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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Optimize and Operationalize a Generative AI Application	30%	- Implement monitoring, logging, and evaluation 1. Evaluate model outputs for quality and bias 2. Monitor model performance and application metrics 3. Implement human-in-the-loop evaluation workflows 4. Implement logging for prompts and responses - Optimize costs and performance 1. Implement auto-scaling for AI workloads 2. Optimize model selection and parameter tuning 3. Optimize token usage and manage costs 4. Implement caching strategies for frequent queries - Implement CI/CD and automation for AI applications 1. Manage model updates and rollback strategies 2. Implement testing strategies for generative AI applications 3. Implement version control for prompts and models 4. Automate deployment of AI models and applications
Topic 2: Build and Implement a Generative AI Application	45%	- Implement security, compliance, and responsible AI 1. Ensure compliance with AI ethics and responsible use 2. Implement content filtering and safety mechanisms 3. Implement IAM roles and policies for AI services 4. Implement data encryption and privacy controls - Implement data preprocessing and vectorization pipelines 1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) 2. Implement embeddings generation using AWS services 3. Ingest and transform data for model consumption 4. Implement document chunking and text processing - Develop the application using AWS AI services 1. Implement retrieval-augmented generation (RAG) patterns 2. Implement prompt engineering and template management 3. Implement inference calls to Amazon Bedrock or other FMs 4. Integrate with knowledge bases
Topic 3: Plan and Design a Generative AI Application	25%	- Select the appropriate foundation models (FMs) and techniques 1. Select model parameters and configurations 2. Consider fine-tuning vs. retrieval-augmented generation (RAG) 3. Design prompt engineering strategies 4. Evaluate foundation models for the use case - Design the generative AI solution architecture 1. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker) 2. Design for scalability, reliability, and cost-effectiveness 3. Design the end-to-end solution architecture 4. Integrate with other AWS services (e.g., storage, databases, security) - Identify and define the business and technical requirements for a generative AI application 1. Identify the target audience and use cases 2. Identify constraints and risks 3. Determine data requirements and availability 4. Define functional and non-functional requirements

Section

Weight

Objectives

Topic 1: Optimize and Operationalize a Generative AI Application

30%

- Implement monitoring, logging, and evaluation

1. Evaluate model outputs for quality and bias
2. Monitor model performance and application metrics
3. Implement human-in-the-loop evaluation workflows
4. Implement logging for prompts and responses

- Optimize costs and performance

1. Implement auto-scaling for AI workloads
2. Optimize model selection and parameter tuning
3. Optimize token usage and manage costs
4. Implement caching strategies for frequent queries

- Implement CI/CD and automation for AI applications

1. Manage model updates and rollback strategies
2. Implement testing strategies for generative AI applications
3. Implement version control for prompts and models
4. Automate deployment of AI models and applications

Topic 2: Build and Implement a Generative AI Application

45%

- Implement security, compliance, and responsible AI

1. Ensure compliance with AI ethics and responsible use
2. Implement content filtering and safety mechanisms
3. Implement IAM roles and policies for AI services
4. Implement data encryption and privacy controls

- Implement data preprocessing and vectorization pipelines

1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
2. Implement embeddings generation using AWS services
3. Ingest and transform data for model consumption
4. Implement document chunking and text processing

- Develop the application using AWS AI services

1. Implement retrieval-augmented generation (RAG) patterns
2. Implement prompt engineering and template management
3. Implement inference calls to Amazon Bedrock or other FMs
4. Integrate with knowledge bases

Topic 3: Plan and Design a Generative AI Application

25%

- Select the appropriate foundation models (FMs) and techniques

1. Select model parameters and configurations
2. Consider fine-tuning vs. retrieval-augmented generation (RAG)
3. Design prompt engineering strategies
4. Evaluate foundation models for the use case

- Design the generative AI solution architecture

1. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)
2. Design for scalability, reliability, and cost-effectiveness
3. Design the end-to-end solution architecture
4. Integrate with other AWS services (e.g., storage, databases, security)

- Identify and define the business and technical requirements for a generative AI application

1. Identify the target audience and use cases
2. Identify constraints and risks
3. Determine data requirements and availability
4. Define functional and non-functional requirements

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q25-Q30):

NEW QUESTION # 25
A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?

