AWS Certified Generative AI Developer - Professional (AIP-C01) Practice Exams
Original scenario-based mock tests for the ability to integrate foundation models into applications and business workflows and implement generative AI solutions in production using AWS technologies.
AWS validates the ability to integrate foundation models into applications and business workflows and implement generative AI solutions in production using AWS technologies. The intended audience includes GenAI developers, software developers, AI engineers and cloud application builders moving production AI workloads onto AWS. AWS recommends 2 or more years building production-grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience, and 1 year of hands-on experience implementing generative AI solutions.
7565 scored and 10 unscored questions
180 minofficial duration
750minimum scaled passing score
3 yearsstandard certification validity
Question formats: AWS currently uses multiple-choice and multiple-response questions. Unscored questions are not identified during the exam, and there is no penalty for guessing.
Prerequisites and recommended knowledge: No specific prior AWS certification is required. AWS notes that AIF-C01, SAA-C03, MLA-C01 or DEA-C01 can be helpful depending on your background. Recommended AWS knowledge includes compute, storage, networking, security best practices, deployment and IaC tools, monitoring and observability, and cost optimization.
Responsible AI, content safety, identity, threat mitigation, governance and auditability.
Operational Efficiency and Optimization for GenAI Applications
12%
Performance, cost, monitoring, observability, resilience and operational trade-offs.
Testing, Validation, and Troubleshooting
11%
Quality evaluation, factual accuracy, relevance, consistency and application troubleshooting.
On smaller screens, swipe the table horizontally to review every column.
Technologies and concepts to review
Amazon Bedrock and Bedrock AgentCore, knowledge bases, vector databases and embeddings, prompt engineering and management, agentic AI, Responsible AI and content safety, Bedrock Guardrails, evaluation and observability tooling, API and event-driven integration, serverless computing, containers, infrastructure as code, CI/CD and hybrid-cloud patterns.
The official guide is the source of truth and may be revised. Domain weights above total 100% of scored content.
Preparation roadmap
AIP-C01 exam preparation and study plan
Use the official blueprint to organize learning, then use ethical practice exams to identify gaps. Focus on explaining concepts and applying them to new business scenarios—not memorizing isolated answers or searching for unauthorized exam dumps.
Map the official blueprintStart with the five weighted domains; Foundation Model Integration, Data Management, and Compliance is the largest at 31%, followed by Implementation and Integration at 26%.
Build production GenAI solutions hands-onPractice Amazon Bedrock model integration, RAG, knowledge bases, vector stores, prompt management, agents and event-driven workflows end to end.
Test production trade-offsReview security, Responsible AI, governance, quality evaluation, observability, resilience, latency and cost - not only successful-path implementation.
Use practice exams diagnosticallyReview every explanation, record weak objectives, return to AWS documentation and repeat targeted hands-on work before another timed attempt.
Readiness check: A practice score is useful only when you understand why each option is right or wrong. No mock-test result guarantees an AWS exam outcome.
Try before enrolling
Five free AIP-C01 sample questions with answers
These independently written questions sample one concept from each public exam domain. They are learning examples—not copied, recalled, leaked or live AWS exam questions—and require no sign-up.
0 of 5 answers reviewed
Domain 1 - Foundation Model Integration, Data Management, and Compliance
Question 1: Answering from documents that change weekly
A team must let a foundation model answer questions using proprietary product manuals that update weekly, without retraining the model every time. Which approach best fits this requirement?
Fine-tune the foundation model every time a manual changes
Use Retrieval Augmented Generation with a knowledge base indexed on the current manuals
Paste the entire manual set into every prompt sent to the model
Increase the model's temperature setting to improve recall of internal documents
Show answer and explanation
Correct answer: B. RAG retrieves current content from an indexed knowledge base at query time, avoiding retraining. Fine-tuning weekly is slow and costly, embedding full documents in every prompt does not scale, and temperature affects output randomness, not access to internal knowledge.
Learning focus: RAG, knowledge bases and keeping generated answers current without retraining.
Domain 2 - Implementation and Integration
Question 2: Reasoning across multiple internal APIs
A production application must let a foundation model call an internal inventory API and a shipping-status API, then reason across multiple steps to answer a customer's order question. Which capability should the developer implement?
A single static prompt template with no external calls
An agentic AI workflow that orchestrates tool and API calls and reasons across steps
A batch ETL job that runs once nightly
A CDN caching rule for the static website
Show answer and explanation
Correct answer: B. Agentic AI orchestrates multi-step reasoning and tool or API invocation to fulfill a dynamic request. A static prompt, a nightly batch job and CDN caching do not provide dynamic multi-step tool use.
Learning focus: Agentic AI, tool and API orchestration, and Amazon Bedrock AgentCore patterns.
