참고: Fast2test에서 Google Drive로 공유하는 무료, 최신 AIP-C01 시험 문제집이 있습니다: https://drive.google.com/open?id=1n4JC8IQgn9vcAyCMVN_fx6117ZWBrf2c
Amazon 인증 AIP-C01시험이 너무 어려워서 시험 볼 엄두도 나지 않는다구요? Fast2test 덤프만 공부하신다면 IT인증시험공부고민은 이젠 그만 하셔도 됩니다. Fast2test에서 제공해드리는Amazon 인증 AIP-C01시험대비 덤프는 덤프제공사이트에서 가장 최신버전이여서 시험패스는 한방에 갑니다. Amazon 인증 AIP-C01시험뿐만 아니라 IT인증시험에 관한 모든 시험에 대비한 덤프를 제공해드립니다. 많은 애용 바랍니다.
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Fast2test에서 제공하는 제품들은 품질이 아주 좋으며 또 업뎃속도도 아주 빠릅니다 만약 우리가제공하는Amazon AIP-C01인증시험관련 덤프를 구매하신다면Amazon AIP-C01시험은 손쉽게 성공적으로 패스하실 수 있습니다.
질문 # 127
An ecommerce company is developing a generative AI (GenAI) solution that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale or are not relevant. Customers also report long response times for some recommendations.
The company confirms that most customer interactions are unique and that the solution recommends products not present in the product catalog.
Which solution will meet this requirement?
정답：A
설명：
Option C is the correct solution because it directly addresses both correctness and performance issues by grounding the model's responses in authoritative product data using Retrieval Augmented Generation.
Amazon Bedrock Knowledge Bases are designed to connect foundation models to trusted enterprise data sources, ensuring that generated responses are constrained to known, validated content.
By ingesting the product catalog into a knowledge base, the GenAI application retrieves only products that actually exist in the catalog. This prevents hallucinated or unavailable recommendations, which is a common issue when models rely solely on prompt instructions without retrieval grounding. RAG ensures that the model's output is based on retrieved facts rather than learned generalizations.
Setting the PerformanceConfigLatency parameter to optimized enables Bedrock to prioritize lower-latency retrieval and inference paths, improving responsiveness for real-time recommendation scenarios. This directly addresses the reported performance issues without requiring provisioned throughput or caching strategies that are ineffective for mostly unique interactions.
Option A improves safety and latency predictability but does not ensure recommendations are limited to valid products. Option B relies on prompt constraints, which are not sufficient to prevent hallucinations. Option D introduces additional validation and caching layers but increases complexity and does not improve generation relevance.
Therefore, Option C best resolves both relevance and latency challenges using AWS-native, low-maintenance GenAI integration patterns.
질문 # 128
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses. The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes. Which solution will meet these requirements?
정답：C
설명：
Amazon SageMaker Serverless Inference is specifically designed for applications that experience intermittent or bursty traffic. It automatically scales compute capacity based on the number of requests and scales down to zero when there is no traffic, satisfying the requirement to optimize resource usage during low demand. To meet the 500 ms latency requirement during peak periods and avoid " cold start " delays, provisioned concurrency keeps a specified number of execution environments warm and ready to respond immediately. This provides a balance between the cost-effectiveness of serverless and the performance predictability of provisioned instances. Multi-model endpoints (Option A) can introduce " noisy neighbor " issues and latency spikes, while asynchronous inference (Option D) is intended for long-running workloads and cannot meet sub-500 ms requirements.
질문 # 129
A company is using AWS Lambda and REST APIs to build a reasoning agent to automate support workflows.
The system must preserve memory across interactions, share relevant agent state, and support event-driven invocation and synchronous invocation. The system must also enforce access control and session-based permissions.
Which combination of steps provides the MOST scalable solution? (Select TWO.)
정답：C,D
설명：
The combination of Options A and B provides the most scalable and AWS-native architecture for building reasoning agents with persistent memory, session awareness, secure access control, and flexible invocation models.
Amazon Bedrock AgentCore is purpose-built to manage agent memory, session context, and identity-aware reasoning across interactions. It eliminates the need for developers to manually store and retrieve agent state, manage session lifecycles, or implement custom memory layers. AgentCore natively supports both synchronous requests and event-driven execution, making it ideal for support workflow automation.
