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| Section | Weight | Objectives |
|---|---|---|
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Performance optimization - Cost optimization and token efficiency |
| Foundation Model Integration, Data Management, and Compliance | 31% | - Compliance and responsible AI practices - Data management and RAG architectures - Foundation model integration and usage |
| AI Safety, Security, and Governance | 20% | - Guardrails and responsible AI implementation - Security controls for GenAI applications |
| Testing, Validation, and Troubleshooting | 11% | - Troubleshooting GenAI systems - Monitoring and observability - Model evaluation and testing |
| Implementation and Integration | 26% | - Embedding FMs into applications and workflows - Agent-based AI systems - Prompt engineering and prompt management |
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問題 #27
A multinational bank wants to implement a RAG solution on AWS to run queries on internal policy and compliance documents. To comply with data residency regulations, the company must ensure that critical customer data remains within a specific AWS Region. The bank wants to use foundation models (FMs) on AWS to reduce infrastructure costs and minimize model maintenance.
Which solution will meet these requirements?
答案：D
解題說明：
Option A is the appropriate AWS-native approach because it keeps the authoritative documents in an Amazon S3 bucket located in the regulated Region while using managed Amazon Bedrock foundation-model infrastructure instead of maintaining containerized models. Amazon Bedrock supports private connectivity through AWS PrivateLink interface VPC endpoints. Applications inside the VPC can therefore reach Bedrock privately without requiring an internet gateway, NAT gateway, public IP address, or public internet path.
Amazon Bedrock Knowledge Bases can use Amazon S3 as a RAG data source. AWS requires the S3 bucket used by a knowledge base to be in the same Region as the Amazon Bedrock knowledge base. During ingestion, source content is converted into vector embeddings and stored in the configured vector store so similarity-based retrieval can locate relevant passages for augmentation. This creates an AWS-managed RAG architecture while preserving explicit regional placement of the underlying source documents.
Option B introduces AWS Outposts unnecessarily. More importantly, AWS documentation states that S3 on Outposts object data physically remains on the Outpost and "is not in an AWS Region." That conflicts with a requirement written specifically as keeping data within a particular AWS Region and also introduces additional infrastructure and operational cost.
C fails because "a Region close to" the regulated Region does not meet a requirement for the specified Region. Encryption does not override residency requirements. D requires the company to deploy and operate foundation-model containers, directly contradicting the objective of minimizing model infrastructure and maintenance.
Therefore, regional S3 storage combined with privately accessed managed Amazon Bedrock services provides the closest match to the stated residency, RAG, cost, and operational requirements.
問題 #28
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
答案：C
解題說明：
Option C best satisfies the requirement to change routing decisions without redeploying code while supporting complex, frequently changing business logic at scale. AWS AppConfig is designed for centrally managing dynamic configuration (feature flags, rules, thresholds, and policy parameters) and deploying changes safely. It supports controlled deployments, validation, and rapid propagation of updated configuration values, which aligns with "real-time cost metrics that change hourly" and the need for "immediate propagation across thousands of concurrent requests." In this design, the Lambda function becomes the policy decision point. For each request, it evaluates user attributes (tier, transaction value), context (regulatory zone, Region), and live cost/performance thresholds stored in AppConfig to determine which Amazon Bedrock FM to invoke. Because the routing rules and FM identifiers are delivered as configuration, the company can switch models, adjust A/B testing weights, or update compliance routing rules by deploying new AppConfig configuration versions rather than pushing new application code. This reduces operational risk and accelerates iteration.
Exposing a single API Gateway endpoint also minimizes client complexity and keeps routing logic server- side, which is important when rules change frequently. Lambda can cache configuration between invocations (within the execution environment) to reduce repeated fetch overhead while still picking up changes quickly, enabling both low latency and rapid rule rollout under high concurrency.
Option A relies on Lambda environment variables, which are not intended for frequent real-time updates and typically require function configuration updates that are slower and operationally brittle. Option B uses mapping templates and stage variables, which are limited for complex rule evaluation and safe rollout patterns. Option D misuses authorizers for business routing, adds extra latency and complexity, and complicates observability and error handling by splitting decisioning from execution.
