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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 |
| Exam Format: | Matching, Ordering, Multiple response, Multiple choice |
| Available Languages: | English, Korean, Japanese, Simplified Chinese |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Passing Score: | 750 (scaled score 100–1000) |
| Certificate Validity Period: | 3 years |
| Exam Price: | 300 USD |
| Recommended Training: | AWS Certified Generative AI Developer - Professional Exam Guide AWS Skill Builder - Official Training |
| Exam Registration: | Pearson VUE Registration AWS Certification Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 2+ years of experience building production-grade applications on AWS or open-source technologies; 1+ year hands-on experience with generative AI implementation; knowledge of AWS compute, storage, networking, security, and deployment tools |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/examguides/ai-professional-01.html |
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질문 # 130
A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions.
The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe.
Which solution will meet these requirements?
정답：B
설명：
Option D is correct because it separates the United States and European workloads and uses the correct Amazon Bedrock inference pattern for European data residency. AWS documentation states that geographic cross-Region inference keeps data processing within specified geographic boundaries such as US, EU, and APAC, and is intended for organizations with data residency and compliance requirements. The regional availability documentation further explains that a geographic profile routes within a defined geography and that prompts and outputs may move within that geography but not outside it. This directly satisfies the requirement that European customer data must be processed only in Europe-based AWS Regions.
Provisioned throughput is appropriate for the US workload because the system has a demanding and predictable performance target: 1,000 transactions per second with sub-500 ms response times. Provisioned capacity is generally used when an application needs reserved throughput and consistent performance rather than depending only on on-demand capacity. Automatic scaling based on utilization metrics supports changing demand while maintaining performance. EventBridge and Lambda can coordinate failover workflows or routing updates when connectivity disruptions occur, supporting high availability across Regions.
Option A is not the best answer because it includes a global inference profile. AWS documentation states that global cross-Region inference can route requests across supported commercial AWS Regions worldwide and has no geographic data-residency restriction. AWS explicitly notes that organizations with data residency or compliance requirements should assess whether global cross-Region inference fits their framework because requests may be processed in other supported commercial Regions. That conflicts with the European data processing requirement.
Option B uses separate Regional provisioned throughput, but it relies on a custom Application Load Balancer approach for Bedrock routing and does not use the Bedrock geographic inference profile that is designed for EU data residency. Option C uses response streaming, which improves perceived response latency for text generation but does not guarantee sub-500 ms fraud-processing latency or European-only processing.
Therefore, option D is the most complete AWS-aligned solution.
질문 # 131
A financial services company is deploying a GenAI assistant that uses Amazon Bedrock to answer customer questions about account activity and company policies. The company must comply with responsible AI principles and governance requirements.
The solution must meet the following requirements:
* Prevent harmful, biased, or non-compliant responses.
* Ensure consistent policy enforcement across all model invocations.
* Provide traceability and auditability for AI-generated outputs.
* Maintain developer productivity without embedding complex safety logic in application code.
Which solution will meet these requirements?
정답：A
설명：
Amazon Bedrock Guardrails is the AWS-managed mechanism specifically intended to implement consistent generative-AI safeguards without forcing developers to reproduce moderation logic throughout application code. Guardrails supports configurable content filters for categories including hate, insults, sexual content, violence, misconduct, and prompt attacks. It also supports denied topics, custom word filters, and sensitive- information filters for PII and other patterns.
For a financial-services assistant, denied topics can prevent conversations outside approved policy boundaries, while content filtering can prevent harmful or inappropriate responses. Sensitive-information controls can detect and filter regulated or personally identifiable information. Because a guardrail configuration is applied as a managed policy during inference, the same safeguards can be applied consistently across invocations rather than depending on every developer to implement identical conditional logic correctly.
Logging model interactions adds the required traceability layer. Operational records of prompts, model invocations, blocked interactions, and responses can support investigations, compliance review, and responsible-AI governance. This is materially stronger than merely establishing behavioral expectations in prompt text.
B is insufficient because system prompts are instructions to the model rather than an independent enforcement mechanism. A model can still generate undesirable content or encounter adversarial inputs despite well- engineered prompts. C can implement controls but creates custom application logic that must be maintained, tested, and kept synchronized across every application path, which conflicts with the productivity requirement. D is entirely retrospective; periodic review can identify violations after they occur but does not prevent an unsafe response from reaching the customer.
The architectural principle is to separate deterministic governance controls from application prompting.
