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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
Topic 2	Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Topic 3	Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.
Topic 4	Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.
Topic 5	AI Safety, Security, and Governance: This domain addresses input output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q136-Q141):

NEW QUESTION # 136
A company is planning to deploy multiple generative AI (GenAI) applications to five independent business units that operate in multiple countries in Europe and the Americas. Each application uses Amazon Bedrock Retrieval Augmented Generation (RAG) patterns with business unit-specific knowledge bases that store terabytes of unstructured data.
The company must establish well-architected, standardized components for security controls, observability practices, and deployment patterns across all the GenAI applications. The components must be reusable, versioned, and governed consistently.
Which solution will meet these requirements?

A. Document security controls, observability requirements, and RAG patterns based on the AWS Well- Architected Generative AI Lens in a shared design document. Use Amazon Macie to enforce deployment. Delegate implementation responsibility to each business unit.
B. Configure Amazon API Gateway REST API endpoints for the GenAI applications. Deploy common security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens in standardized AWS CloudFormation templates. Use CloudFormation Guard after deployment to validate policy compliance in each business unit.
C. Use AWS Service Catalog to define standardized portfolios and versioned products for each business unit. Use the portfolios to enforce security, observability, and RAG patterns based on the AWS Well- Architected Generative AI Lens. Require business units to use the Service Catalog console to deploy resources.
D. Create standardized AWS CloudFormation templates to implement security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Establish a centralized repository for version control. Integrate a CI/CD pipeline with CloudFormation Guard to enforce consistent and repeatable deployments across business units.

Answer: D

Explanation:
Option B best meets the requirement for reusable, versioned, and consistently governed components across multiple business units because it implements "platform-level standardization" through infrastructure as code plus automated compliance enforcement before deployment. Standardized CloudFormation templates provide reusable building blocks for security controls (identity, networking boundaries, encryption), observability practices (metrics, logs, traces), and RAG deployment patterns (knowledge base integration, ingestion pipelines, retrieval controls). This aligns with AWS guidance to operationalize well-architected patterns through repeatable templates rather than ad hoc implementations.
A centralized repository enables version control, change review, and governance of templates across all five business units. This satisfies the "versioned" and "reusable" requirements and provides a single source of truth for approved architectures. Integrating a CI/CD pipeline ensures that deployments are consistent and automated, reducing drift between business units and Regions.
CloudFormation Guard is most effective when used as a preventive control in the pipeline, not only after deployment. By running Guard rules during build or pre-deploy stages, the organization can enforce mandatory security and observability configurations and block noncompliant changes before they reach production. This supports consistent governance while still enabling business units to deploy quickly.
Option A performs compliance validation after deployment, which allows policy violations to be deployed first and remediated later. Option C provides governed provisioning but requiring console-based deployment reduces automation and can slow standardized CI/CD adoption; it also adds an additional governance layer that is not required to meet the stated needs. Option D is not enforceable and does not provide reusable, versioned, governed components.
Therefore, Option B provides the strongest, most scalable, and most consistently governed approach for standardized GenAI deployments across business units.

NEW QUESTION # 137
A company is developing a customer communication platform that uses an AI assistant powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes customer messages and generates initial response drafts.
The company wants to use Amazon Comprehend to implement layered content filtering. The layered content filtering must prevent sharing of offensive content, protect customer privacy, and detect potential inappropriate advice solicitation. Inappropriate advice solicitation includes requests for unethical practices, harmful activities, or manipulative behaviors.
The solution must maintain acceptable overall response times, so all pre-processing filters must finish before the content reaches the FM.
Which solution will meet these requirements?

A. Use custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.
B. Deploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.
C. Use parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.
D. Use toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.

Answer: D

Explanation:
Option D best satisfies all functional, performance, and governance requirements while minimizing architectural complexity. The requirement explicitly states that all filtering must complete before content reaches the foundation model, which rules out asynchronous or streaming-based approaches that could delay enforcement.
Amazon Comprehend supports toxicity detection, prompt safety classification, and PII detection with entity redaction as managed capabilities. Running these filters in parallel ensures low end-to-end latency, which is essential for customer-facing communication platforms. Parallel execution avoids the cumulative latency that would be introduced by sequential pipelines.
Toxicity detection identifies offensive or abusive content early. Prompt safety classification detects requests for unethical, harmful, or manipulative advice, which directly addresses inappropriate advice solicitation requirements. PII detection with entity redaction ensures that customer privacy is preserved before data is sent to the FM, preventing sensitive information from being processed or echoed in generated responses.
Configuring thresholds allows fine-grained control over sensitivity while maintaining acceptable false- positive rates. Using CloudWatch metrics and alarms enables continuous monitoring of filtering behavior and intervention rates without adding custom routing or human review pipelines that would slow responses.
Option A lacks PII redaction. Option B introduces unnecessary model-building complexity and delayed PII checks. Option C adds sequential latency and introduces human review routing, which violates the response- time requirement.
Therefore, Option D provides the most robust, performant, and AWS-aligned layered content filtering solution.

NEW QUESTION # 138
A healthcare company is using Amazon Bedrock to build a Retrieval Augmented Generation (RAG) application that helps practitioners make clinical decisions. The application must achieve high accuracy for patient information retrievals, identify hallucinations in generated content, and reduce human review costs.
Which solution will meet these requirements?

