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Amazon AIP-C01 Exam Syllabus Topics:

SectionWeightObjectives
Build and Implement a Generative AI Application45%- Develop the application using AWS AI services
  • 1. Implement inference calls to Amazon Bedrock or other FMs
  • 2. Integrate with knowledge bases
  • 3. Implement prompt engineering and template management
  • 4. Implement retrieval-augmented generation (RAG) patterns
- Implement data preprocessing and vectorization pipelines
  • 1. Ingest and transform data for model consumption
  • 2. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
  • 3. Implement document chunking and text processing
  • 4. Implement embeddings generation using AWS services
- Implement security, compliance, and responsible AI
  • 1. Implement data encryption and privacy controls
  • 2. Implement content filtering and safety mechanisms
  • 3. Ensure compliance with AI ethics and responsible use
  • 4. Implement IAM roles and policies for AI services
Plan and Design a Generative AI Application25%- Select the appropriate foundation models (FMs) and techniques
  • 1. Consider fine-tuning vs. retrieval-augmented generation (RAG)
  • 2. Select model parameters and configurations
  • 3. Evaluate foundation models for the use case
  • 4. Design prompt engineering strategies
- Identify and define the business and technical requirements for a generative AI application
  • 1. Identify the target audience and use cases
  • 2. Define functional and non-functional requirements
  • 3. Identify constraints and risks
  • 4. Determine data requirements and availability
- 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
Optimize and Operationalize a Generative AI Application30%- Optimize costs and performance
  • 1. Implement auto-scaling for AI workloads
  • 2. Optimize token usage and manage costs
  • 3. Optimize model selection and parameter tuning
  • 4. Implement caching strategies for frequent queries
- Implement CI/CD and automation for AI applications
  • 1. Implement testing strategies for generative AI applications
  • 2. Automate deployment of AI models and applications
  • 3. Implement version control for prompts and models
  • 4. Manage model updates and rollback strategies
- Implement monitoring, logging, and evaluation
  • 1. Implement human-in-the-loop evaluation workflows
  • 2. Monitor model performance and application metrics
  • 3. Evaluate model outputs for quality and bias
  • 4. Implement logging for prompts and responses

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q145-Q150):

NEW QUESTION # 145
A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company's employees. The application must display AI- generated responses character by character as the responses are generated. The application needs to support thousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish.
Which solution will meet these requirements?

Answer: C

Explanation:
This requirement explicitly calls for character-by-character streaming, long-running responses, low latency, and massive concurrency, which aligns directly with Amazon Bedrock streaming inference patterns.
Amazon Bedrock provides the InvokeModelWithResponseStream API specifically for streaming partial model outputs as tokens are generated. This enables near-instant feedback to users instead of waiting for the full response to complete, which is essential when responses last up to 45 seconds.
Amazon API Gateway WebSocket APIs are purpose-built for bidirectional, low-latency, server-initiated communication, allowing the backend to push characters or tokens to clients in real time. This eliminates inefficient polling and supports thousands of concurrent open connections.
AWS Lambda integrates natively with WebSocket APIs and scales automatically with connection volume, enabling a fully managed, serverless architecture. This approach maintains security, centralized authentication, throttling, and observability while avoiding direct client access to Bedrock APIs.
Option B introduces polling latency and unnecessary API overhead and does not provide true streaming.
Option C violates AWS security best practices by exposing Bedrock directly to clients and does not scale securely. Option D only serves completed responses and cannot meet the real-time streaming requirement.
Therefore, Option A is the only solution that fully satisfies streaming behavior, concurrency, latency, and managed-service constraints.


NEW QUESTION # 146
A financial services company needs to pre-process unstructured data such as customer transcripts, financial reports, and documentation. The company stores the unstructured data in Amazon S3 to support an Amazon Bedrock application.
The company must validate data quality, create auditable metadata, monitor data metrics, and customize text chunking to optimize foundation model (FM) performance.
Which solution will meet these requirements with the LEAST development effort?

Answer: B

Explanation:
Option B is the most appropriate solution because it uses AWS-native, purpose-built data engineering and governance services to address data quality validation, metadata creation, monitoring, and transformation with minimal custom development. AWS Glue is designed specifically for large-scale data preparation and integrates seamlessly with Amazon S3, making it ideal for preprocessing unstructured datasets for downstream GenAI applications.
AWS Glue crawlers automatically infer schemas and populate the AWS Glue Data Catalog, creating auditable, queryable metadata for all datasets. This satisfies the requirement for traceability and governance, which is especially critical in financial services environments. Glue ETL jobs allow teams to implement customizable transformation logic, including text normalization and chunking strategies optimized for foundation model context windows.
AWS Glue Data Quality provides built-in rulesets for validating completeness, accuracy, and consistency. It also publishes quality metrics that can be monitored over time, meeting the requirement for ongoing data quality monitoring without building custom validation frameworks.
Because AWS Glue is fully managed, it eliminates the need to manage infrastructure, scaling, or orchestration. This significantly reduces development and operational effort compared to custom Lambda pipelines or EC2-based processing. The processed and validated data can then be safely ingested into Amazon Bedrock workflows or knowledge bases.
Option A and C require custom logic for validation, monitoring, and chunking, increasing development complexity. Option D introduces unnecessary infrastructure management and services not optimized for data preprocessing.
Therefore, Option B best meets the requirements while minimizing development effort and aligning with AWS Generative AI data preparation best practices.


NEW QUESTION # 147
A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system ' s suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?

Answer: C

Explanation:
To achieve the ultra-low latency requirement of less than 500 ms, the system must utilize streaming capabilities at every stage. Using Amazon Transcribe streaming with small (100-ms) audio chunks ensures that transcription begins immediately as the customer speaks. On the model side, Amazon Bedrock's InvokeModelWithResponseStream allows the application to receive tokens as they are generated, rather than waiting for the entire completion, which is critical for real-time interactions. Amazon DynamoDB is the ideal choice for storing supervisor ratings during a live call because it provides the single-digit millisecond latency required for high-frequency writes without impacting the application ' s performance. Options involving batch processing or SQS queuing are unsuitable for sub-500 ms interactive requirements.


NEW QUESTION # 148
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?

Answer: B

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 # 149
A financial services company uses Amazon Bedrock to analyze customer data that is stored in an Amazon S3 bucket. The data includes personally identifiable information (PII). The company must mask PII from foundation model (FM) responses.
Which solution will meet this requirement with the LEAST operational effort?

Answer: B

Explanation:
Option A is correct because Amazon Bedrock Guardrails provides a native sensitive-information filter for personally identifiable information. The filter can detect supported PII types and use the MASK action so detected values in model requests or responses are replaced with placeholders such as the PII type. This directly satisfies the requirement to mask PII from foundation model responses with minimal operational effort. It avoids creating and maintaining an additional preprocessing pipeline. Option B can detect or redact PII with Amazon Comprehend, but it requires Lambda logic and data-processing workflows before Bedrock invocation. Options C and D require scanning, copying, and segregating objects across S3 buckets and still do not directly enforce response masking. For response-level protection in Bedrock, the managed Guardrails capability is the simplest and most purpose-built control. AWS Documentation


NEW QUESTION # 150
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