A. Use Amazon SageMaker AI to deploy a pre-trained model to build a personalized recommendation engine for books. Deploy the model as a SageMaker AI endpoint. Invoke the model endpoint by using Amazon API Gateway.
B. Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon DynamoDB. Create an AWS Lambda function that queries the knowledge base.
Configure Amazon API Gateway to invoke the Lambda function when handling user requests.
C. Create an Amazon Kendra GenAI Enterprise Edition index that uses the S3 connector to index the book catalog data stored in Amazon S3. Configure built-in FAQ in the Kendra index. Develop an AWS Lambda function that queries the Kendra index based on user conversations. Deploy Amazon API Gateway to expose this functionality and invoke the Lambda function.
D. Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon OpenSearch Service. Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.

Answer: D

Explanation:
Option A best meets the requirements because it directly implements a Retrieval Augmented Generation pattern for conversational recommendations using managed Amazon Bedrock capabilities and a scalable vector store. The company's source data already resides in Amazon S3, which aligns naturally with Amazon Bedrock Knowledge Bases ingestion workflows. A knowledge base can ingest book text, reviews, and metadata, generate embeddings using a supported embedding model, and persist those vectors in a purpose- built vector backend such as Amazon OpenSearch Service. This enables semantic retrieval that is well suited to conversation-driven intent, where user prompts are often descriptive and do not map cleanly to keyword filters.
The requirement to suggest books based on conversations implies the system must interpret natural language context and retrieve relevant passages, reviews, and metadata to ground the recommendation. Knowledge Bases provide managed orchestration for embedding creation and retrieval, which reduces development effort compared to building custom embedding pipelines. OpenSearch Service provides scalable vector search and k- nearest neighbors style similarity retrieval, which supports low-latency responses when properly indexed and sized.
For scaling to more than 10,000 concurrent users, the API layer design in option A is a common AWS pattern: Amazon API Gateway provides a managed front door with throttling and request handling, while AWS Lambda scales horizontally with demand and can invoke the knowledge base retrieval operations. This separates compute scaling from the vector store scaling and helps keep latency predictable under load.
Option B is not the best choice because DynamoDB is not the standard native vector store target for Amazon Bedrock Knowledge Bases in this context and would introduce additional implementation complexity around vector indexing and similarity search behavior. Option C requires substantial ML lifecycle work, model hosting, tuning, and continuous iteration to achieve quality recommendations at scale. Option D provides strong enterprise search, but it focuses on retrieval and FAQs rather than a managed RAG recommendation workflow grounded in embeddings and conversational context for generative responses.

NEW QUESTION # 26
A company is designing a solution that uses foundation models (FMs) to support multiple AI workloads.
Some FMs must be invoked on demand and in real time. Other FMs require consistent high-throughput access for batch processing.
The solution must support hybrid deployment patterns and run workloads across cloud infrastructure and on- premises infrastructure to comply with data residency and compliance requirements.
Which combination of steps will meet these requirements? (Select TWO.)

A. Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints.
B. Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high- volume workloads.
C. Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment.
D. Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges.
E. Use Amazon SageMaker JumpStart to host and invoke the FMs.

Answer: B,C

Explanation:
The correct combination is B and C because together they address both workload diversity and hybrid deployment requirements with minimal custom engineering.
Option B provides consistent, high-throughput access by configuring provisioned throughput in Amazon Bedrock. Provisioned throughput guarantees predictable capacity and performance, which is essential for batch processing workloads that require sustained inference rates. This eliminates cold starts and throttling concerns that can occur with purely on-demand usage, making it well suited for high-volume enterprise workloads.
Option C enables hybrid deployment across cloud and on-premises environments by deploying foundation models to Amazon SageMaker AI endpoints and using Amazon SageMaker Neo for edge and on-premises optimization. SageMaker Neo compiles models for target hardware, allowing inference to run efficiently outside the AWS cloud while still using AWS-managed tooling. Orchestrating these deployments with AWS Lambda allows consistent invocation patterns across environments.
Option A uses asynchronous endpoints, which are not suitable for real-time, low-latency inference. Option D addresses scaling but does not support on-premises or hybrid deployment. Option E simplifies model onboarding but does not address hybrid execution or guaranteed throughput.
Therefore, Options B and C together provide real-time and batch support, predictable performance, and true hybrid deployment while minimizing operational overhead.

