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君はまずネットで無料な部分のAmazon認証試験をダウンロードして現場の試験の雰囲気を感じて試験に上手になりますよ。AmazonのAIP-C01認証試験に失敗したら弊社は全額で返金するのを保証いたします。
質問 # 99
A company is developing a generative AI (GenAI) application that analyzes customer service calls in real time and generates suggested responses for human customer service agents. The application must process
500,000 concurrent calls during peak hours with less than 200 ms end-to-end latency for each suggestion. The company uses existing architecture to transcribe customer call audio streams. The application must not exceed a predefined monthly compute budget and must maintain auto scaling capabilities.
Which solution will meet these requirements?
正解：D
解説：
Option B is the correct solution because it aligns with AWS guidance for building high-throughput, ultra-low- latency GenAI applications while maintaining predictable costs and automatic scaling. Amazon Bedrock provides access to foundation models that are specifically optimized for real-time inference use cases, including conversational and recommendation-style workloads that require responses within milliseconds.
Low-latency models in Amazon Bedrock are designed to handle very high request rates with minimal per- request overhead. Purchasing provisioned throughput ensures that sufficient model capacity is reserved to handle peak loads, eliminating cold starts and reducing request queuing during traffic surges. This is critical when supporting up to 500,000 concurrent calls with strict latency requirements.
Automatic scaling policies allow the application to dynamically adjust capacity based on demand, ensuring cost efficiency during off-peak hours while maintaining performance during peak usage. This directly supports the requirement to stay within a predefined monthly compute budget.
Option A fails because batch processing and complex reasoning models introduce higher latency and are not suitable for real-time suggestions. Option C introduces significantly higher operational and cost overhead due to dedicated GPU instances and manual scaling responsibilities. Option D is optimized for batch workloads and cannot meet the sub-200 ms latency requirement.
Therefore, Option B provides the best balance of performance, scalability, cost control, and operational simplicity using AWS-native GenAI services.
質問 # 100
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?
正解：C
解説：
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.
質問 # 101
A global financial services company hosts a fraud-alert system that uses an Amazon Bedrock foundation model (FM) to generate explanations for suspicious transactions. The company processes regulated financial data across three geographic areas. The system must maintain consistent responsiveness globally, support multi-Region failover, and provide full observability for audit and compliance teams.
Load testing shows that the FM's total inference time cannot be reduced. The company cannot increase its inference costs, change the FM, modify token counts, or provision additional compute capacity. Users report that the UI performs slowly because it waits for the complete model response before it shows any content.
The company must improve perceived responsiveness during peak periods, when the system can receive
10,000-15,000 concurrent requests. The solution must maintain multi-Region resiliency and full monitoring visibility.
Which solution will meet these requirements?
正解：A
解説：
Option A is correct because the problem is not total model runtime; the problem is perceived UI responsiveness. AWS documentation states that the Amazon Bedrock InvokeModelWithResponseStream API returns the model response as a stream. AWS also explains that streaming allows an application to access responses in chunks without waiting for the entire result. This lets the frontend display generated content as tokens or chunks arrive, improving the user experience even when the total inference duration remains unchanged.
This option also preserves the stated constraints. It does not require changing the foundation model, increasing token counts, provisioning extra compute, or reducing the model's actual inference time. Instead, it changes how the response is delivered to the user. This is exactly the right pattern when users perceive slowness because the interface waits for the full completion before rendering anything. AWS examples for Bedrock streaming show applications processing response streams in real time, which supports interactive applications during high-concurrency periods.
The monitoring portion of option A is also aligned with AWS guidance. Amazon Bedrock runtime publishes CloudWatch metrics for invocation volume, latency, token consumption, error rates, and model invocation logging delivery. CloudWatch GenAI observability can track invocation count, token usage, and errors using built-in views. This supports audit and compliance visibility across Regions.
Option B adds caching of partially processed fraud explanations, which can be risky for regulated transaction- specific explanations and does not solve the UI issue for unique requests. Option C changes inputs by condensing them, which violates the constraint not to modify token counts or inputs. Option D focuses on Lambda concurrency and log retention, which does not address waiting for the full model response.
Therefore, response streaming with CloudWatch and tracing is the correct solution.
質問 # 102
A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step- by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
正解：D
解説：
Option A is the best solution because it natively delivers retrieval grounding, source attribution, and low operational overhead through Amazon Bedrock Knowledge Bases. The key requirements are: retrieve from company data sources, cite sources, link claims to source documents, and keep latency under 3 seconds.
Knowledge Bases are a managed RAG capability that handles document ingestion, chunking, embeddings, retrieval, and assembly of context for model generation. This eliminates the need to build and maintain custom retrieval infrastructure.
Source attribution is crucial: the application must "link data claims to source documents." When source attribution is enabled, the RAG pipeline can return references to the underlying documents and segments used for generation. This enables traceable citations that can be surfaced to end users and used for internal auditing.
Using the Anthropic Claude Messages API (or equivalent conversational interface) with RAG allows the application to generate recommendations grounded in retrieved context while keeping responses conversational. Setting relevance thresholds helps reduce noisy retrieval, which supports both accuracy and latency targets by limiting the context passed to the model.
Storing reasoning and citations in Amazon S3 supports audit and retention needs with minimal operational burden. While the prompt may request step-by-step reasoning, AWS best practice is to produce user-facing explanations that are faithful and attributable without exposing internal reasoning traces unnecessarily. With source-grounded outputs, the system can provide concise rationale tied to citations while maintaining fast response times.
Option B emphasizes extended thinking, which increases latency and does not ensure source linkage. Option C adds significant operational overhead through custom model hosting and separate citation systems. Option D requires more custom tracking work than A while not improving retrieval attribution beyond what Knowledge Bases already provide.
Therefore, Option A best meets the requirements with the least operational overhead.
質問 # 103
A GenAI developer is using Amazon Bedrock AgentCore to build an agentic AI application. The application orchestrates multiple foundation models (FMs) across development and production environments. The application experiences intermittent failures during tool invocation phases. Each incident requires an average of 3 hours to diagnose because of complex error patterns in the FM chains.
The GenAI developer needs a solution that can identify AI-specific error patterns in tool invocation chains in less time than the existing manual process. The solution must support collaboration between development and security teams. The solution must provide capabilities to protect sensitive customer data.
Which solution will meet these requirements?
正解：D
解説：
Option D is correct because AgentCore Observability is purpose-built for tracing and troubleshooting agentic workloads. AgentCore traces capture the complete execution path, including processing steps, external service calls, tool invocations with inputs and outputs, execution times, decision points, exceptions, recovery attempts, and final responses. CloudWatch Generative AI Observability provides preconfigured views for agent, model, tool, latency, token, and error behavior, allowing development and security teams to investigate failures from shared telemetry instead of correlating several independent logging systems. AWS Distro for OpenTelemetry provides standardized instrumentation for non-runtime agents and related application components. CloudWatch also provides sensitive-data protection capabilities for observability data. Options A and C provide infrastructure-focused monitoring but require more manual correlation and custom dashboards. Option B does not provide the same production-grade centralized AgentCore telemetry. AWS Documentation
質問 # 104
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AIP-C01認定資格試験: https://www.jpntest.com/shiken/AIP-C01-mondaishu
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