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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Exam Duration: | 180 minutes |
| Real Exam Qty: | 85 |
| Passing Score: | 750 (on a scale of 100-1000) |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Exam Price: | $300 USD |
| Exam Format: | Matching, Multiple Choice, Multiple Response, Ordering |
| Related Certifications: | AWS Certified AI Practitioner |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored exam or test center (Pearson VUE) |
| Pre Condition: | Recommended 3+ years of experience in software development and 1+ year of experience building applications with generative AI on AWS. AWS Cloud Practitioner certification or equivalent cloud knowledge recommended. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-generative-ai-developer-professional/ |
IT업계에서 자신만의 위치를 찾으려면 자격증을 많이 취득하는것이 큰 도움이 될것입니다. Amazon 인증 AIP-C01시험은 아주 유용한 시험입니다. Amazon 인증AIP-C01시험출제경향을 퍼펙트하게 연구하여ExamPassdump에서는Amazon 인증AIP-C01시험대비덤프를 출시하였습니다. ExamPassdump에서 제공해드리는Amazon 인증AIP-C01시험덤프는 시장에서 판매하고 있는Amazon 인증AIP-C01덤프중 가장 최신버전덤프로서 덤프에 있는 문제만 공부하시면 시험통과가 쉬워집니다.
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질문 # 123
A company developed a multimodal content analysis application by using Amazon Bedrock. The application routes different content types (text, images, and code) to specialized foundation models (FMs).
The application needs to handle multiple types of routing decisions. Simple routing based on file extension must have minimal latency. Complex routing based on content semantics requires analysis before FM selection. The application must provide detailed history and support fallback options when primary FMs fail.
Which solution will meet these requirements?
정답：D
설명：
Option B is the most appropriate solution because it directly aligns with AWS-recommended architectural patterns for building scalable, observable, and resilient generative AI applications on Amazon Bedrock. The requirements clearly distinguish between simple and complex routing decisions, and this option addresses both in an optimal way.
Simple routing based on file extension is latency sensitive. Handling this logic directly in the application code avoids unnecessary orchestration, state transitions, and service calls. This approach ensures that straightforward requests, such as routing images to vision-capable foundation models or text files to language models, are processed with minimal overhead and maximum performance.
For complex routing based on content semantics, AWS Step Functions is specifically designed for multi-step workflows that require analysis, branching logic, and error handling. Semantic routing often requires inspecting meaning, intent, or structure before selecting the appropriate foundation model. Step Functions enables this by orchestrating analysis steps and applying conditional logic to determine the correct model to invoke using the Amazon Bedrock InvokeModel API.
A key requirement is detailed execution history. Step Functions provides built-in execution tracing, including state inputs, outputs, and error details, which is essential for auditing, debugging, and compliance.
Additionally, Step Functions supports native retry and catch mechanisms, allowing the workflow to automatically fall back to alternate foundation models if a primary model invocation fails. This directly satisfies the fallback requirement without introducing excessive custom code.
The other options lack one or more critical capabilities. Lambda-only logic lacks deep observability and structured fallback handling, SQS introduces additional latency and limited workflow visibility, and multiple coordinated workflows increase architectural complexity without added benefit.
질문 # 124
A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company wants to manage these controls centrally.
Which combination of solutions will meet these requirements? (Select TWO.)
정답：C,E
설명：
The correct combination is C and D because together they enforce centralized governance over both model access and prompt content controls, which are the two core requirements of the scenario.
To ensure employees can use only approved Amazon Bedrock models, governance must be enforced at the organization level and not rely on individual application logic. Service Control Policies (SCPs) are the strongest control mechanism available in AWS Organizations because they define the maximum permissions an account or principal can have. In option C, the SCP prevents any Amazon Bedrock model invocation unless a centrally approved guardrail identifier is specified. This ensures that guardrails are always enforced, regardless of how or where the invocation originates. The additional use of IAM permissions boundaries ensures that even within allowed accounts, employees are restricted to invoking only explicitly approved foundation models.
To prevent specific topics and proprietary information from being included in prompts, Amazon Bedrock Guardrails must be used. Guardrails operate inline during model invocation and can block disallowed content before it is processed by the model. Option D correctly specifies a block filtering policy, which is appropriate when content must be prevented entirely rather than partially redacted. Deploying the guardrail using AWS CloudFormation StackSets allows the company to centrally manage and consistently deploy the same guardrail configuration across all accounts in the organization, ensuring uniform enforcement.