NEW QUESTION # 27
A company runs a generative AI (GenAI)-powered summarization application in an application AWS account that uses Amazon Bedrock. The application architecture includes an Amazon API Gateway REST API that forwards requests to AWS Lambda functions that are attached to private VPC subnets. The application summarizes sensitive customer records that the company stores in a governed data lake in a centralized data storage account. The company has enabled Amazon S3, Amazon Athena, and AWS Glue in the data storage account.
The company must ensure that calls that the application makes to Amazon Bedrock use only private connectivity between the company's application VPC and Amazon Bedrock. The company's data lake must provide fine-grained column-level access across the company's AWS accounts.
Which solution will meet these requirements?

A. Run Lambda functions in private subnets. Configure a NAT gateway to provide access to Amazon Bedrock and the data lake. Use S3 bucket policies and ACLs to manage permissions. Export AWS CloudTrail logs to Amazon S3 to perform weekly reviews.
B. Use VPC endpoints to provide access to Amazon Bedrock and Amazon S3 in the application account.Use only IAM path-based policies to manage data lake access. Send AWS CloudTrail logs to Amazon CloudWatch Logs. Periodically create dashboards and allow public fallback for cross-Region reads to reduce setup time.
C. Create a gateway endpoint only for Amazon S3 in the application account. Invoke Amazon Bedrock through public endpoints. Use database-level grants in AWS Lake Formation to manage data access.
Stream AWS CloudTrail logs to Amazon CloudWatch Logs. Do not set up metric filters or alarms.
D. In the application account, create interface VPC endpoints for Amazon Bedrock runtimes. Run Lambda functions in private subnets. Use IAM conditions on inference and data-plane policies to allow calls only to approved endpoints and roles. In the data storage account, use AWS Lake Formation LF-tag- based access control to create table-level and column-level cross-account grants.

Answer: A

Explanation:
The first option labeled B is the correct solution because it fully satisfies both private connectivity and fine- grained cross-account data governance requirements using AWS-native services.
Creating interface VPC endpoints for Amazon Bedrock runtimes ensures that all inference calls remain on the AWS private network and never traverse the public internet. Running AWS Lambda functions in private subnets enforces network isolation, and using IAM conditions that restrict access to specific VPC endpoints and roles prevents unauthorized inference calls.
For the governed data lake, AWS Lake Formation LF-tag-based access control is the recommended AWS mechanism for enforcing cross-account, column-level permissions. LF-tags allow the company to define data access policies once and apply them consistently across accounts, databases, tables, and even individual columns. This is required for sensitive customer records and is not achievable with S3 bucket policies or IAM alone.
The second option labeled B uses a NAT gateway, which violates the private connectivity requirement.
Option C uses public Bedrock endpoints and only database-level grants, which are insufficient. Option D relies on IAM path-based policies, which cannot enforce column-level access and introduces public fallback paths.
Therefore, the first option labeled B is the only solution that meets all networking, security, and data governance requirements.

NEW QUESTION # 28
A financial services company provides an Amazon Bedrock-powered AI assistant that provides investment guidance to customers. Recent audits found racially and gender-biased responses. Auditors flagged several cases as potential regulatory compliance risks. The company must implement a 30-day remediation plan to eliminate biased outputs. The plan must preserve model accuracy and keep latency within acceptable customer-experience thresholds.
A GenAI developer must design a long-term solution that balances regulatory risks, operational scalability, and model-agnostic enforcement. The solution must apply enforceable at-inference-time controls so bias mitigation measures cannot be bypassed. The solution must provide logging for each inference for audit and regulatory reviews. The solution must support ongoing automated bias monitoring. The solution must not require any model re-training within 30 days. The solution must add minimal inference latency.
Which solution will meet these requirements?