問題 #29
A healthcare company is building a generative AI (GenAI) application to answer patient questions based on patient medical records and published treatment guidelines. To comply with healthcare regulations, the application must provide transparent citations for all information sources. The application must also maintain an audit trail for all data access and ensure that responses reference only authorized medical information.
Which solution will meet these requirements with the LEAST operational overhead?
答案：C
解題說明：
Option B uses managed retrieval and observability instead of custom citation infrastructure. Amazon Bedrock Knowledge Bases can connect applications to controlled enterprise data sources and perform retrieval- augmented generation. The RetrieveAndGenerate workflow returns generated answers together with citations to the specific source chunks used, directly addressing the transparency requirement. Access to the knowledge base and its backing resources can be governed through AWS authorization controls so the application retrieves only permitted medical information. Bedrock invocation logging and CloudWatch observability can record invocation activity, inputs, outputs, usage, and operational events to support investigation and auditing.
Option A does not supply the required source-grounded retrieval and citations by itself. Option C requires a custom external vector system and Lambda-based citation tracking. Option D adds fine-tuning and custom attribution infrastructure even though neither is necessary for a source-grounded healthcare RAG application.
AWS Documentation
問題 #30
A transportation company is building a trip planning AI assistant that users access through a mobile app or a web portal. The AI assistant must interpret natural-language user requests that include departure times, accessibility needs, and transfer preferences. The AI assistant must query appropriate backend APIs to route requests, calculate fares, and retrieve information about travel disruptions.
The company must ensure that the AI assistant generates all fare prices in a deterministic way. The AI assistant must never generate routes or fares on its own. The company must be able to audit all interactions for regulatory review. The company must be able to add new backend services in the future without re-training or modifying the model.
Which solution will meet these requirements?
答案：C
解題說明：
Option B is correct because authoritative route and fare calculations must remain outside the language model.
AgentCore Gateway can expose backend APIs and Lambda functions as governed tools through a unified interface, allowing the agent to discover and invoke the appropriate service while the deterministic business systems calculate routes, fares, accessibility constraints, and disruptions. Strict tool schemas constrain inputs and outputs, while the model can be limited to interpreting user intent and explaining returned results rather than inventing prices. AgentCore observability provides traces of tool calls and execution paths for regulatory review. New services can be added as Gateway targets without retraining the model. Option A relies on prompt instructions, which cannot guarantee deterministic fares. Option C lets the model synthesize answers from documents. Option D improves orchestration but still does not establish authoritative deterministic tool boundaries. AWS Documentation
問題 #31
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.
Which solution will meet these requirements with the LEAST custom development effort?
答案：D
解題說明：
Option B best satisfies the requirements with the least custom development effort by using native Amazon Bedrock capabilities for prompt experimentation, traffic management, fairness monitoring, and alerting.
Amazon Bedrock Prompt Management allows teams to define and manage multiple prompt variants without code changes, making it ideal for comparing recommendation strategies across demographic groups.
Amazon Bedrock Flows enables controlled traffic allocation between prompt variants, which supports real- time A/B testing. This allows the company to collect live fairness metrics under production conditions instead of relying on offline analysis. Because Flows are fully managed, they eliminate the need for custom routing or experimentation frameworks.
Amazon Bedrock guardrails provide built-in monitoring and intervention mechanisms. When configured for fairness-related checks, guardrails can detect policy violations and surface metrics such as InvocationsIntervened, which indicate when outputs are modified or blocked due to rule enforcement. These metrics integrate directly with Amazon CloudWatch, enabling real-time dashboards and threshold-based alarms. Setting an alarm at a 15% discrepancy threshold satisfies the alerting requirement with minimal configuration.
Weekly reporting can be generated from CloudWatch metrics using scheduled exports or dashboards without building custom analytics pipelines. Option A requires significant custom post-processing logic. Option C introduces an additional service with higher operational overhead and is not optimized for real-time monitoring. Option D focuses on offline evaluation jobs and does not provide continuous real-time fairness monitoring.
Therefore, Option B provides the most AWS-native, scalable, and low-effort solution for fairness evaluation and monitoring.
問題 #32
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AIP-C01資料: https://www.vcesoft.com/AIP-C01-pdf.html
P.S. VCESoft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=1D-EZ6_kwyrZBHLrpmT4Br4g5lflSg21F