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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Plan and Design a Generative AI Application	25%	- Design the generative AI solution architecture 1. Design for scalability, reliability, and cost-effectiveness 2. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker) 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. Determine data requirements and availability 2. Define functional and non-functional requirements 3. Identify the target audience and use cases 4. Identify constraints and risks - Select the appropriate foundation models (FMs) and techniques 1. Design prompt engineering strategies 2. Select model parameters and configurations 3. Evaluate foundation models for the use case 4. Consider fine-tuning vs. retrieval-augmented generation (RAG)
Topic 2: Optimize and Operationalize a Generative AI Application	30%	- 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 - 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. Implement caching strategies for frequent queries 3. Optimize token usage and manage costs 4. Optimize model selection and parameter tuning
Topic 3: Build and Implement a Generative AI Application	45%	- Implement data preprocessing and vectorization pipelines 1. Implement embeddings generation using AWS services 2. Ingest and transform data for model consumption 3. Implement document chunking and text processing 4. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) - Develop the application using AWS AI services 1. Implement prompt engineering and template management 2. Implement inference calls to Amazon Bedrock or other FMs 3. Implement retrieval-augmented generation (RAG) patterns 4. Integrate with knowledge bases - Implement security, compliance, and responsible AI 1. Implement content filtering and safety mechanisms 2. Ensure compliance with AI ethics and responsible use 3. Implement IAM roles and policies for AI services 4. Implement data encryption and privacy controls

Section

Weight

Objectives

Topic 1: Plan and Design a Generative AI Application

25%

- Design the generative AI solution architecture

1. Design for scalability, reliability, and cost-effectiveness
2. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)
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. Determine data requirements and availability
2. Define functional and non-functional requirements
3. Identify the target audience and use cases
4. Identify constraints and risks

- Select the appropriate foundation models (FMs) and techniques

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

Topic 2: Optimize and Operationalize a Generative AI Application

30%

- 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

- 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. Implement caching strategies for frequent queries
3. Optimize token usage and manage costs
4. Optimize model selection and parameter tuning

Topic 3: Build and Implement a Generative AI Application

45%

- Implement data preprocessing and vectorization pipelines

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

- Develop the application using AWS AI services

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

- Implement security, compliance, and responsible AI

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

>> Examcollection AIP-C01 Questions Answers <<

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q19-Q24):

NEW QUESTION # 19
A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarity to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must search documents in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type.
The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort while providing low-latency responses for real-time customer interactions.
Which solution will meet these requirements?

A. Use Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate S3 Vectors with Amazon Bedrock for RAG.
B. Use Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering.
Integrate with Amazon Bedrock Knowledge Bases to enable Retrieval Augmented Generation (RAG) using an Anthropic Claude foundation model.
C. Set up an Amazon Neptune Analytics database with a vector index. Use graph-based retrieval and Amazon Bedrock for response generation.
D. Deploy an Amazon Aurora PostgreSQL database with the pgvector extension. Store embeddings and metadata in tables. Use SQL queries for similarity search and send results to Amazon Bedrock for response generation.

Answer: B

Explanation:
Option A is the optimal solution because it provides scalable semantic search, rich metadata filtering, and tight integration with Amazon Bedrock while minimizing operational overhead. Amazon OpenSearch Serverless is designed for high-volume, low-latency search workloads and removes the need to manage clusters, capacity planning, or scaling policies.
With support for vector search and structured metadata filtering, OpenSearch Serverless enables efficient similarity search across 10 million embeddings while applying constraints such as language, publication date, regulatory agency, and document type. This is critical for financial services use cases where relevance and compliance depend on precise filtering.
Integrating OpenSearch Serverless with Amazon Bedrock Knowledge Bases enables a fully managed RAG workflow. The knowledge base handles embedding generation, retrieval, and context assembly, while Amazon Bedrock generates responses using a foundation model. This significantly reduces custom glue code and operational complexity.
Multilingual support is handled at the embedding and retrieval layer, allowing documents in English, Spanish, and Portuguese to be searched semantically without language-specific query logic. OpenSearch's distributed architecture ensures consistent low-latency responses for real-time customer interactions.
Option B increases operational overhead by requiring database tuning and scaling for vector workloads.
Option C does not support advanced metadata filtering, which is a key requirement. Option D introduces unnecessary complexity and is not optimized for large-scale semantic document retrieval.
Therefore, Option A best meets the requirements for performance, scalability, multilingual support, and minimal management effort in an Amazon Bedrock-based RAG application.

