P.S. KaoGuTi在Google Drive上分享了免費的、最新的AIP-C01考試題庫：https://drive.google.com/open?id=1hLERL9kws6JPocSVgrEkJMrC54l91Aud
也許你在其他相關網站上也看到了與 Amazon AIP-C01 認證考試相關的相關培訓工具，但是我們的 KaoGuTi在IT 認證考試領域有著舉足輕重的地位。KaoGuTi研究的材料可以保證你100%通過考試。有了KaoGuTi你的職業生涯將有所改變，你可以順利地在IT行業中推廣自己。當你選擇了KaoGuTi你就會真正知道你已經為通過Amazon AIP-C01認證考試做好了準備。我們不僅能幫你順利地通過考試還會為你提供一年的免費服務。
| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional (AIP-C01) |
| Exam Number: | AIP-C01 |
| Exam Format: | Multiple choice, Multiple response |
| Available Languages: | Japanese, Simplified Chinese, Korean, English |
| Real Exam Qty: | 75 scored questions + 10 unscored questions |
| Exam Duration: | 180 minutes |
| Passing Score: | 750 (scaled score, range 100–1000) |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Data Engineer - Associate AWS Certified AI Practitioner AWS Certified Solutions Architect - Associate AWS Certified Machine Learning Engineer - Associate |
| Exam Price: | USD 300 |
| Recommended Training: | Amazon Bedrock Documentation AWS Skill Builder GenAI Learning Path |
| 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/ |
KaoGuTi不僅為你提供優秀的資料，而且還為你提供優質的服務。如果你購買了KaoGuTi的考古題，KaoGuTi將為你提供一年的免費更新。這樣你就可以一直擁有最新的AIP-C01試題資料。而且，萬一你用了AIP-C01考古題以後，考試還是失敗的話，KaoGuTi保證全額退款。這樣一來，你還擔心什麼呢？KaoGuTi對自己的資料有足夠的信心，你也要對KaoGuTi有足夠的信心。為了你的考試能夠成功，千萬不要錯過KaoGuTi這個網站。因為如果錯過了它，你就等於錯失了一次成功的機會。
| 主題 | 簡介 |
|---|---|
| 主題 1 |
|
| 主題 2 |
|
| 主題 3 |
|
| 主題 4 |
|
| 主題 5 |
|
問題 #135
An ecommerce company is using an Anthropic Claude Sonnet model in Amazon Bedrock to generate product recommendations. An AWS Lambda function retrieves customer purchase data from Amazon DynamoDB, product reviews from Amazon S3, and customer profile information from Amazon RDS. Then the function sends the data directly to the Amazon Bedrock model through API calls. Recently, customers who have extensive purchase histories have begun to receive incomplete recommendations.
Amazon CloudWatch logs for the Lambda function show execution timeouts. CloudWatch logs for Amazon Bedrock API calls show intermittent errors. The company reviews the logs and finds that some requests are failing with context-length-exceeded errors. Other requests finish but appear to ignore portions of the input data.
The company wants the recommendation system to consider all customer data when the system generates recommendations. The company wants to use Amazon Bedrock Knowledge Bases to improve data organization and retrieval.
Which combination of solutions will meet these requirements? (Select TWO.)
答案：B,E
解題說明：
Options A and E are correct because the failure pattern is caused by exceeding the model's usable context window and by sending too much raw customer data directly to the FM. Amazon Bedrock Knowledge Bases follows the RAG pattern by splitting source content into manageable chunks, converting chunks into embeddings, storing them in a vector index, and using semantic similarity to retrieve relevant content for a user query. This supports option E because the application can index customer purchases, profiles, and review- related information, then retrieve only the most relevant records for the current recommendation request instead of stuffing all raw data into one prompt.
Option A is also correct because chunking and staged synthesis are standard ways to handle inputs that exceed an FM's context capacity. Processing smaller segments avoids context-length-exceeded errors, and a final synthesis call can combine intermediate findings into a complete recommendation. This is especially useful when the company says it wants the system to consider all customer data, not merely truncate older or less important data. Amazon Bedrock Knowledge Bases also lets teams configure how content is chunked for storage and retrieval, which supports a structured approach rather than an oversized one-shot prompt.
Option B is not sufficient because truncating "less important" data conflicts with the requirement to consider all customer data. Option C violates the implied design direction because the company wants to use Knowledge Bases, and simply choosing a larger context model does not solve long-term growth of customer histories. Option D is technically incorrect: model parameters such as max_tokens control output generation limits, not the model's maximum context window, and different Claude models have model-specific maximum values. You cannot use additionalModelRequestFields to bypass the FM's context limit.
