매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
AWS Certified Generative AI Developer - Professional 온라인 연습
최종 업데이트 시간: 2026년06월29일
당신은 온라인 연습 문제를 통해 Amazon AIP-C01 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 AIP-C01 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 85개의 시험 문제와 답을 포함하십시오.
정답:
Explanation:
Amazon Bedrock Prompt Management is the purpose-built service for prompt lifecycle management. it provides native version control, allowing developers to test and compare variants side-by-side. Use of parameterized variables ensures that a single prompt structure can be consistently reused across different sub-applications (assistants vs. graders) while still being tailored to the specific context. To "integrate into the main application" with minimal overhead, Amazon Bedrock Flows provide a managed orchestration layer. Flows allow developers to link managed prompts with AWS services (like knowledge bases or Lambda functions) without writing complex state-machine logic in Step Functions (Option B). This combination ensures consistent, auditable, and easily deployable prompt assets across the university's diverse use cases.
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Explanation:
: AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for "tamper-evident, immutable audit logs" is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.
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Explanation:
: For a 3-week proof-of-concept in a regulated field like healthcare, Retrieval Augmented Generation (RAG) is more efficient and safer than fine-tuning. RAG allows the use of anonymized patient records without risking the leak of sensitive data into the model's permanent memory. To evaluate accuracy quantitatively and rapidly, the "LLM-as-a-judge" pattern is recommended. Using a strong judge model to score the outputs of multiple candidate FMs provides objective metrics (e.g., factual alignment, completeness) that manual qualitative feedback (Option C) cannot scale to provide within the timeline. Fine-tuning (Option B) typically takes longer than 3 weeks to properly data-prep and validate for clinical accuracy.
정답:
Explanation:
: The errors described indicate missing permissions at both the application orchestration and data access levels. The 400 Bad Authorization from BedrockAgentRuntime indicates the Lambda execution role lacks the identity permission to invoke the agent; adding bedrock: InvokeAgent and aoss: APIAccessAll (which allows the principal to interact with OpenSearch Serverless APIs) is necessary. The 403 Forbidden error from OpenSearch Serverless specifically relates to data-plane permissions. Unlike traditional OpenSearch, Serverless uses data access policies. To "manage collections at scale" automatically, a policy must be created that uses pattern-based resource rules (e.g., matching a prefix), ensuring that as new collections or indexes are created, the required principals (the Lambda role and the Bedrock service role) are granted the necessary access without manual policy updates for every new resource.
정답:
Explanation:
: This complex set of requirements is best addressed by Amazon Bedrock Prompt Management. It allows the creation of parameterized prompts where variables (like demographics) can be injected at runtime, ensuring consistent medical terminology while adapting recommendations to the specific patient. Prompt Management natively supports versioning and approval workflows, which is a requirement for clinical safety and compliance. For audit and retention, Bedrock model invocation logging can be configured to send detailed prompt and response data to Amazon S3. Storing these logs in S3 supports the 2-year retention requirement and facilitates local audits. S3 is more cost-effective for long-term storage than CloudWatch Logs alone. CloudTrail (Option A) only logs management events, not the actual prompt/response content required for medical auditing.
정답:
Explanation:
Amazon CloudWatch provides the most integrated path for unifying technical and business metrics. By importing business metrics into CloudWatch (via custom metrics or metric streams), teams can build custom dashboards that provide a single pane of glass for both system health and conversion performance. Composite alarms allow stakeholders to be notified only when multiple conditions are met (e.g., high latency and dropping conversion rates), reducing alert fatigue. Applying anomaly detection to these metrics is essential for GenAI workloads because performance baselines can shift subtly; CloudWatch can automatically detect these deviations and trigger alerts through Amazon SNS. This solution provides comprehensive correlation and automated alerting with less operational complexity than managing external visualization servers (Option B) or multi-service analytics pipelines (Option C).
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Explanation:
: When a foundation model fails to include specific required content or fails to integrate it coherently, prompt engineering techniques like output indicators or "wrappers" are highly effective. By explicitly defining where the promotional message should appear (e.g., "The response must end with the following message: [PROMO TEXT]") or providing an example output structure, the developer reinforces the constraint within the model's generation path. This is more direct and less computationally expensive than generating multiple variants and reranking them (Option B) or adding complex post-processing layers (Option C). Guardrails (Option A) are intended for filtering harmful content rather than enforcing specific promotional copy insertion.
