base models from scratch.

How many questions are scored?

The current AWS exam guide says the exam includes 75 total questions. Of those, 65 questions affect your score and 10 are unscored. The unscored questions are not identified on the exam.

What score do I need?

AWS reports AIP-C01 results on a scaled score from 100 to 1,000. The minimum passing score is 750. AWS also notes that the exam uses compensatory scoring, so you pass based on the overall score rather than needing to pass every individual domain.

Which question types should I expect?

AWS lists multiple choice and multiple response. Multiple-choice items have one correct response. Multiple-response items require all correct selections to receive credit.

For multiple response, do not pick answers only because each one sounds technically true. Count the constraints in the stem and reject options that miss security, data boundary, evaluation, cost, latency, or operational requirements.

Where should I go for IT Mastery practice?

What should I study first?

Start with Foundation Model Integration, Data Management, and Compliance because it is the largest domain and frames the rest of the exam. Then study implementation, safety/security/governance, operations, and testing.

Use this priority order:

What is the biggest exam trap?

The highest-risk trap is: Bigger model equals better answer. The better move is: Use model fit, grounding, latency, cost, and quality evidence.

Other common traps:

How should I use practice questions?

Use practice questions after a focused read, not before any context. For every miss, write:

• the tested lane: RAG, agent, prompt, governance, cost, monitoring, evaluation, or troubleshooting
• the deciding constraint: latency, cost, security, freshness, auditability, quality, or workflow failure
• the correct rule
• why each distractor failed

The goal is not to memorize the answer. The goal is to recognize the operational constraint faster next time.

How current are the domain weights?

As of the current AWS Documentation exam guide checked for this update, the domain weights are:

Always re-open the official AWS exam guide before booking because vendor exam guides can change.

Is this more like an AI exam or an AWS architecture exam?

It is both, but the exam behavior is closer to a production GenAI developer/architecture exam than a pure theory exam. You should know AWS services and patterns well enough to decide:

• when to use Bedrock Knowledge Bases versus custom retrieval
• when to use agents versus explicit workflow orchestration
• when to use guardrails versus IAM/KMS/data controls
• when to optimize tokens versus model route versus caching versus throughput
• when to troubleshoot retrieval, prompt assembly, tool calls, or model behavior

How should I approach scenario questions?

Use this order:

1. Identify the workload: direct FM call, RAG, agent, extraction, evaluation, or operations.
2. Identify the failure risk: hallucination, stale context, unsafe output, data exposure, latency, cost, permissions, or weak observability.
3. Choose the smallest production pattern that addresses that risk.
4. Add the missing control: IAM, KMS, VPC endpoint, guardrail, logging, evaluation, retry, human review, or rollback.
5. Reject answers that improve model output but ignore data boundary, quality evidence, or operational ownership.

How should I study the AWS services?

Do not study services as isolated flashcards. Study them as answer roles:

What should I check in the final week?

Focus on decision speed:

• recite the five domains and weights
• practice RAG failure diagnosis: stale data, weak chunking, wrong filters, noisy context, bad citations
• practice agent failure diagnosis: tool schema, permissions, state, retries, approval, audit
• practice safety distinction: guardrails vs IAM vs KMS vs data classification vs human review
• practice operations distinction: token cost vs latency vs throughput vs monitoring
• practice evaluation distinction: demo vs regression dataset vs canary vs rollback

What official pages should I keep open?

Use the AIP-C01 resources page as the curated link list. At minimum, keep the AWS certification page, the AWS Documentation exam guide, and the relevant Bedrock service docs available during final review.

Which live facts should be checked before scheduling?

Use the official vendor source for live details such as exam name, status, beta or retirement notes, price, duration, delivery method, languages, and domain weights.