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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: Plan and Design a Generative AI Application

25%

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

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

- Select the appropriate foundation models (FMs) and techniques

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

- Design the generative AI solution architecture

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

Topic 2: Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- 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. Ingest and transform data for model consumption
3. Implement embeddings generation using AWS services
4. Implement document chunking and text processing

Topic 3: Optimize and Operationalize a Generative AI Application

30%

- Implement monitoring, logging, and evaluation

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

- Optimize costs and performance

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

- Implement CI/CD and automation for AI applications

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q121-Q126):

NEW QUESTION # 121
A financial services company is deploying a GenAI assistant that uses Amazon Bedrock to answer customer questions about account activity and company policies. The company must comply with responsible AI principles and governance requirements.
The solution must meet the following requirements:
* Prevent harmful, biased, or non-compliant responses.
* Ensure consistent policy enforcement across all model invocations.
* Provide traceability and auditability for AI-generated outputs.
* Maintain developer productivity without embedding complex safety logic in application code.
Which solution will meet these requirements?

A. Implement post-processing checks in AWS Lambda functions to manually review and redact model outputs before returning responses to users.
B. Use prompt engineering techniques and system prompts to guide the model ' s behavior.
C. Use Amazon Bedrock Guardrails to enforce content policies. Configure response filtering and topic restrictions. Configure logging for model interactions.
D. Log all prompts and responses to Amazon S3. Conduct periodic human reviews to identify violations of responsible AI principles.

Answer: C

Explanation:
Amazon Bedrock Guardrails is the AWS-managed mechanism specifically intended to implement consistent generative-AI safeguards without forcing developers to reproduce moderation logic throughout application code. Guardrails supports configurable content filters for categories including hate, insults, sexual content, violence, misconduct, and prompt attacks. It also supports denied topics, custom word filters, and sensitive- information filters for PII and other patterns.
For a financial-services assistant, denied topics can prevent conversations outside approved policy boundaries, while content filtering can prevent harmful or inappropriate responses. Sensitive-information controls can detect and filter regulated or personally identifiable information. Because a guardrail configuration is applied as a managed policy during inference, the same safeguards can be applied consistently across invocations rather than depending on every developer to implement identical conditional logic correctly.
Logging model interactions adds the required traceability layer. Operational records of prompts, model invocations, blocked interactions, and responses can support investigations, compliance review, and responsible-AI governance. This is materially stronger than merely establishing behavioral expectations in prompt text.
B is insufficient because system prompts are instructions to the model rather than an independent enforcement mechanism. A model can still generate undesirable content or encounter adversarial inputs despite well- engineered prompts. C can implement controls but creates custom application logic that must be maintained, tested, and kept synchronized across every application path, which conflicts with the productivity requirement. D is entirely retrospective; periodic review can identify violations after they occur but does not prevent an unsafe response from reaching the customer.
The architectural principle is to separate deterministic governance controls from application prompting.
Guardrails supplies a reusable policy layer around foundation-model interactions, while centralized logging provides an auditable operational record. Consequently, A satisfies safety enforcement, consistency, governance, auditability, and developer-productivity requirements together.

NEW QUESTION # 122
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses. The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes. Which solution will meet these requirements?

A. Deploy all three models to a single Amazon SageMaker AI multi-model endpoint. Enable dynamic scaling on the endpoint. Use a compute optimized instance type. Configure auto scaling policies that are based on invocation metrics to handle peak loads.
B. Deploy each model to a separate Amazon SageMaker Serverless Inference endpoint. Set provisioned concurrency to handle peak loads. Configure maximum concurrency limits and memory sizing based on each model ' s specific requirements.
C. Deploy each model to a separate Amazon SageMaker AI endpoint. Use an asynchronous inference configuration. Store model requests and responses in Amazon S3. Use Amazon SNS to send alerts to users when the application finishes processing requests.
D. Deploy the models by using Amazon Bedrock with provisioned throughput to handle peak loads.
Configure the number of model units (MUs) based on expected token throughput needs. Implement request batching for each model.