Option E uses mask filtering, which is better suited for redacting sensitive output rather than preventing prohibited content from being submitted in prompts. Option B attempts to use SCPs alone but does not enforce guardrail deployment or content filtering. Option A incorrectly places guardrail enforcement in permissions boundaries, which are not designed to validate request parameters such as guardrail identifiers.
By combining SCP-based enforcement with centrally deployed Bedrock guardrails, options C and D together provide strong, scalable, and centrally managed controls for both content safety and model governance across the organization.
질문 # 125
A healthcare company uses a multi-agent system on Amazon Bedrock AgentCore. The system uses multiple FMs to process 2,000 medical report documents daily. The documents range from 5 to 50 pages each.
The company discovers that the system is not performing complete analysis on documents that exceed 30 pages. Documents over 30 pages miss critical information from later sections. The results are truncated, but there are no explicit errors.
The company must resolve this issue while maintaining response times under 10 seconds. The company must keep processing costs low.
Which solution will meet these requirements?
정답：C
설명：
Option B is correct because the symptoms indicate context-window pressure rather than a compute-memory or Lambda-timeout problem. Large documents should be split into manageable chunks while preserving semantic continuity across boundaries. Amazon Bedrock Knowledge Bases supports chunking strategies, including semantic chunking and overlap-oriented approaches, so relevant context can be retained without sending an entire long report to the model at once. Monitoring InputTokenCount provides evidence of context utilization and helps identify when requests approach model limits. Diagnostic logging can then locate where truncation occurs. Option A increases timeouts but does not enlarge the model context window. Option C adds sequential orchestration that can jeopardize the 10-second target. Option D increases infrastructure resources, but host memory does not change a foundation model's maximum context window and adds unnecessary cost. AWS Documentation
질문 # 126
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?
정답：B
설명：
Option B is the best solution because hierarchical chunking is specifically designed to preserve broader semantic context while still enabling precise retrieval at paragraph or sub-paragraph granularity. The problem described-answers citing irrelevant sections when a query spans multiple paper sections-often occurs when chunks are either too small (losing cross-paragraph context) or too "flat" (retrieving isolated snippets without their surrounding rationale).
In a scientific paper, related information is frequently distributed across methodology, results, and discussion.
Flat, fixed-size chunking (Option A) can split these logically connected ideas into separate chunks, causing retrieval to surface fragments that match a term but not the full intent. Semantic chunking (Option C) improves boundary placement, but it does not inherently provide a multi-resolution structure that helps preserve section-level continuity at massive scale.
Hierarchical chunking solves this by creating parent chunks (larger context windows) that capture broader section context and child chunks (smaller units) that retain retrieval precision. When the retriever identifies relevant child chunks, it can also bring in the associated parent context so the foundation model sees the surrounding methodological or discussion framing. The defined overlaps further reduce the risk that key transitions or references are split across chunks.
This approach is well suited for a corpus of 25 million papers because it improves relevance without requiring a custom reranking model or a manual preprocessing pipeline. It remains operationally efficient because it is configured at the knowledge base level rather than implemented through custom code per document.
Option D introduces high operational complexity and inconsistent document handling at scale. Therefore, Option B best meets the requirement to preserve semantic context across related paragraphs and improve citation relevance across scientific paper sections.
질문 # 127
A company wants to implement an Amazon Bedrock application that helps lawyers search thousands of legal documents. The solution must give lawyers the ability to query based on the type of legal case, jurisdiction, and case outcome. Queries must return information about document authors and versions. The solution must integrate with the company ' s existing tools and support future growth in volume and query complexity.
Which solution will meet these requirements?
정답：D
설명：
Option B is correct because Amazon Bedrock Knowledge Bases supports structured document metadata that can be stored separately from the text and used as filters during retrieval. Case type, jurisdiction, and case outcome are ideal filterable metadata attributes, while author and version fields preserve provenance for returned documents. AWS documentation confirms that S3 knowledge-base data sources can use sidecar metadata files and that metadata remains available for filtering even when it is excluded from the embedding.
This gives the application precise semantic retrieval plus structured constraints without custom extraction at query time. Option A lacks structured filtering. Option C adds unnecessary Comprehend processing. Option D embeds metadata inside document text, which does not provide reliable field-level filtering and adds unnecessary agent-side extraction complexity. AWS Documentation
질문 # 128
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