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Amazon AIP-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Plan and Design a Generative AI Application25%- 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
- Select the appropriate foundation models (FMs) and techniques
  • 1. Select model parameters and configurations
  • 2. Evaluate foundation models for the use case
  • 3. Design prompt engineering strategies
  • 4. Consider fine-tuning vs. retrieval-augmented generation (RAG)
- 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)
Topic 2: Build and Implement a Generative AI Application45%- Develop the application using AWS AI services
  • 1. Implement prompt engineering and template management
  • 2. Integrate with knowledge bases
  • 3. Implement retrieval-augmented generation (RAG) patterns
  • 4. Implement inference calls to Amazon Bedrock or other FMs
- Implement data preprocessing and vectorization pipelines
  • 1. Ingest and transform data for model consumption
  • 2. Implement embeddings generation using AWS services
  • 3. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
  • 4. Implement document chunking and text processing
- Implement security, compliance, and responsible AI
  • 1. Implement content filtering and safety mechanisms
  • 2. Implement IAM roles and policies for AI services
  • 3. Implement data encryption and privacy controls
  • 4. Ensure compliance with AI ethics and responsible use
Topic 3: Optimize and Operationalize a Generative AI Application30%- Implement CI/CD and automation for AI applications
  • 1. Manage model updates and rollback strategies
  • 2. Implement version control for prompts and models
  • 3. Implement testing strategies for generative AI applications
  • 4. Automate deployment of AI models and applications
- 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. 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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset with a low index count. The application must perform similarity searches.
The Amazon Bedrock model's responses must maximize accuracy and maintain high performance.
The company needs to configure the vector database and integrate it with the application.
Which solution will meet these requirements?

Answer: B

Explanation:
Option B is the optimal solution because it maximizes similarity search accuracy and performance for a small, proprietary dataset while maintaining low operational complexity. Amazon MemoryDB is a fully managed, in- memory database that provides microsecond-level latency, making it ideal for real-time RAG workloads that require fast vector similarity searches.
For small datasets with low index counts, the Hierarchical Navigable Small World (HNSW) algorithm is recommended by AWS for its high recall and accuracy. Unlike approximate methods optimized for massive datasets, HNSW excels at returning the most semantically relevant vectors with minimal loss of precision, which directly improves the quality of responses generated by the Amazon Bedrock foundation model.
Vertical scaling in MemoryDB is sufficient for this use case because the dataset size is limited. Scaling up instance size provides increased memory and compute capacity without the complexity of managing distributed indexes or sharding strategies. This simplifies operations while maintaining predictable performance.
Option A's Flat algorithm is computationally expensive and inefficient at scale, even for moderate query volumes. Option C introduces higher latency and operational overhead by using a relational database not optimized for in-memory vector search. Option D is unsuitable because Amazon DocumentDB is not designed for high-performance vector similarity workloads and introduces unnecessary replica management complexity.
Therefore, Option B best meets the requirements for accuracy, performance, and efficient integration with an Amazon Bedrock-based RAG application.


NEW QUESTION # 53
A company is using AWS Lambda and REST APIs to build a reasoning agent to automate support workflows.
The system must preserve memory across interactions, share relevant agent state, and support event-driven invocation and synchronous invocation. The system must also enforce access control and session-based permissions.
Which combination of steps provides the MOST scalable solution? (Select TWO.)

Answer: C,E

Explanation:
The combination of Options A and B provides the most scalable and AWS-native architecture for building reasoning agents with persistent memory, session awareness, secure access control, and flexible invocation models.
Amazon Bedrock AgentCore is purpose-built to manage agent memory, session context, and identity-aware reasoning across interactions. It eliminates the need for developers to manually store and retrieve agent state, manage session lifecycles, or implement custom memory layers. AgentCore natively supports both synchronous requests and event-driven execution, making it ideal for support workflow automation.
Option B complements AgentCore by enabling seamless tool invocation. By registering AWS Lambda functions and REST APIs as agent actions through API Gateway and EventBridge, the agent can invoke tools reactively or synchronously without custom orchestration code. EventBridge enables event-driven execution, while API Gateway supports synchronous request-response patterns.
This combination provides built-in security, observability, and scaling, while avoiding the operational burden of managing queues, databases, or custom workflow engines.
Option C introduces unnecessary orchestration complexity. Option D increases infrastructure management and cost. Option E stores agent state in S3, which is not suitable for low-latency, session-based reasoning.
Therefore, A and B together deliver the most scalable, secure, and low-overhead solution for production- grade reasoning agents on AWS.


NEW QUESTION # 54
A GenAI developer is building a RAG system that uses Amazon Bedrock Knowledge Bases. The system needs to process 50 textbooks that are stored in an Amazon S3 bucket. The textbooks are all an average of
500 pages long. The system needs to generate a knowledge base to answer domain-specific customer questions.
During initial testing on a subset of documents, the GenAI developer notices that query responses contain irrelevant information and sometimes miss critical context from the source materials. The GenAI developer must ensure that the solution provides accurate responses with low latency and no hallucinations.
Which solution will meet these requirements?