Guardrails supplies a reusable policy layer around foundation-model interactions, while centralized logging provides an auditable operational record. Consequently, A satisfies safety enforcement, consistency, governance, auditability, and developer-productivity requirements together.
질문 # 132
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：D
설명：
Option A best matches the requirement because managed knowledge-base retrieval provides source attribution and lets access be governed through IAM, avoiding custom result-merging logic. Amazon Bedrock RetrieveAndGenerate returns citations tied to retrieved source references. One implementation detail matters under current AWS documentation: structured Knowledge Bases use Amazon Redshift as the query engine; Amazon Redshift federated queries can access Amazon RDS and Aurora operational databases. Therefore, the RDS-backed knowledge-base leg should be implemented through that supported structured-data path rather than interpreted as a direct native RDS vector connector. Options B, C, and D introduce extra agents or Lambda-based retrieval/merging, which increases development effort and makes unified citation handling more custom. A keeps both information sources behind managed retrieval and preserves provenance while IAM controls access. AWS Documentation
질문 # 133
A financial services company deploys a RAG application. The application uses Amazon OpenSearch Service to provide vector storage and Amazon Bedrock to generate text. The application ingests thousands of documents daily and processes hundreds of user queries each hour.
Several weeks after the deployment, users report increased response times despite sufficient compute resources. The company needs a monitoring solution that proactively identifies performance issues across the entire RAG pipeline.
Which solution will meet this requirement?
정답：A
설명：
Option D is correct because troubleshooting a RAG application requires observability across retrieval, generation, and ingestion rather than monitoring infrastructure resources in isolation. Amazon OpenSearch Service exposes search and indexing latency metrics, while Amazon Bedrock publishes InvocationLatency and other inference metrics through CloudWatch. CloudWatch anomaly detection continuously analyzes historical metric behavior and establishes expected-value bands, allowing abnormal latency or ingestion behavior to be identified without maintaining rigid static thresholds. This makes it suitable for detecting gradual performance degradation before a fixed alarm threshold is crossed. Option A is reactive and monitors only part of the pipeline. Option B incorrectly treats instance restarts as a general performance remedy.
Option C lacks end-to-end correlation and uses static thresholds that do not adapt to changing workload patterns. AWS Documentation
질문 # 134
A company is building a custom agentic application. The company must have fine-grained control over the agent orchestration loop. The application must implement custom logic to select tools, handle multi-turn conversations that involve complex state management, integrate with proprietary logging systems, and implement custom retry strategies for tool failures.
The company wants to use Amazon Bedrock FMs but must have full control over the orchestration logic. The company has expertise in building orchestration logic but wants to use AWS infrastructure to manage model inference and tool execution.
Which solution will meet these requirements?
정답：B
설명：
Option B matches the defining requirement: the company, rather than a managed agent harness, must own the orchestration loop . Amazon Bedrock AgentCore Runtime provides a serverless, purpose-built environment for hosting agents and tools while remaining framework agnostic. AWS explicitly supports agents created with frameworks such as LangGraph, Strands, and CrewAI as well as fully custom agents that use no agent framework.
AWS further distinguishes AgentCore Runtime from its managed harness by stating that with Runtime, the customer brings the agent code and "the orchestration loop is yours." The custom application can therefore implement proprietary tool-selection logic, complex conversation-state transitions, specialized retry algorithms, proprietary telemetry, validation stages, and any other control-flow behavior required by the business. Runtime supplies infrastructure capabilities such as isolation, scaling, sessions, authentication, and observability plumbing rather than taking ownership of the decision loop.
Amazon Bedrock can separately provide managed foundation-model inference, allowing the company to use Bedrock FMs without deploying or operating model-serving infrastructure.
A is incorrect specifically because of its final statement. AgentCore Policy is an authorization mechanism, not an orchestration or retry engine. AWS documents that Policy evaluates Cedar or Dogwood policies to determine whether tool invocations are permitted or forbidden. It can govern which actions are authorized, but it does not implement an agent ' s tool-selection algorithm or failure-retry strategy.
C relinquishes precisely the orchestration control the company requires by depending on managed behavior.
D could provide deterministic workflow coordination but places Step Functions outside the agent loop and is cumbersome for highly dynamic multi-turn agent reasoning, proprietary state transitions, and tool-selection decisions.
Therefore, B preserves complete application-level orchestration control while outsourcing scalable runtime infrastructure and foundation-model inference to AWS.
질문 # 135
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