참고: DumpTOP에서 Google Drive로 공유하는 무료, 최신 AIP-C01 시험 문제집이 있습니다: https://drive.google.com/open?id=1XIfpnOSAYWCOpPLzv3e8wQ4MbrPSXT3_
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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional (AIP-C01) |
| Exam Number: | AIP-C01 |
| Real Exam Qty: | 75 scored questions + 10 unscored questions |
| Passing Score: | 750 (scaled score, range 100–1000) |
| Exam Duration: | 180 minutes |
| Related Certifications: | AWS Certified Machine Learning Engineer - Associate AWS Certified Solutions Architect - Associate AWS Certified Data Engineer - Associate AWS Certified AI Practitioner |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD 300 |
| Exam Format: | Multiple choice, Multiple response |
| Available Languages: | English, Japanese, Korean, Simplified Chinese |
| Recommended Training: | Amazon Bedrock Documentation AWS Skill Builder GenAI Learning Path |
| Exam Registration: | Official AWS Certification Page AWS Skill Builder Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite testing center (Pearson VUE) |
| Pre Condition: | Recommended: 2+ years AWS or cloud development experience and 1 year hands-on GenAI implementation experience |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-generative-ai-developer-professional/ |
DumpTOP의 덤프선택으로Amazon AIP-C01인증시험에 응시한다는 것 즉 성공과 멀지 않았습니다. 여러분의 성공을 빕니다.
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질문 # 107
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?
정답：A
질문 # 108
A financial technology company is using Amazon Bedrock to build an assessment system for the company's customer service AI assistant. The AI assistant must provide financial recommendations that are factually accurate, compliant with financial regulations, and conversationally appropriate. The company needs to combine automated quality evaluations at scale with targeted human reviews of critical interactions.
What solution will meet these requirements?
정답：D
설명：
Option B meets the requirement to combine scalable automated evaluation with targeted human oversight using managed AWS GenAI capabilities. Amazon Bedrock evaluations enable systematic, repeatable quality assessment across large volumes of interactions. Using an LLM-as-a-judge approach with a strong evaluator model such as Anthropic Claude Sonnet allows the company to automatically score outputs for dimensions like factual accuracy, conversational appropriateness, and policy alignment. This directly supports "automated quality evaluations at scale" without building custom scoring models.
However, financial recommendations add higher risk because regulatory compliance requires additional enforcement beyond general quality scoring. Amazon Bedrock guardrails provide a dedicated policy enforcement layer that can block or intervene when responses violate compliance constraints. Guardrails are particularly important for preventing disallowed financial guidance patterns and ensuring consistent behavior across deployments.
The requirement also calls for "targeted human reviews of critical interactions." Amazon Augmented AI (A2I) is a managed human review service that supports routing specific items to human reviewers based on rules or confidence thresholds. In this design, the system can automatically send only high-risk or policy- flagged interactions to qualified financial experts for review, keeping human effort focused where it matters most while maintaining scale.
Option A is not scalable because it requires manual review of all responses. Option C relies on static rules and end-user feedback, which is insufficient for regulatory compliance and factual accuracy assurance. Option D provides monitoring but not structured quality evaluation or policy enforcement.
Therefore, Option B provides the most complete, AWS-aligned solution for scalable evaluation plus human oversight in a regulated financial context.
질문 # 109
A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket.
The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time.
The company determines that the underlying models will not benefit from additional fine-tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture.
Which solution will meet these requirements?
정답：C
설명：
Option C is the correct solution because it directly addresses the root cause of the problem-overly broad retrieval-while requiring minimal architectural change. Amazon Bedrock Knowledge Bases support metadata-aware filtering, which allows the system to constrain retrieval queries based on indexed metadata such as content type, publication date, source, or category.
By indexing Amazon S3 object metadata, the company can restrict vector searches to relevant subsets of the corpus, such as recent emergency reports, specific content formats, or trusted sources. This significantly reduces the number of documents evaluated during retrieval, which improves both latency and result relevance without changing embedding models or retrieval infrastructure.
This approach aligns with AWS best practices for optimizing RAG systems: when embeddings are already strong, retrieval quality is often improved by narrowing the candidate set rather than increasing model complexity. Metadata filtering reduces noise and ensures that retrieved documents are more contextually aligned with user queries.
Option A requires retraining or adapting embedding models, which the company has already determined will not provide additional benefit. Option B introduces a migration to OpenSearch, which adds operational overhead and deviates from the existing Bedrock knowledge base architecture. Option D requires moving to a different indexing service, increasing complexity and implementation effort.
Therefore, Option C provides the most effective and low-effort solution to improve retrieval accuracy and performance in the existing Amazon Bedrock RAG system.
질문 # 110
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?
정답：D
설명：
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.
질문 # 111
A company is using Amazon Bedrock to build a GenAI assistant that answers employee questions based on internal documentation. The company stores documents in Amazon S3, Atlassian Confluence, and an internal wiki system. The GenAI assistant must retrieve relevant content and provide grounded responses.
The solution must meet the following requirements:
* Integrate multiple document sources into a single retrieval layer.
* Support semantic search rather than keyword-only queries.
* Minimize custom ingestion and synchronization logic.
* Ensure that retrieved content can be directly used to augment the GenAI assistant ' s foundation model (FM).
Which solution will meet these requirements?
정답：C
설명：
Amazon Bedrock Knowledge Bases is designed specifically for managed retrieval-augmented generation. A knowledge base connects source repositories to an embedding model and vector store. During ingestion, source content is transformed into numerical vector embeddings. At retrieval time, the query is similarly represented so the knowledge base can compare semantic similarity and return passages that are conceptually relevant rather than depending exclusively on exact keyword matches.
Amazon Bedrock supports managed data-source connectors for multiple repositories. The AWS documentation lists Amazon S3, Confluence, Microsoft SharePoint, Salesforce, web crawling, and custom data sources among supported connection patterns. These connectors reduce the amount of ingestion, crawling, and synchronization code an organization needs to maintain.
For Atlassian Confluence specifically, Bedrock can crawl supported Confluence content and supports incremental synchronization for added, modified, or deleted material. Current managed Confluence integration also supports crawling pages, blog posts, and attachments, subject to the documented connector limitations.
This architecture supplies the retrieval layer needed to augment an FM with source-grounded context. It centralizes retrieval semantics while letting the application use Bedrock ' s managed ingestion and retrieval capabilities instead of implementing its own embedding pipelines.
B explicitly uses keyword-oriented mappings and therefore does not meet the semantic-search requirement. C could technically implement RAG, but Lambda-based embedding generation, synchronization, and custom retrieval logic create precisely the operational burden the company wants to avoid. D is not an appropriate semantic RAG architecture because ordinary DynamoDB queries do not automatically produce vector-based semantic retrieval from unstructured documentation.
For an internal wiki not covered by a built-in connector, the knowledge-base custom data-source capability can be used while keeping the overall retrieval interface centralized. Therefore, A provides the closest fit to every stated architectural requirement.
질문 # 112
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AIP-C01시험대비 덤프공부문제: https://www.dumptop.com/Amazon/AIP-C01-dump.html
DumpTOP AIP-C01 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1XIfpnOSAYWCOpPLzv3e8wQ4MbrPSXT3_