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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional (AIP-C01) |
| Exam Number: | AIP-C01 |
| Exam Format: | Multiple choice, Multiple response |
| Real Exam Qty: | 75 scored questions + 10 unscored questions |
| Related Certifications: | AWS Certified Solutions Architect - Associate AWS Certified Data Engineer - Associate AWS Certified AI Practitioner AWS Certified Machine Learning Engineer - Associate |
| Exam Duration: | 180 minutes |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD 300 |
| Passing Score: | 750 (scaled score, range 100–1000) |
| Available Languages: | English, Simplified Chinese, Japanese, Korean |
| Recommended Training: | AWS Skill Builder GenAI Learning Path Amazon Bedrock Documentation |
| Exam Registration: | Official AWS Certification Page AWS Skill Builder Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite testing center (Pearson VUE) |
| Pre Condition: | Recommended: 2+ years AWS or cloud development experience and 1 year hands-on GenAI implementation experience |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-generative-ai-developer-professional/ |
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질문 # 152
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) 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?
정답：A
설명：
Option C is the correct solution because AWS AppConfig is designed for real-time, validated, centrally managed configuration changes with safe rollout, immediate propagation, and rollback support-exactly matching the company's requirements.
By storing routing rules, cost thresholds, regulatory constraints, and A/B testing logic in AWS AppConfig, the company can switch between Amazon Bedrock foundation models without redeploying Lambda code.
AppConfig supports feature flags, dynamic configuration updates, JSON schema validation, and staged rollouts, which are essential for safely managing complex and frequently changing routing logic.
Using the AWS AppConfig Agent, Lambda functions can retrieve cached configurations efficiently, ensuring low latency even under thousands of concurrent requests. This approach allows the Lambda function to apply proprietary business logic-such as user tier, transaction value, Region compliance, and real-time cost metrics-before selecting the appropriate FM.
Option A is operationally fragile because environment variable changes require function restarts and do not support validation or controlled rollouts. Option B is too limited for complex, dynamic logic and is difficult to maintain at scale. Option D misuses Lambda authorizers, which are intended for authentication and authorization, not high-frequency dynamic routing decisions.
Therefore, Option C provides the most scalable, flexible, and low-overhead architecture for dynamic, regulation-aware FM routing in a global GenAI system.
질문 # 153
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgreSQL database.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?
정답：C
설명：
Option D requires the least development effort because it uses a managed retrieval workflow that bundles the most time-consuming parts of semantic search: embedding generation, vector indexing, and natural language retrieval. With an Amazon Bedrock knowledge base, the application does not need to implement and operate separate services to (1) generate embeddings for hundreds of millions of records, (2) store and manage vectors, (3) build query-time embedding conversion logic, and (4) implement k-NN search orchestration.
Instead, the knowledge base is configured to automatically create embeddings during ingestion, and the application queries it using the Amazon Bedrock Retrieve API, which accepts natural language input and performs the vector search as a managed capability.
The performance requirement (95% of queries within 500 ms) is best served by a purpose-built vector search backend rather than running similarity search directly inside a transactional PostgreSQL system at this scale.
A knowledge base is designed for retrieval patterns and can be backed by scalable vector stores, which helps meet latency goals under heavy concurrency. The hourly freshness requirement maps naturally to ingestion updates: the pipeline can re-ingest updated restaurant details on a schedule so the knowledge base remains current without building custom re-embedding workflows in application code.
Cost-effective scaling during peak periods is also easier with a managed retrieval layer because scaling the retrieval workload is separated from the operational database. This avoids overprovisioning PostgreSQL for peak semantic-search traffic and reduces the engineering effort to tune performance, sharding, indexing, and retry logic.
Options B and C can work, but they require the team to build and maintain embedding pipelines, query embedding generation, vector index management, and operational scaling strategies. Option A does not provide semantic search because it relies on keyword-based matching rather than embeddings.
질문 # 154
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?
정답：A
질문 # 155
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.)
정답：B,E
설명：
The correct answers are A and D because they directly reduce time-to-first-token and stabilize p95 latency for interactive, real-time chat workloads hosted on Amazon SageMaker AI real-time endpoints.
Option D addresses the biggest driver of uneven latency: cold starts and scale-to-zero behavior. By setting the minimum number of instances to greater than 0, the endpoint always has warm capacity and loaded runtime resources, eliminating the first-request penalty that causes users to wait multiple seconds. Enabling response streaming improves perceived latency by returning the first tokens as soon as they are generated rather than waiting for the complete response. This directly targets the abandonment problem described (users leaving after waiting for the first token).
Option A further improves p95 latency and throughput by removing model loading overhead during inference and improving GPU utilization. Preloading model weights during container startup ensures the model is ready before traffic arrives and avoids unpredictable on-demand weight loading. Dynamic batching increases efficiency by grouping compatible requests into a single inference pass, reducing per-request overhead and improving GPU saturation. When tuned properly for interactive workloads, batching can reduce tail latency while preserving responsiveness by enforcing small batch windows.
Option B makes latency worse because setting minimum instances to 0 and lazily loading weights guarantees cold-start delays and unpredictable first-token performance. Option C similarly increases cold-start behavior through lazy loading and offers no batching benefits. Option E is designed for non-interactive workloads and introduces queueing and storage latency, which conflicts with the 800 ms p95 requirement for interactive chat.
Therefore, A and D are the best combination to achieve consistently low p95 latency and fast first-token streaming for a SageMaker-hosted chat assistant.
질문 # 156
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 # 157
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