[NEW] AWS Certified Generative AI Developer - Professional

AWS Certified Generative AI Developer - Professional Detailed Exam Domain CoverageBefore diving into the course details, here is the exact breakdown of the AWS Certified Generative AI Developer – Professional exam domains to help you focus your study efforts:Domain 1: Foundation Model Integration, Data Management, and Compliance (31%)Integrate foundation models into applications and workflows. Design and manage data pipelines for GenAI solutions. Apply compliance, governance, and data security standards.

Utilize vector stores and Retrieval Augmented Generation (RAG). Evaluate foundation models for quality and responsibility. Domain 2: Implementation and Integration (26%)Implement GenAI services using AWS Bedrock and related services.

Develop and apply prompt engineering techniques. Build agentic AI solutions and orchestrate workflows. Integrate GenAI applications with AWS Lambda, API Gateway, and other services.

Deploy and manage generative AI models in production. Domain 3: AI Safety, Security, and Governance (20%)Apply security controls and encryption for GenAI workloads. Implement responsible AI practices and risk assessments.

Establish governance frameworks for model usage. Ensure data privacy and compliance with regulatory requirements. Monitor and audit AI system behavior for safety.

Domain 4: Operational Efficiency and Optimization for GenAI Applications (12%)Optimize cost and performance of GenAI workloads. Scale inference using appropriate AWS compute options. Monitor application metrics with CloudWatch and logs.

Implement caching and latency reduction strategies. Tune model parameters for operational efficiency. Domain 5: Testing, Validation, and Troubleshooting (11%)Validate model outputs against quality criteria.

Conduct functional and performance testing of GenAI solutions. Troubleshoot integration and runtime issues. Perform load and stress testing for scalability.

Implement continuous monitoring and alerting. Course DescriptionPassing the AWS Certified Generative AI Developer – Professional certification requires more than a high-level understanding of artificial intelligence. It demands deep, practical expertise in building, securing, and scaling production-grade generative AI applications on AWS.

I created this practice test course to bridge the gap between theoretical knowledge and the complex, scenario-based questions you will face on the actual exam. Throughout these practice exams, you will be tested on real-world architectural decisions. The questions dive into evaluating foundation models for specific use cases, setting up secure data pipelines for Retrieval-Augmented Generation (RAG), and using Amazon Bedrock to build agentic workflows.

Because operational efficiency and governance make up a significant portion of the exam, I have also heavily focused on scenarios requiring you to optimize inference costs, implement Guardrails for data privacy, and monitor AI workloads with Amazon CloudWatch. Every question in this bank includes a comprehensive breakdown of the correct architecture and detailed explanations of why the incorrect options would fail in a production environment. My goal is to ensure you understand the core AWS GenAI methodologies so you can walk into the exam room with complete confidence.

Practice Questions PreviewHere is a sample of the exact type of scenario-based questions you will find inside the course:Question 1: You are building a generative AI customer support agent using Amazon Bedrock. The agent needs to query a company's internal inventory API to answer user questions about product availability. Which implementation requires the LEAST amount of custom orchestration code?

Option A: Deploy a custom orchestration script on an Amazon EC2 instance using an open-source framework like LangChain. Option B: Configure Amazon Bedrock Agents with an Action Group that triggers an AWS Lambda function to query the inventory API. Option C: Create an AWS Step Functions state machine that alternatingly calls the Bedrock InvokeModel API and the inventory API.

Option D: Fine-tune a foundation model on the internal inventory database so it has native knowledge of product availability. Option E: Deploy a custom foundation model on Amazon SageMaker endpoints and use AWS Glue to inject inventory data into the prompt. Option F: Use Amazon Kendra to index the inventory database and pass the search results directly to the user without a foundation model.

Correct Answer: Option BOverall Explanation: Amazon Bedrock Agents are designed to autonomously orchestrate interactions between foundation models, data sources, and external APIs. By defining an Action Group and linking it to a Lambda function, the agent handles the complex reasoning and API orchestration natively, drastically reducing the custom code required compared to building manual state machines or hosting open-source frameworks. Why Options are Correct/Incorrect:A (Incorrect): Managing custom LangChain scripts on EC2 requires high operational overhead (patching, scaling) and significant custom code.

B (Correct): Bedrock Agents natively handle the orchestration and tool use. Connecting an Action Group to a Lambda function requires minimal custom code strictly for the API call itself. C (Incorrect): Step Functions can orchestrate APIs, but manually building the routing logic between user prompts, the LLM, and the API requires heavy custom configuration.

D (Incorrect): Fine-tuning does not provide real-time lookup capabilities. The model's knowledge of inventory would be instantly outdated. E (Incorrect): SageMaker endpoints are for hosting models, not orchestrating API calls.