A. Configure Amazon CloudWatch Synthetics to generate test queries that have known answers on a regular schedule, and track model success rates. Set up dashboards that compare synthetic test results against expected outcomes.
B. Deploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use a built-in Amazon Bedrock evaluation to track retrieval precision and hallucination rates.
C. Implement automated large language model (LLM)-based evaluations that use a specialized model that is fine-tuned for medical content to assess all responses. Deploy AWS Lambda functions to parallelize evaluations. Publish results to Amazon CloudWatch metrics that track relevance and factual accuracy.
D. Use Amazon Comprehend to analyze and classify RAG responses and to extract medical entities and relationships. Use AWS Step Functions to orchestrate automated evaluations. Configure Amazon CloudWatch metrics to track entity recognition confidence scores. Configure CloudWatch to send an alert when accuracy falls below specified thresholds.

Answer: B

Explanation:
Option D is the correct solution because it directly addresses all three requirements: high retrieval accuracy, hallucination detection, and reduced human review costs. AWS recommends a layered evaluation strategy for high-stakes domains such as healthcare, where generative outputs must be both accurate and safe.
Using an automated LLM-as-a-judge evaluation enables scalable, consistent assessment of generated responses for factual grounding, relevance, and hallucination risk. This automated screening significantly reduces the number of responses that require manual inspection. Only responses that fall below defined quality thresholds or exhibit ambiguous behavior are escalated to targeted human reviews, which optimizes review effort and cost.
The use of Amazon Bedrock built-in evaluations provides standardized metrics specifically designed for RAG systems, including retrieval precision, faithfulness to source documents, and hallucination rates. These evaluations integrate directly with Amazon Bedrock knowledge bases and models, eliminating the need to build and maintain custom evaluation pipelines.
Option A focuses on entity extraction confidence, which does not reliably detect hallucinations in generative text. Option B requires maintaining and scaling a separate fine-tuned evaluation model, increasing complexity and cost. Option C is useful for regression testing but cannot detect hallucinations in real-world, open-ended clinical queries.
Therefore, Option D provides the most effective and operationally efficient approach to maintaining clinical- grade accuracy while minimizing human review effort.

NEW QUESTION # 139
A cooking spice company is launching a marketing campaign that will invite customers to upload videos of themselves cooking with the company ' s spices to a platform that the company operates. The company is designing a solution for the video-sharing platform.
The solution must extract video transcripts and generate embeddings to help users search for videos based on spices, other ingredients, and cooking methods. The solution must integrate with the company ' s existing open-source chat system so users can interact with videos in real time. The solution must analyze user chat text for sentiment to determine the most positively received videos. Each month, the recipe videos that received the most positive feedback and the highest number of views will be displayed on the platform.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select THREE.)

A. Store video metrics such as positive feedback counts and view counts in an Amazon Aurora PostgreSQL database that uses the pgvector extension.
B. Use AWS Step Functions to orchestrate the entire application pipeline. Configure a step in the workflow to use an AWS Lambda function to call the chat system API.
C. Use the Strands Agents SDK with built-in HTTP tools to call the open-source chat system API. Use the Strands Agents SDK built-in orchestration capabilities to coordinate calls to Amazon Bedrock Knowledge Bases to enable video search and to Amazon Comprehend to perform sentiment analysis.
D. Store video metrics such as positive feedback counts and view counts in Amazon DynamoDB.
E. Configure a custom orchestration AWS Lambda function to call all application components directly, manage errors, and embed custom code in calls to the chat system API.
F. Use Amazon Transcribe to perform automatic speech recognition (ASR) to extract video transcripts.
Use Amazon Comprehend to perform sentiment analysis on chat feedback. Use an Amazon Bedrock embeddings model to generate embeddings. Store the embeddings in an Amazon Bedrock knowledge base to enable semantic retrieval.

Answer: C,D,F

Explanation:
Options A, D, and E provide the required capabilities while minimizing custom infrastructure. Amazon Transcribe converts speech in uploaded media into text, Amazon Comprehend provides managed sentiment detection, and Bedrock Knowledge Bases can support embedding-based semantic retrieval over transcript content. DynamoDB is appropriate for lightweight, scalable counters such as views and positive-feedback totals and avoids operating a relational vector database solely for metrics. The Strands Agents SDK provides prebuilt HTTP-request tooling for invoking the existing chat API and supports tool-based and multi-agent orchestration patterns, reducing bespoke integration code. Option B adds Step Functions and Lambda when Strands can coordinate the required application interactions directly. Option C requires custom orchestration and error-management code. Option F introduces Aurora PostgreSQL and pgvector even though vector retrieval is already handled through the Bedrock knowledge-base layer. AWS Documentation

NEW QUESTION # 140
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain- specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon Bedrock knowledge base to store the reference medical documents. Enable query decomposition in the knowledge base. Configure an Amazon Bedrock flow that uses a foundation model and the knowledge base to support the application.
B. Create an Amazon Bedrock agent to orchestrate multiple AWS Lambda functions to decompose queries. Create an Amazon Bedrock knowledge base to store the reference medical documents. Use the agent's built-in knowledge base capabilities. Add deep research and reasoning capabilities to the agent to reduce ambiguity in the medical terminology.
C. Use Amazon API Gateway to route incoming queries to an Amazon Bedrock agent. Configure the agent to use an Anthropic Claude model to decompose queries and an Amazon Titan model to expand queries. Create an Amazon Bedrock knowledge base to store the reference medical documents.
D. Use Amazon SageMaker AI to host custom ML models for both query decomposition and query expansion. Configure Amazon Bedrock knowledge bases to store the reference medical documents.
Encrypt the documents in the knowledge base.

Answer: A

NEW QUESTION # 141
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