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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	AI Safety, Security, and Governance: This domain addresses input output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.
Topic 2	Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.
Topic 3	Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.
Topic 4	Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Topic 5	Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q96-Q101):

NEW QUESTION # 96
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. 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.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 97
A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.
A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company's approved resources. The company does not want to make project-level modifications.
Which solution will meet these requirements?

A. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.
B. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.
C. Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples.
Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the workspace context to retrieve suggestions from the Git repository.
D. Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference for code suggestions.

Answer: B

Explanation:
Option D is the correct solution because Amazon Q Developer customizations are designed to incorporate organization-approved knowledge and coding guidance without requiring per-project changes. A customization can point Amazon Q Developer to curated internal sources such as approved libraries, coding standards, architectural patterns, and proprietary techniques. This allows the assistant's suggestions to align with company policies and preferred implementations consistently across teams and repositories.
The key requirement is that the company does not want to make project-level modifications. Options A, B, and C all require adding files or repositories into the project workspace, which directly violates this constraint.
They also rely on developer behavior to "use workspace context," which is harder to enforce and can lead to inconsistent adherence to standards.
With a customization, the organization centrally manages and updates approved resources. This reduces operational overhead because updates to libraries, patterns, or guidelines propagate automatically to developers using the customization, without requiring changes to each project. This is especially valuable for a new team, where consistent enforcement of approved practices is important to reduce compliance risk, security issues, and inconsistent code style.
Additionally, customizations support governance by allowing the company to standardize how Amazon Q Developer responds, ensuring that suggestions reflect approved internal content rather than generic public patterns.
Therefore, Option D best satisfies the requirement for centralized enforcement of approved resources with minimal ongoing management and no project-level modifications.

NEW QUESTION # 98
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs).
A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear, it does not always flow with the rest of the text.
The GenAI developer must ensure that the promotional message always appears in the FM responses.
Which solution will meet this requirement?

A. Generate multiple response variants that include the promotional message in different ways. Use a reranker model to select the most coherent version based on relevance to the original prompt.
B. Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM.
C. Run the prompt through Amazon Bedrock. Process the response through Amazon Bedrock AgentCore to add the promotional message. Rerank the results by using the original prompt and the desired message as context.
D. Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH.

Answer: B

Explanation:
Option D is the intended Bedrock prompt-engineering solution. AWS guidance recommends using output indicators to explicitly describe constraints and characteristics that the model should follow when constructing its response. The prompt should clearly state that the promotional message is mandatory, where it should appear, and how it should be integrated with the surrounding recommendation. This gives the model a stronger output constraint than an indirect instruction embedded elsewhere in a dynamically assembled prompt. Option A uses Guardrails, which are designed to evaluate and constrain unsafe or disallowed content rather than require insertion of specific marketing text. Option B adds unnecessary model calls and still does not enforce the desired response structure. Option C introduces additional AgentCore and reranking processing for a requirement that should be addressed directly through explicit output instructions in the prompt. AWS Documentation

NEW QUESTION # 99
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?

A. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.
B. Configure an AWS Lambda function to fetch routing configuration from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
C. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
D. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.

Answer: B

NEW QUESTION # 100
A logistics company is building an agentic GenAI-powered solution to automate freight optimization. The solution must retrieve data in real time from multiple internal and external systems. The solution must include a human-in-the-loop approval step before the optimization process is finished. The solution must support modular growth as the number of integrations and amount of logic increases.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use an Amazon SageMaker AI endpoint that hosts a large language model (LLM) that directly calls all internal databases and external APIs. Use a custom web application that provides a UI to implement the human-in-the-loop review step.
B. Use a single Amazon Bedrock AgentCore agent with AWS Lambda-based tools to integrate with all internal and external systems. Use AWS Step Functions to orchestrate the approval workflow.
C. Use AWS Glue to aggregate operational data into Amazon S3. Use Amazon Athena to query the data.
Invoke an AWS Lambda function to generate route assignments. Use Amazon SNS to send notifications to supervisor agents.
D. Build a hierarchical system by using the Strands Agents SDK and Amazon Bedrock AgentCore.
Configure a coordinating agent to delegate tasks to multiple specialized agents. Use MCP to facilitate inter-agent messaging. Use AWS Step Functions to implement a human-in-the-loop approval step.

Answer: D

Explanation:
Option B provides the strongest modular architecture for an integration-heavy agentic workload. Strands supports hierarchical agents-as-tools patterns in which a coordinating agent delegates specialized work to independent agents, allowing integrations and domain logic to be added or modified without creating one increasingly complex agent. AgentCore Gateway exposes tools through standardized MCP interfaces so agents can discover and invoke internal or external capabilities through a unified connectivity layer. AWS Step Functions supports callback tasks that pause execution until a human or external process returns a task token, making it suitable for the required human approval checkpoint. Option A requires custom model hosting and approval UI development. Option C is primarily a batch data-processing architecture rather than real-time agent orchestration. Option D can function initially, but a single agent becomes less modular and maintainable as the number of tools and business domains expands. Strands Agents

NEW QUESTION # 101
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