問題 #136
A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.
The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Select THREE.)
答案：A,B,D
解題說明：
The correct combination is A, B, and D because these three options collectively satisfy the mandatory requirements for structured extraction, PII redaction before inference, regional human review, data residency, auditability, and high-scale availability with managed AWS services.
Option A is essential because Amazon Textract is the AWS-managed service designed to extract structured data from scanned documents such as forms, tables, and financial statements. Textract provides confidence scores, and Amazon Augmented AI (A2I) is purpose-built to route low-confidence extractions to human reviewers. Deploying Textract and A2I within the same Region ensures that the human review loop remains regionally constrained, meeting strict data residency requirements for applicants.
Option B satisfies the requirement to redact PII before inference by using AWS Lambda preprocessing. It also adds Amazon Bedrock guardrails to enforce safety controls on model outputs. Region-specific IAM roles ensure that only authorized principals in the correct Region can access the extracted data and invoke downstream services, strengthening residency enforcement and auditability.
Option D ensures that source documents are stored in Amazon S3 in the same Region as the applicant. Object metadata and tagging provide an auditable trail, supporting compliance reporting and traceability. S3 also provides the durability and availability needed to support 99.9% application availability as part of a well- architected pipeline.
Option C is not the correct approach for structured extraction from scans. Option E adds useful quality validation but is not strictly required to meet the stated requirements compared to A, B, and D. Option F is unrelated to the extraction/redaction/residency workflow requirements.
Therefore, A, B, and D are the best three choices to meet all stated requirements with minimal operational overhead.
問題 #137
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?
答案：B
解題說明：
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.
問題 #138
A healthcare company is developing a generative AI (GenAI) application that recommends patient treatment plans to physicians. The company wants to use Amazon Bedrock to build the application.
The application must document model limitations, prevent unauthorized clinical recommendations, and maintain detailed audit trails of all AI-generated outputs. The company must store outputs in compliance with healthcare regulations. The solution must prevent output tampering.
Which solution will meet these requirements?
答案：C
解題說明：
Option A addresses all three governance layers. Model cards provide documented information about a model' s capabilities, limitations, supported modalities, context constraints, and other characteristics that teams can review before deployment. Amazon Bedrock Guardrails can enforce application-specific safeguards on user inputs and model responses, and IAM policies can require a specific guardrail for inference calls, helping prevent uncontrolled model use. Storing generated outputs in Amazon S3 provides durable audit storage, while S3 Object Lock uses a write-once-read-many model that can prevent retained objects from being overwritten or deleted and is specifically intended to support regulatory retention requirements. Option B lacks formal model-limit documentation and relies on custom postprocessing. Option C does not protect stored audit evidence against tampering. Option D relies primarily on prompts and ordinary database storage rather than enforceable safety and immutable-retention controls. AWS Documentation
問題 #139
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?
答案：D
解題說明：
Option A is the best solution because it satisfies streaming, token control, and retry requirements while keeping operational overhead low by using fully managed, serverless AWS services. Amazon API Gateway HTTP APIs provide a lightweight, cost-effective front door for APIs and integrate cleanly with AWS Lambda for request processing and security controls.
AWS Lambda response streaming allows the API to begin returning content to the client as soon as partial model output is available, reducing perceived latency and improving user experience for long responses.
Using Lambda as the integration layer also provides a centralized place to enforce token-aware request handling, such as rejecting oversized requests, truncating optional context, or applying consistent limits across users and tenants to manage compute usage.
Retry logic is best handled in the client or integration layer for transient failures such as timeouts and throttling. Lambda can implement controlled retries with exponential backoff and jitter, while API Gateway timeouts help bound request lifetimes and prevent hung connections from consuming resources indefinitely.
Because the model service is managed, the company avoids infrastructure management and focuses only on request shaping, safety, and resiliency behavior.
Option B is not suitable because client-side polling is not true streaming, front-end token enforcement is insecure and inconsistent, and API Gateway does not provide model-aware retry behavior on its own. Option C introduces container hosting and scaling complexity, which increases operational overhead compared to serverless. Option D can work, but REST APIs are generally heavier than HTTP APIs for this pattern and do not reduce overhead compared to Option A.
Therefore, Option A provides the required streaming and resiliency capabilities with the least infrastructure management effort.
問題 #140
......
AIP-C01參考資料: https://www.kaoguti.com/AIP-C01_exam-pdf.html
從Google Drive中免費下載最新的KaoGuTi AIP-C01 PDF版考試題庫：https://drive.google.com/open?id=1hLERL9kws6JPocSVgrEkJMrC54l91Aud