Option B complements AgentCore by enabling seamless tool invocation. By registering AWS Lambda functions and REST APIs as agent actions through API Gateway and EventBridge, the agent can invoke tools reactively or synchronously without custom orchestration code. EventBridge enables event-driven execution, while API Gateway supports synchronous request-response patterns.
This combination provides built-in security, observability, and scaling, while avoiding the operational burden of managing queues, databases, or custom workflow engines.
Option C introduces unnecessary orchestration complexity. Option D increases infrastructure management and cost. Option E stores agent state in S3, which is not suitable for low-latency, session-based reasoning.
Therefore, A and B together deliver the most scalable, secure, and low-overhead solution for production- grade reasoning agents on AWS.
질문 # 130
A company is implementing a serverless inference API by using AWS Lambda. The API will dynamically invoke multiple AI models hosted on Amazon Bedrock. The company needs to design a solution that can switch between model providers without modifying or redeploying Lambda code in real time. The design must include safe rollout of configuration changes and validation and rollback capabilities.
Which solution will meet these requirements?
정답：D
설명：
Option B is the correct solution because AWS AppConfig is specifically designed to support dynamic configuration management with safe rollout, validation, and rollback, which are explicit requirements in the scenario.
By storing the active model provider configuration in AWS AppConfig, the company can switch between Amazon Bedrock model providers in real time without redeploying Lambda code. AppConfig supports deployment strategies such as canary releases, linear rollouts, and immediate deployments, allowing safe and controlled changes. If a configuration causes issues, AppConfig supports automatic rollback, reducing operational risk.
AWS AppConfig also supports schema validation, ensuring that configuration values such as model identifiers, provider names, or inference parameters are valid before being applied. This prevents misconfiguration from impacting production workloads.
Option A uses Parameter Store, which lacks native rollout strategies, validation, and automated rollback, making it unsuitable for safe real-time switching. Option C requires manual routing changes and code coupling, increasing operational overhead and deployment risk. Option D introduces unnecessary complexity by hosting configuration files in Amazon S3 when AppConfig already supports native hosted configurations.
Therefore, Option B provides the most robust, scalable, and low-maintenance solution for dynamic model switching in a serverless Amazon Bedrock inference architecture.
질문 # 131
A company uses Amazon Bedrock to deploy an application that generates technical documentation for users across multiple AWS Regions and in multiple languages. Users frequently submit semantically similar questions in different languages, which results in increased inference costs and response latency.
The company needs a caching solution that significantly reduces inference costs, provides low-latency responses globally, maintains cache freshness with a 5-minute TTL, and minimizes custom cache key generation and application-managed caching logic.
Which solution will meet these requirements?
정답：D
설명：
Option C is the technically supportable choice under current AWS documentation. Amazon Bedrock prompt caching does not provide semantic caching for differently worded or cross-language questions; it reuses cached prompt content through matching prompt prefixes or defined cache checkpoints. Therefore, Option B ' s assumption that Bedrock prompt caching automatically recognizes semantically equivalent multilingual queries is incorrect, even though many supported models provide a 5-minute prompt-cache TTL. ElastiCache for Redis OSS supports application-controlled cache entries and TTLs, while Global Datastore provides managed cross-Region replication and low-latency geographic reads. A multilingual semantic fingerprint can normalize equivalent requests to a shared cache key before inference. Options A and D similarly lack built-in semantic equivalence handling, while DAX is specifically a DynamoDB caching layer. Thus, C is the viable architecture among the listed choices.
질문 # 132
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Amazon인증AIP-C01시험준비를 하고 계시다면Fast2test에서 출시한Amazon인증AIP-C01덤프를 제일 먼저 추천해드리고 싶습니다. Fast2test제품은 여러분들이 제일 간편한 방법으로 시험에서 고득점을 받을수 있도록 도와드리는 시험동반자입니다. Amazon인증AIP-C01시험패는Fast2test제품으로 고고고!
AIP-C01최신 업데이트 공부자료: https://kr.fast2test.com/AIP-C01-premium-file.html
그 외, Fast2test AIP-C01 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1n4JC8IQgn9vcAyCMVN_fx6117ZWBrf2c