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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Plan and Design a Generative AI Application

25%

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

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

- Design the generative AI solution architecture

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

- Select the appropriate foundation models (FMs) and techniques

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

Optimize and Operationalize a Generative AI Application

30%

- Implement CI/CD and automation for AI applications

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

- 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 monitoring, logging, and evaluation

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

Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- Implement security, compliance, and responsible AI

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

- Implement data preprocessing and vectorization pipelines

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q110-Q115):

NEW QUESTION # 110
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 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.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 111
An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs when processing documents that exceed the FM's context window limits.
Which solution will resolve this problem?

A. Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the Lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.
B. Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000 tokens is reached before making inference calls.
C. Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences.
Use the RetrieveAndGenerate API to dynamically select the most relevant chunks based on embedding similarity scores.
D. Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.

Answer: C

Explanation:
Option C directly addresses the root cause of truncated and inconsistent responses by using AWS- recommended semantic chunking and dynamic retrieval rather than static or sequential chunk processing.
Amazon Bedrock documentation emphasizes that foundation models have fixed context windows and that sending oversized or poorly structured input can lead to truncation, loss of context, and degraded output quality.
Semantic chunking breaks documents based on meaning instead of fixed token counts. By using a breakpoint percentile threshold and sentence buffers, the content remains coherent and semantically complete. This approach reduces the likelihood that important concepts are split across chunks, which is a common cause of inconsistent summarization results.
The RetrieveAndGenerate API is designed specifically to handle large documents that exceed a model's context window. Instead of forcing all content into a single inference call, the API generates embeddings for chunks and dynamically selects only the most relevant chunks based on similarity to the user query. This ensures that the FM receives only high-value context while staying within its context window limits.
Option A is ineffective because chaining chunks sequentially does not align with how FMs process context and risks exceeding context limits or introducing irrelevant information. Option B improves structure but still relies on larger parent chunks, which can lead to inefficiencies when processing very large documents. Option D processes segments independently, which often causes loss of global context and inconsistent summaries.
Therefore, Option C is the most robust, AWS-aligned solution for resolving truncation and consistency issues when processing large technical documents with Amazon Bedrock.

NEW QUESTION # 112
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 semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.
B. Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.
C. 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.
D. 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.

Answer: D

NEW QUESTION # 113
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity.
Which solution will meet these requirements?

A. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the FM in the nearest secondary Region when quotas are reached.
B. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when calling the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.
C. Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts when usage approaches thresholds.
D. Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in application code to switch Regions when throttling occurs. Use AWS Global Accelerator to route traffic based on user location.

Answer: B

Explanation:
Option B is the most appropriate solution because it directly uses Amazon Bedrock cross-Region inference profiles, which are designed to provide resilience and load distribution while respecting data residency boundaries. Cross-Region inference profiles allow applications to distribute inference requests across multiple Regions within a defined geographic boundary, such as Europe or North America, without requiring custom failover logic.
By specifying geographical codes in the inference profile ID, the application ensures that European user data is processed only within Europe-based Regions, satisfying regulatory requirements. At the same time, Bedrock automatically routes requests to healthy Regions within that geography when traffic spikes or service quotas are reached, improving availability and maintaining low latency.
Using separate Amazon API Gateway HTTP APIs for Europe and North America provides a clean, simple routing layer that directs users to the appropriate regional inference profile. This avoids complex custom routing or retry logic in application code and minimizes operational overhead.
Option A relies on custom routing and manual monitoring, which increases complexity and does not provide automatic resilience. Option C introduces custom retry and fallback logic that risks violating data residency requirements if misconfigured. Option D requires significant application-level failover logic and adds operational burden with Global Accelerator configuration.
Therefore, Option B best meets the requirements for low latency, data residency compliance, resilience during traffic spikes, and minimal operational complexity.

NEW QUESTION # 114
A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture.
The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries.
Which solution will meet these requirements?

A. Isolate data for each department in separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a single general-purpose agent. Configure multiple action groups within the general-purpose agent to perform specific department functions. Implement rule-based routing logic in the general-purpose agent instructions.
B. Isolate data for each agent by using separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a supervisor agent to perform natural language intent classification on patient inquiries. Configure the supervisor agent to route queries to specialized collaborator agents to respond to department-specific queries. Configure each specialized collaborator agent to use Retrieval Augmented Generation (RAG) with the agent's department-specific knowledge base.
C. Create a separate supervisor agent for each department. Configure individual collaborator agents to perform natural language intent classification for each specialty domain within each department.
Integrate each collaborator agent with department-specific knowledge bases only. Implement manual handoff processes between the supervisor agents.
D. Implement multiple independent supervisor agents that run in parallel to respond to patient inquiries for each department. Configure multiple collaborator agents for each supervisor agent. Integrate all agents with the same knowledge base. Use external routing logic to merge responses from multiple supervisor agents.

Answer: B

Explanation:
Option A best meets the requirements because it applies an AWS-aligned multi-agent pattern that cleanly separates responsibilities: a supervisor agent performs intent classification and orchestration, while specialized collaborator agents handle domain-specific tasks using the right knowledge sources. This structure is well suited for healthcare workflows where clinical questions, scheduling, and insurance processes require different policies, terminology, and data access boundaries.
The requirement for appropriate domain-specific responses is addressed by routing each user query to a department-focused collaborator agent that is grounded with its own department-specific knowledge base.
Using Retrieval Augmented Generation with the correct knowledge base improves factual alignment and reduces cross-department leakage (for example, avoiding claims content in a clinical answer). It also supports better prompt grounding and more consistent tone and constraints per department.
The requirement to isolate data maps to using separate knowledge bases per agent and enforcing access through IAM controls, ensuring that each agent can retrieve only from the authorized datasets. This is important for minimizing unintended exposure of sensitive or irrelevant departmental data and supports governance and compliance needs.
For scalability and thousands of parallel interactions, this architecture minimizes contention and bottlenecks. Each collaborator agent can scale independently because requests are distributed across multiple agents and multiple retrieval backends. Operationally, onboarding new features is also simpler: the company can add a new collaborator agent (for example, "billing disputes" or "pharmacy refills") with its own knowledge base and policies without redesigning the entire assistant.
Option B introduces unnecessary complexity with multiple supervisors and manual handoffs. Option C overloads a single agent with broad instructions and rule-based routing, which increases prompt complexity and reduces maintainability as features grow. Option D creates high operational complexity and risks inconsistent outputs when merging responses from parallel supervisors, and it weakens data isolation by using a shared knowledge base across agents.

NEW QUESTION # 115
......

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