정답:
Explanation:
: Amazon Q Developer Pro provides a built-in administrator dashboard designed specifically for organizational observability. This dashboard provides native visibility into user-level metrics across the entire AWS Organization, allowing administrators to identify active vs. underused subscriptions and recognize power users. Crucially, it tracks high-level usage patterns, including code acceptance metrics (such as lines of code generated and accepted), which is a key requirement for measuring ROI and identifying training needs. Using the built-in dashboard provides the necessary insights with the least operational overhead, as it does not require building custom data pipelines (Option C) or complex log processing architectures (Option D).
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Explanation:
: To handle high concurrency (100+ requests) with sub-500 ms response times and diverse authentication methods without infrastructure changes, Amazon API Gateway with Lambda authorizers is the optimal choice. The Lambda authorizers can evaluate multiple authentication tokens or signatures centrally before the request reaches the processing logic, preventing unauthorized traffic and potential authentication failures at scale. AWS Lambda integrated with Amazon Bedrock provides the scalability to handle ticket surges (up to 250,000 daily) without over-provisioning resources. For high availability (99.9%) and multi-region resilience, storing the resulting sentiment and responses in Amazon DynamoDB global tables ensures that data is accessible across regions with minimal latency.
Option B is less secure due to the "NONE" auth type, and Option C introduces queuing latency that may exceed the 500 ms target.
정답:
Explanation:
AWS Step Functions provides native service integrations that minimize code and operational overhead. For this workflow, Step Functions can directly invoke Amazon Transcribe and, upon completion, directly invoke Amazon Bedrock foundation models. Modern foundation models can be prompted to return outputs in JSON format, fulfilling the requirement for structured analysis without needing an intermediate Lambda function to format the request or response. To trigger the process "as soon as new files are created," configuring Amazon S3 to send events to Amazon EventBridge is the recommended event-driven pattern. This allows for fine-grained routing and decouples the storage layer from the processing logic, ensuring the workflow scales reliably as call volume grows.
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Explanation:
: 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.
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Explanation:
: To achieve the ultra-low latency requirement of less than 500 ms, the system must utilize streaming capabilities at every stage. Using Amazon Transcribe streaming with small (100-ms) audio chunks ensures that transcription begins immediately as the customer speaks. On the model side, Amazon Bedrock’s InvokeModelWithResponseStream allows the application to receive tokens as they are generated, rather than waiting for the entire completion, which is critical for real-time interactions. Amazon DynamoDB is the ideal choice for storing supervisor ratings during a live call because it provides the single-digit millisecond latency required for high-frequency writes without impacting the application's performance.
Options involving batch processing or SQS queuing are unsuitable for sub-500 ms interactive requirements.
정답:
Explanation:
Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model’s parametric memory.
The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.
The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company’s product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.
Alright, after comparing List B (txt file) against List A (Word file), I have identified the unique questions. These questions cover scenarios or architectural configurations that were not present in the existing list.
Here are the unique questions from List B, formatted as requested:
정답:
Explanation:
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.
정답:
Explanation:
Option B is correct because the application is currently invoking the base foundation model identifier, which routes traffic to the on-demand capacity pool rather than the company’s purchased provisioned throughput. In Amazon Bedrock, provisioned throughput is attached to a specific provisioned resource created through the provisioned throughput APIs. To consume that reserved capacity, inference requests must target the provisioned resource identifier that represents the purchased throughput, not the generic model identifier used for on-demand inference.
The code snippet uses modelId="anthropic.claude-v2". This value selects the on-demand endpoint for that model. As a result, requests are subject to on-demand quotas and throttling behavior, while the provisioned throughput remains idle. This directly explains the CloudWatch observation: provisioned capacity metrics show unused capacity because no traffic is being directed to the provisioned resource, and the on-demand path is throttling because it is exceeding the applicable on-demand limits during peak volume.
Replacing the modelId value with the provisioned throughput ARN returned by the
CreateProvisionedModelThroughput workflow ensures the runtime invocation is routed to the reserved capacity. Once traffic is directed correctly, the purchased model units provide the consistent throughput required for predictable performance during business hours, which is exactly why provisioned throughput is used.
Option A could increase capacity, but it does not fix the core issue that the application is not using the provisioned resource at all.
Option C can reduce the impact of throttling temporarily, but it adds latency and does not guarantee consistent throughput; it also still wastes the provisioned capacity.
Option D changes the response delivery mechanism, but throttling is a capacity routing and quota issue, not a streaming API issue.