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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. 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 - Implement CI/CD and automation for AI applications 1. Implement version control for prompts and models 2. Implement testing strategies for generative AI applications 3. Manage model updates and rollback strategies 4. Automate deployment of AI models and applications - Optimize costs and performance 1. Implement caching strategies for frequent queries 2. Implement auto-scaling for AI workloads 3. Optimize token usage and manage costs 4. Optimize model selection and parameter tuning
Topic 2: Build and Implement a Generative AI Application	45%	- 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. Integrate with knowledge bases 4. Implement retrieval-augmented generation (RAG) patterns - Implement security, compliance, and responsible AI 1. Implement data encryption and privacy controls 2. Implement content filtering and safety mechanisms 3. Implement IAM roles and policies for AI services 4. Ensure compliance with AI ethics and responsible use - Implement data preprocessing and vectorization pipelines 1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) 2. Implement document chunking and text processing 3. Ingest and transform data for model consumption 4. Implement embeddings generation using AWS services
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. Design prompt engineering strategies 3. Consider fine-tuning vs. retrieval-augmented generation (RAG) 4. Evaluate foundation models for the use case - Design the generative AI solution architecture 1. Design the end-to-end solution architecture 2. Design for scalability, reliability, and cost-effectiveness 3. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker) 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. Define functional and non-functional requirements 2. Determine data requirements and availability 3. Identify the target audience and use cases 4. Identify constraints and risks

Section

Weight

Objectives

Topic 1: Optimize and Operationalize a Generative AI Application

30%

- 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

- Implement CI/CD and automation for AI applications

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

- Optimize costs and performance

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

Topic 2: Build and Implement a Generative AI Application

45%

- 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. Integrate with knowledge bases
4. Implement retrieval-augmented generation (RAG) patterns

- Implement security, compliance, and responsible AI

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

- Implement data preprocessing and vectorization pipelines

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

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. Design prompt engineering strategies
3. Consider fine-tuning vs. retrieval-augmented generation (RAG)
4. Evaluate foundation models for the use case

- Design the generative AI solution architecture

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
A pharmaceutical company is developing a Retrieval Augmented Generation 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 not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.
B. 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.
C. 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.
D. 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.

Answer: C

Explanation:
Option B is the best fit because hierarchical chunking is designed to preserve local detail while keeping broader document context available during retrieval, which directly addresses the problem of questions spanning methodology, results, and discussion. In large scientific papers, a single answer often depends on linked paragraphs across adjacent sections. If the knowledge base retrieves only small, isolated chunks, the RAG system can cite text that is semantically close to a query term but not contextually correct, producing inconsistent answers and irrelevant citations.
With hierarchical chunking, the knowledge base creates child chunks that are small enough for high- precision vector similarity matching, such as 200 tokens, which improves the likelihood that the retrieved text is tightly related to the user's query. At the same time, each child chunk is associated with a larger parent chunk, such as 1,000 tokens, which retains the surrounding narrative and section-level context. This structure helps the retrieval pipeline return passages that include the relevant subsection plus the explanatory framing that prevents misinterpretation, which is especially important in scientific writing where methods, results, and discussion are interdependent.
The configured overlap further reduces boundary effects where key statements split across chunks. This improves continuity for paragraphs that bridge sections, such as a results paragraph that references the methodological setup or a discussion paragraph interpreting a specific metric.
Option A can improve consistency slightly, but fixed-size chunking still risks separating related paragraphs and does not provide a built-in mechanism to retrieve broader context linked to precise matches. Option C can create more meaningful boundaries, but it does not guarantee the parent-level context that hierarchical chunking provides at retrieval time. Option D increases operational burden and is not practical at the scale of
25 million

NEW QUESTION # 67
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%.
A GenAI developer must implement a near real-time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?

A. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.
B. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations.
Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock Guardrails to enable contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.
D. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.

Answer: C

Explanation:
Option C combines AWS-native capabilities for the three required monitoring dimensions with minimal custom code. Amazon Bedrock model invocation logging can capture request and response information and deliver invocation data to Amazon S3 or CloudWatch Logs. Bedrock Guardrails contextual grounding checks are specifically designed to detect and filter responses that are not sufficiently grounded in supplied source information; AWS explicitly lists summarization as a supported use case. CloudWatch provides token-usage observability, while anomaly-detection mechanisms can establish adaptive baselines instead of requiring manually maintained static thresholds. This enables earlier detection of unusual consumption and potential cost anomalies. Option A requires custom Athena-based hallucination analysis. Option B relies on evaluation jobs and custom Lambda processing rather than near-real-time managed controls. Option D combines services that do not directly provide the required Bedrock-specific hallucination monitoring. AWS Documentation

NEW QUESTION # 68
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 not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.
B. 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.
C. 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.
D. 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.

Answer: C

NEW QUESTION # 69
A healthcare company uses a multi-agent system on Amazon Bedrock AgentCore. The system uses multiple FMs to process 2,000 medical report documents daily. The documents range from 5 to 50 pages each.
The company discovers that the system is not performing complete analysis on documents that exceed 30 pages. Documents over 30 pages miss critical information from later sections. The results are truncated, but there are no explicit errors.
The company must resolve this issue while maintaining response times under 10 seconds. The company must keep processing costs low.
Which solution will meet these requirements?

A. Use Amazon CloudWatch Logs Insights queries to analyze Amazon Bedrock agent processing times.
Increase the timeout settings for the Lambda functions that host the agents. Implement pagination for larger documents to process in sequential batches.
B. Implement document chunking with semantic overlap between chunks where content from the end of one chunk appears at the beginning of the next chunk. Configure Amazon CloudWatch metrics to monitor InputTokenCount. Set up a CloudWatch dashboard for context utilization percentage.
Configure diagnostic logging that identifies truncation points in the processing flow.
C. Deploy the system in a higher memory configuration with a larger compute instance size to accommodate complete documents in memory. Add Amazon Comprehend Medical to identify and prioritize critical medical entities for improved context management.
D. Configure agent action groups to process documents sequentially by automatically creating separate tasks for each document section. Set up an AWS Step Functions workflow to coordinate processing.
Combine the results from individual sections.

Answer: B

Explanation:
Option B is correct because the symptoms indicate context-window pressure rather than a compute-memory or Lambda-timeout problem. Large documents should be split into manageable chunks while preserving semantic continuity across boundaries. Amazon Bedrock Knowledge Bases supports chunking strategies, including semantic chunking and overlap-oriented approaches, so relevant context can be retained without sending an entire long report to the model at once. Monitoring InputTokenCount provides evidence of context utilization and helps identify when requests approach model limits. Diagnostic logging can then locate where truncation occurs. Option A increases timeouts but does not enlarge the model context window. Option C adds sequential orchestration that can jeopardize the 10-second target. Option D increases infrastructure resources, but host memory does not change a foundation model's maximum context window and adds unnecessary cost. AWS Documentation

NEW QUESTION # 70
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 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.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 71
......

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