A. Use AWS Lambda to build a custom fairness classifier that evaluates model outputs. Store classifier scores in Amazon DynamoDB. Block responses that fall below the fairness threshold.
B. Use AWS Lambda to build a custom fairness classifier that evaluates model outputs against demographic fairness criteria. Store classifier scores in Amazon DynamoDB. Track metadata for the scores. Block responses that fall below the fairness threshold. Route flagged cases to compliance teams for manual reviews before delivering responses to customers.
C. Use Amazon SageMaker Clarify to generate offline bias reports. Schedule weekly manual reviews with compliance teams to evaluate fairness trends and recommend model adjustments.
D. Route all responses through Amazon API Gateway and AWS WAF. Configure AWS WAF to use a custom managed rule group to detect biased terms and to block disallowed responses. Use AWS CloudTrail Lake to analyze long-term traffic trends.

Answer: B

Explanation:
Option C most completely satisfies the mandatory controls. A model-independent fairness classifier can evaluate every generated response at inference time, enforce a blocking threshold, preserve classifier scores and associated metadata for auditability, and route questionable cases for human compliance review without retraining the underlying FM. Option B provides automated blocking but omits the stronger audit metadata and regulatory-review workflow explicitly required by the scenario. Option A is insufficient because SageMaker Clarify can monitor bias and publish bias metrics, but scheduled offline or periodic analysis does not provide a non-bypassable control on every Bedrock response. Option D is inappropriate because AWS WAF matching operates primarily on HTTP request components and pattern-based conditions; detecting prohibited words is not equivalent to evaluating demographic fairness or semantic bias in generated investment guidance.

NEW QUESTION # 29
A company uses AWS Lambda functions to build an AI agent solution. A GenAI developer must set up a Model Context Protocol (MCP) server that accesses user information. The GenAI developer must also configure the AI agent to use the new MCP server. The GenAI developer must ensure that only authorized users can access the MCP server.
Which solution will meet these requirements?

A. Use a Lambda function to host the MCP server. Create an Amazon API Gateway HTTP API that proxies requests to the Lambda function. Configure the AI agent solution to use the Streamable HTTP transport to make requests through the HTTP API. Use Amazon Cognito to enforce OAuth 2.1.
B. Use a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent to use the STDIO transport with the MCP server.
C. Use a Lambda layer to host the MCP server. Add the Lambda layer to the AI agent Lambda functions.Configure the agentic AI solution to use the STDIO transport to send requests to the MCP server. In the AI agent's MCP configuration, specify the Lambda layer ARN as the command. Specify the user credentials as environment variables.
D. Use a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent's MCP client to invoke the MCP server asynchronously.

Answer: A

Explanation:
Option C is the correct solution because it provides a secure, scalable, and standards-compliant way to expose an MCP server to an AI agent while enforcing strong user authorization. The Model Context Protocol supports HTTP-based transports for remote MCP servers, making Streamable HTTP the appropriate choice when the server is hosted as a managed service rather than a local process.
Hosting the MCP server in AWS Lambda enables automatic scaling and cost-efficient execution. By placing Amazon API Gateway in front of the Lambda function, the company creates a secure, managed HTTP endpoint that the AI agent can invoke reliably. This architecture cleanly separates transport, authentication, and business logic, which aligns with AWS serverless best practices.
Using Amazon Cognito to enforce OAuth 2.1 ensures that only authenticated and authorized users can access the MCP server. This satisfies security and compliance requirements when the MCP server handles sensitive user information. Cognito integrates natively with API Gateway, removing the need for custom authentication logic and reducing operational overhead.
Option A lacks user-level authorization controls. Option B and Option D rely on STDIO transport, which is intended for local or tightly coupled processes and is not suitable for distributed, serverless architectures.
Option D also introduces security risks by handling credentials through environment variables.
Therefore, Option C best meets the requirements for secure access control, scalability, and correct MCP integration in an AWS-based AI agent architecture.

NEW QUESTION # 30
......

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