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Amazon AIP-C01 考試大綱：

主題	簡介
主題 1	Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
主題 2	Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.
主題 3	Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
主題 4	AI Safety, Security, and Governance: This domain addresses input output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.
主題 5	Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.

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最新的 Amazon Professional AIP-C01 免費考試真題 (Q33-Q38):

問題 #33
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain- specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon Bedrock knowledge base to store the reference medical documents. Enable query decomposition in the knowledge base. Configure an Amazon Bedrock flow that uses a foundation model and the knowledge base to support the application.
B. Use Amazon SageMaker AI to host custom ML models for both query decomposition and query expansion. Configure Amazon Bedrock knowledge bases to store the reference medical documents.
Encrypt the documents in the knowledge base.
C. Use Amazon API Gateway to route incoming queries to an Amazon Bedrock agent. Configure the agent to use an Anthropic Claude model to decompose queries and an Amazon Titan model to expand queries. Create an Amazon Bedrock knowledge base to store the reference medical documents.
D. Create an Amazon Bedrock agent to orchestrate multiple AWS Lambda functions to decompose queries. Create an Amazon Bedrock knowledge base to store the reference medical documents. Use the agent's built-in knowledge base capabilities. Add deep research and reasoning capabilities to the agent to reduce ambiguity in the medical terminology.

答案：A

解題說明：
Option B provides the least operational overhead because it keeps the solution primarily inside managed Amazon Bedrock capabilities, minimizing custom orchestration code and infrastructure to operate. The core requirements are domain grounding, reduced semantic dilution for complex questions, and consistent low- latency responses at high request volume. A Bedrock knowledge base is purpose-built for Retrieval Augmented Generation by ingesting domain documents, chunking content, generating embeddings, and retrieving the most relevant passages at runtime. This directly addresses the need to reference domain-specific medical terminology from authoritative documents to reduce ambiguity and improve factual accuracy.
Reducing semantic dilution typically requires improving the retrieval query so that the retriever focuses on the most relevant concepts, especially for long or multi-intent questions. Enabling query decomposition allows the system to break a complex medical query into smaller, more targeted sub-queries. This increases retrieval precision and recall for each sub-question, which helps the model generate a more accurate synthesized response grounded in the retrieved medical context.
Amazon Bedrock Flows provide a managed way to orchestrate multi-step generative AI workflows, such as preprocessing the input, performing retrieval against the knowledge base, invoking a foundation model, and formatting the final response. Because flows are managed, the company avoids maintaining custom state machines, multiple Lambda functions, or bespoke routing logic. This reduces operational overhead while still supporting repeatable, observable execution.
Compared with the alternatives, option A introduces an agent plus API Gateway routing and multiple model choices, increasing configuration and runtime complexity. Option C requires hosting and scaling custom models on SageMaker AI, which adds significant operational burden and latency risk. Option D relies on multiple Lambda functions orchestrated by an agent, which adds more moving parts and increases cold-start and integration overhead. Option B most directly meets the requirements with the smallest operational footprint.

問題 #34
A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM.
Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.
Which combination of solutions will meet this requirement? (Select TWO.)

A. Set the minimum number of instances to greater than 0. Enable response streaming.
B. Switch to a multi-model endpoint. Use lazy loading without request batching.
C. Switch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an Amazon S3 bucket. Set the minimum number of instances to 0.
D. Select a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.
E. Enable model preload upon container startup. Implement dynamic batching to process multiple user requests together in a single inference pass.

答案：A,E

問題 #35
A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models.
The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.
The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.
Which solution will meet these requirements?

A. Store model invocation logs in an Amazon S3 bucket. Use AWS Lambda to process logs in real time.Manually update CloudWatch alarm thresholds based on trends identified by the Lambda function.
B. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch dashboards for token metrics. Configure static CloudWatch alarms with fixed thresholds for each tool integration.
C. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms that automatically adjust baselines for each tool's token metrics.
D. Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token usage trends.