Domain 3 - AI Safety, Security, and Governance
Question 3: Defending a public GenAI application
A public-facing GenAI application must resist attempts to extract its system prompt or bypass content policies through crafted inputs, while still logging attempted violations for audit. Which combination best addresses this?
Rely solely on the foundation model's default behavior
Apply prompt-attack and sensitive-content guardrails, least-privilege IAM access, and centralized logging and monitoring
Remove all logging to protect user privacy
Place confidential business rules directly in the system prompt for convenience
Show answer and explanation
Correct answer: B. Defense in depth combining guardrails against prompt attacks, least-privilege access and logging/monitoring satisfies both protection and auditability. Relying on default behavior, removing logging, or exposing confidential rules in the prompt all increase risk.
Learning focus: Bedrock Guardrails, least privilege, prompt-attack defense and governance logging.
Domain 4 - Operational Efficiency and Optimization for GenAI Applications
Question 4: Balancing latency, cost and RAG context
A GenAI application's response latency and inference cost have both increased significantly after adding long RAG context. Which action is the most direct way to address this trade-off?
Accept the increase because foundation models are inherently slow
Evaluate chunking and retrieval-relevance tuning to reduce unnecessary context length while monitoring quality
Switch all requests to synchronous polling without caching
Permanently disable retrieval regardless of the accuracy impact
Show answer and explanation
Correct answer: B. Tuning chunk size and retrieval relevance reduces unnecessary context without abandoning RAG's accuracy benefits. Accepting the cost, adding synchronous polling, or removing retrieval entirely either ignore the problem or overcorrect.
Learning focus: Cost and latency optimization, chunking strategy and retrieval tuning.
Domain 5 - Testing, Validation, and Troubleshooting
Question 5: Diagnosing inconsistent model answers
A team notices a foundation-model-powered feature returns inconsistent answers to the same customer question across different sessions. Which activity is most appropriate to diagnose whether this is expected behavior or a defect?
Roll back the entire application immediately without investigation
Evaluate consistency and quality metrics across repeated test prompts and inspect temperature and retrieval configuration
Tell customers the inconsistency is expected without any evaluation
Disable all monitoring to reduce noise in the logs
Show answer and explanation
Correct answer: B. Systematic evaluation against quality and consistency metrics, plus reviewing configuration such as temperature and retrieval settings, is the appropriate troubleshooting step. The other options skip diagnosis entirely.
Learning focus: Evaluation metrics, consistency testing and troubleshooting model configuration.
Five sample answers cannot measure full exam readiness. Use them to identify concepts that need deeper study.
Original ethical practice
How these AIP-C01 practice exams support preparation
This independent Udemy-hosted course uses original scenario questions and explanation-led review. It supplements—not replaces—the AWS exam guide, AWS documentation, Skill Builder and hands-on practice.
Apply GenAI architecture judgmentCompare foundation models, RAG patterns, knowledge bases, vector stores, prompt strategies and agentic workflows.
Review production trade-offsPractice security, governance, quality, latency, resilience, observability and cost decisions.
Learn from explanationsUnderstand why plausible alternatives fail and where to return to official documentation.
Prepare ethicallyWork with original learning questions - not leaked, copied, recalled or unauthorized content.
Redeem the AIP-C01 free Udemy coupon in three steps
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Reader questions
AIP-C01 practice exam FAQs
Who is AIP-C01 designed for?
GenAI developers, software developers, AI engineers and cloud application builders moving production AI workloads onto AWS. AWS recommends 2 or more years building production-grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience, and 1 year of hands-on experience implementing generative AI solutions.
How many questions and how much time?
AWS currently lists 75 questions and 180 minutes.
What is the passing score?
The official guide reports a minimum scaled score of 750 out of 1,000.
Is AIP-C01 the same as AWS AI Practitioner AIF-C01?
No. AIF-C01 is Foundational and does not require building solutions. AIP-C01 is Professional-level and targets developers who integrate and operate production GenAI solutions.
Do I need another AWS certification before AIP-C01?
No specific prior certification is required. AWS notes that AIF-C01, SAA-C03, MLA-C01 or DEA-C01 can be helpful depending on your experience.
Does AIP-C01 require hands-on production experience?
Yes. Unlike AIF-C01, AWS targets candidates with 2 or more years building production-grade applications and 1 year of hands-on experience implementing GenAI solutions.
Which AWS services should I know for AIP-C01?
Focus on Amazon Bedrock and Bedrock AgentCore, knowledge bases, vector stores, Bedrock Guardrails, and AWS deployment, monitoring and cost-management tooling relevant to production GenAI applications.
Are these real exam questions or dumps?
No. CertShield provides original independent practice content, not leaked, copied, recalled or unauthorized exam material.
Does a practice score guarantee certification success?
No. Use scores to identify gaps; they cannot predict your exact AWS exam result.
What if the free coupon expires?
Check the current coupon page. If no free allocation is available, use the standard Udemy listing only if the course still fits your needs.