NEW QUESTION # 20
A company is building a generative AI (GenAI) application that produces content based on a variety of internal and external data sources. The company wants to ensure that the generated output is fully traceable.
The application must support data source registration and enable metadata tagging to attribute content to its original source. The application must also maintain audit logs of data access and usage throughout the pipeline.
Which solution will meet these requirements?

A. Store data in Amazon S3 and use object tagging for attribution. Use AWS Glue Data Catalog to manage schema information. Use AWS CloudTrail to log access to S3 buckets.
B. Use AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.
C. Use AWS Lake Formation to catalog data sources and control access. Apply metadata tags directly in Amazon S3. Use AWS CloudTrail to monitor API activity.
D. Use AWS Glue Data Catalog to register all data sources. Apply metadata tags to attribute data sources.Use AWS CloudTrail to log access and activity across services.

Answer: D

Explanation:
Option D is the correct solution because it directly satisfies all three core requirements: data source registration, metadata-based attribution, and end-to-end audit logging, while remaining service-agnostic and scalable across internal and external data sources.
The AWS Glue Data Catalog is the AWS-native service for registering datasets and managing metadata centrally. It supports structured registration of diverse data sources and enables consistent tagging that can be used to attribute generated content back to its original source. This is essential for GenAI applications that combine multiple datasets and must provide traceability for outputs.
Metadata tags applied within the Glue Data Catalog ensure a consistent attribution framework that downstream systems-such as Retrieval Augmented Generation (RAG) pipelines or evaluation systems-can reference without embedding attribution logic directly in application code. This improves maintainability and governance.
AWS CloudTrail provides immutable audit logs of API activity across AWS services, including data access, metadata changes, and pipeline interactions. CloudTrail logs are critical for compliance and regulatory review because they capture who accessed which data, when, and through which service. This satisfies the requirement to maintain audit logs "throughout the pipeline," not just at storage or application layers.
Option A introduces Lake Formation, which is primarily intended for fine-grained data lake permissions and is not required solely for traceability. Option B relies on CloudWatch Logs, which does not provide authoritative audit logging across services. Option C limits audit scope to S3 access and does not register or govern all data sources comprehensively.
Therefore, Option D provides the most complete and least intrusive solution for traceable, auditable GenAI data pipelines.

NEW QUESTION # 21
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity.
The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content.
Which solution will meet these requirements?

A. Configure a guardrail and set content filters to medium for harmful content. Set up denied topics for investment advice and include clear definitions and sample phrases to block. Configure sensitive information filters to mask PII in responses and to block financial information in inputs. Enable both input and output evaluations that use custom blocked messages for audits.
B. Configure a single guardrail and set content filters to high for all categories. Set up denied topics for investment advice and include sample phrases to block. Set up sensitive information filters that apply the block action for all PII entities. Apply the guardrail to all model inference calls.
C. Create a separate guardrail for each use case. Create one guardrail that applies a harmful content filter.Create a guardrail to apply topic filters for investment advice. Create a guardrail to apply sensitive information filters to block PII. Use AWS Step Functions to chain the guardrails sequentially.
D. Configure multiple guardrails by using tiered policies. Create one guardrail and set content filters to high. Configure the guardrail to block PII for public interactions. Configure a second guardrail and set content filters to medium. Configure the second guardrail to mask PII for internal use. Configure multiple topic-specific guardrails to block investment advice and set up contextual grounding checks.