Answer: A

Explanation:
Option C is correct because the issue is retrieval quality: responses contain irrelevant information and sometimes miss important surrounding context. Amazon Bedrock Knowledge Bases supports hierarchical chunking with parent and child chunks. In this pattern, smaller child chunks improve precise retrieval, while larger parent chunks provide broader surrounding context when returned for generation. AWS documentation states that hierarchical chunking supports two levels: a parent chunk size and a child chunk size, with configurable overlap. This directly addresses the textbook problem because dense technical materials often need both fine-grained matching and enough context to avoid incomplete or misleading answers.
Hybrid search is also the strongest retrieval option for domain-specific textbooks. AWS documentation for Knowledge Bases explains that HYBRID search uses both vector embeddings and raw text, while SEMANTIC search uses only vector embeddings. For technical content, equations, terms, acronyms, product names, and exact phrases may be missed by pure semantic search. Hybrid search reduces this risk by combining semantic similarity with keyword matching. The Knowledge Base vector search configuration also supports controls such as number of source chunks, metadata filters, search type override, and reranking configuration, all of which help tune retrieval quality.
Adjusting the relevance threshold, or filtering low-confidence retrieval results in the application layer, helps reduce hallucinations because the model should not generate answers from weak or unrelated context. The key GenAI principle is that RAG quality depends on retrieving the right evidence before generation. If irrelevant chunks are passed to the FM, the FM can produce irrelevant or unsupported answers.
Option A uses very small fixed chunks, which can miss context across sections. Option B adds caching and feedback loops but does not fix the underlying retrieval issue. Option D's semantic chunking can help topic boundaries, but it does not explicitly solve exact-term retrieval or low-confidence filtering. Therefore, hierarchical chunking plus hybrid search and relevance filtering is the best answer.


NEW QUESTION # 55
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company ' s data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application ' s decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?

Answer: D

Explanation:
Option C provides the strongest combination of residency controls, data classification, retention protection, and auditability. Keeping source data in Region-specific S3 buckets and processing it according to geographic origin establishes a clear architectural boundary around data belonging to Europe, North America, and Asia.
Amazon S3 Object Lock provides write-once-read-many protection and can prevent protected object versions from being overwritten or deleted, which is particularly valuable for regulated records and audit evidence.
Compliance mode provides especially strong retention enforcement.
Amazon Macie provides the required classification capability. Macie performs automated sensitive-data discovery against Amazon S3 and produces classification results describing whether analyzed objects contain sensitive information. It also calculates sensitivity scores and labels for monitored S3 buckets, supporting governance and compliance workflows.
AWS CloudTrail supplies auditable records of AWS API activity. When CloudTrail log-file integrity validation is enabled, CloudTrail uses SHA-256 hashing and digital signatures so modifications or deletions of delivered logs can be detected. Combined with protected S3 storage, this provides a strong evidentiary audit trail.
AWS now also supports geographic cross-Region inference profiles for Amazon Bedrock that keep inference processing within defined geographic boundaries such as the US, EU, and APAC. However, option A merely states "cross-Region inference" and lacks the required classification service while relying on manual compliance tracking. Geographic residency must be explicitly controlled rather than assumed.
B does not provide a data-classification mechanism. D creates significantly greater administrative burden and depends on custom monitoring and manual reporting. Therefore, C best satisfies the combined residency, classification, audit, and compliance requirements.


NEW QUESTION # 56
A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type.
Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing.
The company needs a solution to fix these performance issues and enhance the user experience.
Which solution will meet these requirements?

Answer: C

Explanation:
Option A is the best solution because it directly addresses both observed problems: user-perceived latency and resolver timeouts that occur more frequently for complex prompts. In the current design, an AWS AppSync Lambda resolver is configured with synchronous RequestResponse behavior. That means the client receives nothing until the entire retrieval and generation workflow completes. For longer-running knowledge base queries, this increases the likelihood of hitting request time limits in the synchronous path and creates a poor user experience because the UI appears stalled.
Using AWS Amplify AI Kit to implement streaming responses allows the application to return partial output incrementally as the model produces tokens. This improves perceived responsiveness because users can see the answer forming immediately, even when the full response takes longer. Streaming also reduces the impact of variable model latency and retrieval time because the client no longer waits for a single final payload before rendering content. From a troubleshooting perspective, streaming makes it easier to distinguish "slow generation" from "no response," and it provides faster feedback during testing of complex questions.
Option B is not sufficient because increasing timeouts and adding retries can worsen load and cost while still producing a stalled UI experience. Retries also risk duplicating requests to the knowledge base and can amplify token usage. Option C introduces an awkward polling model for GraphQL interactions and adds significant operational complexity, while not inherently improving interactivity. Option D adds major architectural changes by replacing the knowledge base RetrieveAndGenerate call path with a different streaming invocation API and introducing a WebSocket layer, which is unnecessary when the goal is primarily to fix timeouts and improve UX within the existing AppSync and Amplify design.
Therefore, streaming through Amplify AI Kit is the most effective and lowest-friction improvement.
Thought for 24s


NEW QUESTION # 57
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