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Amazon AIP-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q83-Q88):

NEW QUESTION # 83
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company ' s approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)

Answer: B,C,F


NEW QUESTION # 84
A retail company is using Amazon Bedrock to develop a customer service AI assistant. Analysis shows that
70% of customer inquiries are simple product questions that a smaller model can effectively handle. However,
30% of inquiries are complex return policy questions that require advanced reasoning.
The company wants to implement a cost-effective model selection framework to automatically route customer inquiries to appropriate models based on inquiry complexity. The framework must maintain high customer satisfaction and minimize response latency.
Which solution will meet these requirements with the LEAST implementation effort?

Answer: A

Explanation:
Option B is the correct solution because it leverages native Amazon Bedrock intelligent prompt routing, which is specifically designed to reduce cost and complexity in multi-model GenAI architectures. Intelligent prompt routing automatically analyzes incoming prompts and selects the most appropriate foundation model based on prompt characteristics and complexity-without requiring custom classification logic or orchestration code.
This approach directly meets the requirement for least implementation effort. The company does not need to deploy additional Lambda functions, maintain routing rules, or manage separate classification stages. Routing decisions are handled by Bedrock, which simplifies architecture and reduces operational risk.
By routing the majority (70%) of simple product inquiries to smaller, lower-cost models, the company minimizes inference cost and latency. More complex return policy inquiries are automatically routed to larger models that provide better reasoning capabilities, preserving response quality and customer satisfaction.
Because routing is handled inline by Bedrock, response latency remains low compared to multi-stage architectures that require an additional classification model call before inference. This is critical for customer service scenarios where responsiveness directly impacts satisfaction.
Option A introduces additional inference steps and custom logic. Option C increases cost by overusing a mid- sized model for all queries. Option D relies on brittle keyword rules and increases operational overhead through endpoint management.
Therefore, Option B delivers the optimal balance of cost efficiency, performance, and simplicity for dynamic model selection in Amazon Bedrock.


NEW QUESTION # 85
A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5-100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.
Which solution will meet these requirements?

Answer: B

Explanation:
Option B best satisfies the requirements because it directly applies Retrieval Augmented Generation principles using managed Amazon Bedrock Knowledge Bases, which are designed to handle large, complex documents while preserving contextual relationships. Financial reports often interleave tables with explanatory narrative, and accurate analysis depends on keeping those elements logically connected. By segmenting documents based on their structural layout-for example, sections, subsections, tables, and surrounding commentary-the knowledge base can retrieve semantically relevant chunks that maintain this relationship during inference.
Amazon Bedrock Knowledge Bases support contextual chunking strategies that go beyond simple fixed-size segmentation. This is critical for financial documents, where a metric in a table may be explained in adjacent paragraphs or footnotes. Context-aware chunking ensures that retrieved content includes both the numeric data and its interpretation, enabling the foundation model to generate accurate, grounded responses. Including citations further improves analyst trust and auditability by allowing users to trace answers back to specific source sections, which is a common requirement in financial environments.
Scalability is another key requirement. Knowledge Bases manage embedding generation, indexing, and retrieval orchestration as a managed service, which allows the solution to scale across large document collections without requiring custom infrastructure or model hosting. This approach also supports efficient updates as new quarterly reports are added, ensuring the retrieval layer remains current.
Option A does not scale well because processing entire 5-100 page documents in a single prompt increases token usage, latency, and cost while risking context truncation. Option C relies on fixed-size chunking triggered at query time, which often breaks semantic relationships in structured financial content. Option D introduces unnecessary architectural complexity by splitting structured and unstructured data into separate applications, increasing operational overhead without providing better contextual retrieval than a unified RAG approach.


NEW QUESTION # 86
A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real- time personally identifiable information (PII) filtering and audit trails to automatically report compliance.
Which solution will meet these requirements?

Answer: B

Explanation:
Option A is the most comprehensive and architecturally aligned solution for meeting end-to-end data lineage, real-time PII filtering, and automated compliance reporting requirements in a medical GenAI system built on Amazon Bedrock. Each requirement maps directly to a managed AWS service that is purpose-built for governance, security, and compliance.
AWS Glue Data Catalog is designed to register datasets across multiple sources and maintain metadata that supports lineage tracking. By cataloging all inputs that flow into the Bedrock-based system, the organization can trace how data moves from ingestion through processing and storage, which is essential for regulatory audits in healthcare environments.
For real-time PII filtering, Amazon Bedrock Guardrails provide native PII detection and filtering during model inference. Guardrails operate inline with model invocation, ensuring sensitive information is blocked or redacted before responses are returned to users. This satisfies the requirement for real-time protection rather than post-processing analysis.
AWS CloudTrail delivers a complete audit trail of all Amazon Bedrock API calls, including InvokeModel requests and configuration changes. Storing these logs in Amazon S3 enables long-term retention and supports compliance audits. CloudTrail ensures traceability of who accessed the system, when, and how it was used.
To strengthen compliance monitoring, Amazon Macie continuously scans stored data for sensitive information and automatically classifies findings. Publishing Macie findings to Amazon CloudWatch Logs and visualizing them through dashboards enables near-real-time visibility into compliance posture and supports automated reporting workflows.
The other options fall short. Option B performs PII filtering at the application edge rather than at inference time and relies on scheduled analysis instead of real-time enforcement. Option C focuses on replication and document processing rather than inline GenAI governance. Option D uses services that are not designed for PII detection in text-based GenAI workflows and lacks native lineage tracking.
Therefore, A best fulfills all stated requirements using AWS-recommended governance and security capabilities.


NEW QUESTION # 87
A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models.
The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.
The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.
Which solution will meet these requirements?

Answer: C

Explanation:
Option C best meets the requirements by combining native Amazon Bedrock logging with adaptive monitoring and minimal operational overhead. Amazon Bedrock model invocation logging can be sent directly to CloudWatch Logs, where detailed fields such as InputTokenCount, OutputTokenCount, and tool invocation metadata are captured for each request.
CloudWatch metric filters allow extraction of structured metrics from logs, including tool-specific token consumption patterns. By defining filters per tool integration, the company can isolate which tools are responsible for increased token usage without building custom log-processing pipelines.
CloudWatch anomaly detection provides automatic baseline modeling and dynamic thresholds based on historical traffic patterns. Unlike static alarms, anomaly detection adapts as usage evolves, making it ideal for applications with changing workloads or seasonal usage patterns. This directly satisfies the requirement to automatically adjust thresholds as traffic patterns change.
When abnormal token consumption occurs, anomaly detection alarms trigger immediately, enabling rapid investigation and remediation. Because this solution uses fully managed AWS services without custom analytics jobs or manual threshold tuning, it significantly reduces operational effort.
Option A fails to adapt to changing patterns. Option B introduces batch analysis and delayed insights. Option D requires manual intervention and custom code, increasing maintenance burden.
Therefore, Option C provides the most scalable, adaptive, and low-maintenance solution for monitoring and controlling token consumption in Amazon Bedrock-based applications.


NEW QUESTION # 88
......

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