Answer: A

Explanation:
Option C is the correct solution because it uses a single, well-tuned Amazon Bedrock guardrail that applies different actions to different content types, which is the recommended approach for minimizing false positives while enforcing strong policy controls.
Setting content filters to medium rather than high reduces overblocking of benign customer conversations while still preventing harmful content. Amazon Bedrock guardrails are designed to balance precision and recall, and medium sensitivity is commonly recommended for customer-facing financial services use cases.
Denied topics explicitly prevent the assistant from discussing investment advice, which is a regulatory requirement. Including definitions and sample phrases improves detection accuracy and reduces ambiguity.
Sensitive information filters support different actions per context. Masking PII in responses preserves conversational usefulness for legitimate customer support while preventing exposure of sensitive data.
Blocking sensitive financial information in inputs prevents downstream processing of disallowed content before it reaches the foundation model.
Critically, enabling both input and output evaluation ensures that guardrails are applied consistently at every stage of interaction. Custom blocked messages and audit logging provide clear compliance evidence for regulators and internal audits.
Option A causes excessive false positives by blocking all PII outright. Option B introduces unnecessary complexity and is not how Bedrock guardrails are intended to be applied. Option D uses orchestration logic that Bedrock guardrails already handle natively.
Therefore, Option C best satisfies enforcement, flexibility, auditability, and accuracy requirements.

NEW QUESTION # 22
A financial services company is developing a customer service AI assistant application that uses a foundation model (FM) in Amazon Bedrock. The application must provide transparent responses by documenting reasoning and by citing sources that are used for Retrieval Augmented Generation (RAG). The application must capture comprehensive audit trails for all responses to users. The application must be able to serve up to
10,000 concurrent users and must respond to each customer inquiry within 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Enable tracing for Amazon Bedrock agents. Integrate a custom RAG pipeline with Amazon OpenSearch Service to retrieve and cite sources. Configure structured prompts to present retrieved evidence. Deploy the application behind an Amazon API Gateway REST API. Use AWS Lambda functions and Amazon CloudFront to scale the application and to provide low latency. Store logs in Amazon S3 and use AWS CloudTrail to capture audit trails.
B. Store generated responses and supporting evidence in an Amazon S3 bucket. Enable versioning on the bucket for audits. Use AWS Glue to catalog retrieved documents. Process the retrieved documents in Amazon Athena to generate periodic compliance reports.
C. Use Amazon CloudWatch to monitor latency and error rates. Embed model prompts directly in the application backend to cite sources. Store application interactions with users in Amazon RDS for audits.
D. Enable tracing for Amazon Bedrock Agents. Configure structured prompts that direct the FM to provide evidence presentations. Integrate Amazon Bedrock Knowledge Bases with data sources to enable RAG.
Configure the application to reference and cite authoritative content. Deploy the application in a Multi- AZ architecture. Use Amazon API Gateway and AWS Lambda functions to scale the application. Use Amazon CloudFront to provide low-latency delivery.

Answer: D

Explanation:
Option A is the correct solution because it relies on native Amazon Bedrock capabilities to deliver transparency, auditability, scalability, and low latency with minimal operational overhead. Amazon Bedrock Knowledge Bases provide a fully managed Retrieval Augmented Generation (RAG) implementation that automatically handles document ingestion, embedding, retrieval, and source attribution, enabling the application to cite authoritative content without building custom pipelines.
Enabling tracing for Amazon Bedrock Agents provides end-to-end visibility into agent reasoning steps, tool usage, and model interactions. This satisfies the requirement for comprehensive audit trails and supports regulatory review in financial services environments. Structured prompts further ensure that responses explicitly present reasoning and supporting evidence in a controlled, auditable format.
Using Amazon API Gateway and AWS Lambda allows the application to scale automatically to thousands of concurrent users without capacity planning. These services are designed for bursty workloads and can easily support the stated requirement of up to 10,000 concurrent users. Amazon CloudFront reduces latency by caching and accelerating content delivery, helping the application meet the strict 2-second response-time requirement.
Option B introduces a custom RAG pipeline with OpenSearch, increasing operational complexity and maintenance effort. Option C lacks native RAG integration and does not provide transparent reasoning or citation management. Option D focuses on offline compliance reporting rather than real-time transparency and low-latency responses.
Therefore, Option A best meets all requirements while minimizing infrastructure and operational overhead.

NEW QUESTION # 23
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. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.
B. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.
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: A

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 # 24
......

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