答案：C

解題說明：
Option C best meets the requirements by combining native Amazon Bedrock logging with adaptive monitoring and minimal operational overhead. Amazon Bedrock model invocation logging can be sent directly to CloudWatch Logs, where detailed fields such as InputTokenCount, OutputTokenCount, and tool invocation metadata are captured for each request.
CloudWatch metric filters allow extraction of structured metrics from logs, including tool-specific token consumption patterns. By defining filters per tool integration, the company can isolate which tools are responsible for increased token usage without building custom log-processing pipelines.
CloudWatch anomaly detection provides automatic baseline modeling and dynamic thresholds based on historical traffic patterns. Unlike static alarms, anomaly detection adapts as usage evolves, making it ideal for applications with changing workloads or seasonal usage patterns. This directly satisfies the requirement to automatically adjust thresholds as traffic patterns change.
When abnormal token consumption occurs, anomaly detection alarms trigger immediately, enabling rapid investigation and remediation. Because this solution uses fully managed AWS services without custom analytics jobs or manual threshold tuning, it significantly reduces operational effort.
Option A fails to adapt to changing patterns. Option B introduces batch analysis and delayed insights. Option D requires manual intervention and custom code, increasing maintenance burden.
Therefore, Option C provides the most scalable, adaptive, and low-maintenance solution for monitoring and controlling token consumption in Amazon Bedrock-based applications.

問題 #36
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company ' s approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)

A. Set a low grounding score threshold.
B. Configure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.
C. Add the high-risk conversation patterns to a denied topics guardrail.
D. Configure a content filter guardrail to filter prompts that contain competitor names.
E. Add the names of competitors as custom word filters. Set the input and output actions to block.
F. Set a high grounding score threshold.

答案：C,E,F

問題 #37
A global research company is building a RAG-enabled AI system that uses Amazon Bedrock Knowledge Bases. The company stores documents in Amazon S3 and indexes the documents into an Amazon OpenSearch Serverless vector collection.
When the company evaluates the system, the company identifies three issues. Queries return outdated documents when users request only recent research. Medical research queries return both medical research documents and engineering domain documents. Users can retrieve documents that were authored by researchers who the users should not have access to based on company policies.
The company wants to improve the system so that retrieval becomes more precise and contextually appropriate.
Which solution will meet these requirements?

A. Use S3 object metadata to store each document ' s timestamp. Use a custom metadata field to record authorship. Use S3 object tags to record the research domain. Propagate the metadata fields into the OpenSearch Serverless vector collection as filterable attributes. Use Knowledge Bases to apply timestamp, author, and domain filters before running vector similarity search.
B. Store documents in Amazon S3. Extract timestamp, author metadata, and research-domain tags, and store the data in an Amazon DynamoDB table. During retrieval, apply author, domain, and timestamp filters in DynamoDB to identify candidate document IDs. Use the filtered document IDs to narrow the vector similarity search in the OpenSearch Serverless collection.
C. Store documents in Amazon S3 with custom metadata to record authorship. Use Amazon Comprehend to classify each document into a research domain. Store the classification results in Amazon Aurora.Query Aurora during retrieval to identify relevant domains before performing vector similarity search in the OpenSearch Serverless collection.
D. Add custom metadata fields to the documents in Amazon S3 to record timestamp, authorship, and research domain. Index the document embeddings and the custom metadata fields into the OpenSearch Serverless vector collection. At query time, use the knowledge base to run vector similarity search and return the stored metadata with the results to help the model interpret document relevance.

答案：A

解題說明：
Option B is correct because the retrieval problem requires filtering before semantic similarity search, not merely returning metadata after retrieval. Amazon Bedrock Knowledge Bases can apply filters to metadata associated with source documents, so timestamp, author, and research-domain attributes can restrict the candidate set before vector results are returned. This prevents old, wrong-domain, or unauthorized-author documents from entering the retrieved context. AWS also documents S3 knowledge-base metadata files and filterable metadata attributes. Option A only returns metadata with already-selected vector results, so irrelevant documents can still be retrieved. Option C introduces DynamoDB and a two-stage custom retrieval path. Option D adds Comprehend and Aurora, increasing operational complexity. The key design principle is to make the required attributes filterable in the vector retrieval layer. AWS Documentation

問題 #38
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