Answer: B

Explanation:
Amazon SageMaker Serverless Inference is specifically designed for applications that experience intermittent or bursty traffic. It automatically scales compute capacity based on the number of requests and scales down to zero when there is no traffic, satisfying the requirement to optimize resource usage during low demand. To meet the 500 ms latency requirement during peak periods and avoid " cold start " delays, provisioned concurrency keeps a specified number of execution environments warm and ready to respond immediately. This provides a balance between the cost-effectiveness of serverless and the performance predictability of provisioned instances. Multi-model endpoints (Option A) can introduce " noisy neighbor " issues and latency spikes, while asynchronous inference (Option D) is intended for long-running workloads and cannot meet sub-500 ms requirements.

NEW QUESTION # 123
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?

A. Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain 1,000 tokens and child chunks that contain 200 tokens. Set a 50-token overlap between chunks.
B. Configure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.
C. Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.
D. Configure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size and a
10% overlap between chunks. Use an appropriate Amazon Bedrock embedding model.

Answer: A

NEW QUESTION # 124
A company is using Amazon Bedrock to build a GenAI assistant that answers employee questions based on internal documentation. The company stores documents in Amazon S3, Atlassian Confluence, and an internal wiki system. The GenAI assistant must retrieve relevant content and provide grounded responses.
The solution must meet the following requirements:
* Integrate multiple document sources into a single retrieval layer.
* Support semantic search rather than keyword-only queries.
* Minimize custom ingestion and synchronization logic.
* Ensure that retrieved content can be directly used to augment the GenAI assistant ' s foundation model (FM).
Which solution will meet these requirements?

A. Index documents from the source documents into Amazon OpenSearch Service by using keyword mappings. Invoke the FM and manually select search results.
B. Use Amazon Bedrock Knowledge Bases and managed data connectors to ingest content from the source documents. Enable semantic retrieval to augment the FM.
C. Store the source documents in Amazon DynamoDB. Query the DynamoDB table directly to provide contextual input to the FM.
D. Store the source documents in Amazon S3. Use AWS Lambda functions to generate embeddings.
Implement custom retrieval logic in the GenAI assistant application layer.

Answer: B

Explanation:
Amazon Bedrock Knowledge Bases is designed specifically for managed retrieval-augmented generation. A knowledge base connects source repositories to an embedding model and vector store. During ingestion, source content is transformed into numerical vector embeddings. At retrieval time, the query is similarly represented so the knowledge base can compare semantic similarity and return passages that are conceptually relevant rather than depending exclusively on exact keyword matches.
Amazon Bedrock supports managed data-source connectors for multiple repositories. The AWS documentation lists Amazon S3, Confluence, Microsoft SharePoint, Salesforce, web crawling, and custom data sources among supported connection patterns. These connectors reduce the amount of ingestion, crawling, and synchronization code an organization needs to maintain.
For Atlassian Confluence specifically, Bedrock can crawl supported Confluence content and supports incremental synchronization for added, modified, or deleted material. Current managed Confluence integration also supports crawling pages, blog posts, and attachments, subject to the documented connector limitations.
This architecture supplies the retrieval layer needed to augment an FM with source-grounded context. It centralizes retrieval semantics while letting the application use Bedrock ' s managed ingestion and retrieval capabilities instead of implementing its own embedding pipelines.
B explicitly uses keyword-oriented mappings and therefore does not meet the semantic-search requirement. C could technically implement RAG, but Lambda-based embedding generation, synchronization, and custom retrieval logic create precisely the operational burden the company wants to avoid. D is not an appropriate semantic RAG architecture because ordinary DynamoDB queries do not automatically produce vector-based semantic retrieval from unstructured documentation.
For an internal wiki not covered by a built-in connector, the knowledge-base custom data-source capability can be used while keeping the overall retrieval interface centralized. Therefore, A provides the closest fit to every stated architectural requirement.

NEW QUESTION # 125
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?

A. Configure SAML federation between Microsoft Entra ID and AWS Identity and Access Management.
Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.
B. Configure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute- based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID's built-in logging capabilities to maintain an audit trail of model interactions.
C. Create a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles' access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.
D. Create an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID.
Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints.
Enable CloudWatch Logs to capture model interactions for auditing purposes.

Answer: A

NEW QUESTION # 126
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