This adds immense complexity and does not solve the orchestration problem efficiently. F (Incorrect): Kendra is an intelligent search service, not an orchestration agent. It cannot parse conversational queries, query live transactional APIs, and generate conversational responses on its own.

Question 2: A financial institution is using Amazon Bedrock Knowledge Bases to build a Retrieval-Augmented Generation (RAG) application. The source PDFs stored in Amazon S3 contain sensitive Personally Identifiable Information (PII). How can you ensure the PII is redacted before the foundation model generates a response, while enforcing responsible AI content filtering?

Option A: Write a custom AWS Lambda function to parse and redact text using regular expressions before it reaches Bedrock. Option B: Implement Guardrails for Amazon Bedrock, configuring sensitive information filters for PII and content filters for toxicity. Option C: Enable AWS WAF on the Amazon API Gateway fronting the Bedrock application to block requests containing PII.

Option D: Process all source documents with Amazon Macie to permanently delete PII from the S3 bucket prior to ingestion. Option E: Train a custom text-classification model using Amazon SageMaker to filter out PII and toxic content. Option F: Use AWS Key Management Service (KMS) to encrypt the PII data fields within the S3 objects.

Correct Answer: Option BOverall Explanation: Guardrails for Amazon Bedrock provide a native, managed way to implement safeguards across your generative AI applications. They allow you to define sensitive information filters (which can automatically mask or block PII) and content filters (to block harmful or toxic content) without writing complex custom regex or managing separate classification models. Why Options are Correct/Incorrect:A (Incorrect): Custom regex in Lambda is fragile, difficult to maintain, and does not natively handle responsible AI content filtering (toxicity).

B (Correct): Guardrails natively support both PII redaction (sensitive information filters) and toxicity blocking (content filters) at the Bedrock API level. C (Incorrect): AWS WAF analyzes web request headers and payloads for exploits, not semantic PII or toxicity in generative AI interactions. D (Incorrect): Amazon Macie can discover PII, but permanently deleting data from source documents alters the original records, which may not be acceptable.

Guardrails mask the data dynamically. E (Incorrect): Building and maintaining a custom SageMaker model for classification introduces unnecessary operational overhead when a native managed feature exists. F (Incorrect): KMS encrypts data at rest.

When the RAG pipeline reads the document, the data is decrypted, meaning the LLM would still be exposed to the cleartext PII. Question 3: You are optimizing a high-traffic generative AI chatbot powered by Amazon Bedrock. Users frequently ask the same or semantically similar questions.

You need to reduce Bedrock API invocation costs and improve inference latency. Which strategy is the MOST operationally efficient? Option A: Implement exact-match caching using Amazon API Gateway.

Option B: Store user queries in Amazon SQS and process them in large batches via AWS Lambda. Option C: Provision Provisioned Throughput for the chosen foundation model in Amazon Bedrock. Option D: Implement a semantic cache using a vector database like Amazon OpenSearch Serverless or Amazon ElastiCache.

Option E: Replace the foundation model with a smaller, significantly less capable open-source model hosted on EC2. Option F: Configure an AWS Glue job to pre-generate answers for all possible user queries and store them in Amazon DynamoDB. Correct Answer: Option DOverall Explanation: For generative AI workloads, users rarely type the exact same string, so exact-match caching is ineffective.

Semantic caching leverages a vector database to convert incoming prompts into embeddings and search for similar previous queries. If a highly similar query was recently answered, the cached response is returned immediately. This reduces expensive API calls to the LLM and drastically cuts down latency.

Why Options are Correct/Incorrect:A (Incorrect): API Gateway caching relies on exact string matches. It will miss variations of the same question (e. g.

, "How do I reset my password? " vs "Password reset instructions"). B (Incorrect): Batching requests with SQS introduces significant latency, ruining the real-time experience of a chatbot.

C (Incorrect): Provisioned Throughput guarantees inference capacity and can stabilize latency, but it is expensive and does not reduce invocation costs for repetitive queries. D (Correct): Semantic caching uses embeddings to match the meaning of a prompt, successfully returning cached answers for similar questions, saving both time and money. E (Incorrect): Migrating to an underpowered model on EC2 compromises the quality of the application and increases the operational burden of managing infrastructure.

F (Incorrect): It is impossible to predict and pre-generate answers for every possible natural language query a user might ask. Welcome to the Mock Exam Practice Tests Academy to help you prepare for your AWS Certified Generative AI Developer - Professional. You can retake the exams as many times as you wantThis is a huge original question bankYou get support from instructors if you have questionsEach question has a detailed explanationMobile-compatible with the Udemy appI hope that by now you're convinced!

And there are a lot more questions inside the course.