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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional (AIP-C01) |
| Exam Number: | AIP-C01 |
| Passing Score: | 750 (scaled score, range 100–1000) |
| Exam Duration: | 180 minutes |
| Exam Format: | Multiple choice, Multiple response |
| Exam Price: | USD 300 |
| Available Languages: | English, Simplified Chinese, Korean, Japanese |
| Related Certifications: | AWS Certified Machine Learning Engineer - Associate AWS Certified AI Practitioner AWS Certified Data Engineer - Associate AWS Certified Solutions Architect - Associate |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 75 scored questions + 10 unscored questions |
| Recommended Training: | AWS Skill Builder GenAI Learning Path Amazon Bedrock Documentation |
| Exam Registration: | AWS Skill Builder Registration Official AWS Certification Page |
| 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/ |
現在の状況に満足することは決してなく、AIP-C01試験実践ガイドを必ず拡張および更新してください。イノベーションに焦点を当て、専門チームを編成して新しい知識ポイントをまとめ、テストバンクを更新します。私たちはクライアントを神として扱い、AIP-C01学習教材へのサポートを前進の原動力として扱います。そのため、クライアントはAIP-C01試験問題に関する最新のイノベーションの結果を楽しんで、より多くの学習リソースを獲得できます。クレジットは、私たちの勤勉で献身的な専門技術革新チームと専門家に帰属します。
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質問 # 158
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?
正解：A
解説：
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
質問 # 159
A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
正解：A
解説：
Option A best satisfies the requirement to capture multi-hop, highly interconnected relationships with minimal operational overhead. Traditional vector similarity search excels at finding semantically similar text but is not optimized for reasoning over explicit entity-to-entity relationships, especially when analysts need indirect, multi-hop connections (for example, fund # holding # issuer # sector # regulation). Graph-based retrieval is designed specifically for these kinds of relationship traversals.
GraphRAG combines retrieval-augmented generation with graph-aware context selection. By representing entities and their relationships in a graph store, the system can traverse multiple hops to assemble a holistic set of relevant facts. This improves completeness and reduces the chance that the model misses indirect relationships that are essential for accurate investment guidance.
Amazon Neptune Analytics provides a managed graph analytics environment capable of efficiently traversing and analyzing complex relationship networks. When integrated with Amazon Bedrock Knowledge Bases, it reduces custom engineering by providing managed ingestion, retrieval, and orchestration patterns suitable for GenAI applications. This lowers operational overhead compared to building and maintaining custom multi- stage retrieval logic.
Meeting the sub-3-second requirement is also more feasible with a graph-optimized engine because multi-hop traversals can be executed efficiently compared to chaining multiple vector searches and joining results in an application layer. The managed nature of Knowledge Bases and Neptune Analytics reduces maintenance, scaling, and operational burden while enabling strong performance.
Option B and C require extensive custom logic and orchestration, increasing complexity and latency. Option D is not designed for graph-style multi-hop exploration and would require significant custom indexing and retrieval logic.
Therefore, Option A is the most AWS-aligned and operationally efficient approach for multi-hop relationship- aware RAG with strong performance.
質問 # 160
A healthcare company creates a custom foundation model (FM) that uses a proprietary architecture to summarize and answer questions about sensitive patient records and conversations. To comply with regulations, the company must ensure confidentiality by implementing extensive monitoring and controls. The company must verify the accuracy of the FM by checking prompts and responses for hallucinations.
Which solution will meet these requirements?
正解：C
解説：
Option B is correct under current AWS capabilities. Amazon Bedrock Custom Model Import accepts specified supported model architectures; it is not a mechanism for importing an arbitrary proprietary architecture. SageMaker inference supports bringing a custom container, making it appropriate when custom model architecture or inference code must be preserved. Amazon Bedrock Guardrails can be applied independently of Bedrock-hosted foundation models through the ApplyGuardrail API, including to output from third-party or externally hosted models. Contextual grounding checks calculate grounding and relevance scores and can block responses below configured thresholds, directly addressing hallucination detection.
Options A and C incorrectly use ordinary content filters for grounding and relevance, which are separate contextual-grounding capabilities. Option D incorrectly assigns contextual-grounding functionality to AWS HealthScribe rather than Amazon Bedrock Guardrails. AWS Documentation
質問 # 161
A healthcare company is using Amazon Bedrock to build a system to help practitioners make clinical decisions. The system must provide treatment recommendations to physicians based only on approved medical documentation and must cite specific sources. The system must not hallucinate or produce factually incorrect information.
Which solution will meet these requirements with the LEAST operational overhead?
正解：C
解説：
Option B is the correct solution because Amazon Bedrock Knowledge Bases with the RetrieveAndGenerate API provide a fully managed Retrieval Augmented Generation (RAG) capability that directly addresses grounding, citation, and hallucination prevention with the least operational overhead.
Amazon Bedrock Knowledge Bases automatically manage document ingestion, chunking, embedding, retrieval, and ranking from approved data sources. When used with the RetrieveAndGenerate API, the model is constrained to generate responses only from retrieved, approved clinical documentation, significantly reducing the risk of hallucinations or unsupported claims. The API also returns explicit source citations, which satisfies regulatory and clinical transparency requirements without requiring custom comparison or validation logic.
This approach aligns with AWS best practices for healthcare GenAI workloads, where correctness and traceability are critical. Because retrieval and generation are tightly integrated, the system avoids multi-step orchestration, custom verification pipelines, or additional compute layers that would increase latency and maintenance burden.
Option A introduces Amazon Kendra and custom post-processing logic, increasing operational complexity.
Option C focuses on entity extraction rather than controlled knowledge grounding and does not guarantee citation or hallucination prevention. Option D requires manual orchestration between retrieval and generation and custom verification logic, which increases development and maintenance effort.
Therefore, Option B delivers accurate, grounded, and cited clinical recommendations with minimal infrastructure and operational overhead.
質問 # 162
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?
正解：D
解説：
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.
質問 # 163
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