퍼펙트한AIP-C01인증공부문제덤프자료

2026 Itexamdump 최신 AIP-C01 PDF 버전 시험 문제집과 AIP-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1nTOIpCLKluiPRoga8NXBIOGlpQRUSOQw

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Amazon AIP-C01 시험요강:

주제	소개
주제 1	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.
주제 2	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.
주제 3	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.
주제 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.
주제 5	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.

>> AIP-C01인증공부문제 <<

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최신 Amazon Professional AIP-C01 무료샘플문제 (Q16-Q21):

질문 # 16
A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?

A. Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan Embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.
B. Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
C. Use Amazon OpenSearch Service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.
D. Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.

정답：C

설명：
Option B is the correct solution because legal research workloads require both semantic understanding and exact lexical precision, especially for statutes, citations, and domain-specific terminology. A hybrid search architecture directly addresses this need by combining vector similarity search with traditional keyword-based retrieval.
Vector search alone is often insufficient for legal research because exact phrases, citation formats, and jurisdiction-specific terms must be matched precisely. Keyword search ensures high recall and precision for citations and legal terms, while vector search captures deeper semantic relationships between legal concepts, precedents, and arguments. Amazon OpenSearch Service natively supports hybrid search, enabling efficient scoring and ranking without external orchestration.
Applying an Amazon Bedrock reranker model further improves relevance by reordering retrieved documents based on deeper contextual understanding. Reranking is especially valuable in legal research because multiple documents may appear relevant, but only a subset truly addresses the user's legal question. The reranker optimizes final results before they are passed to the Anthropic Claude FM, improving answer accuracy and reducing hallucinations.
Option A relies on default vector search, which does not reliably handle citations and exact terminology.
Option C focuses on query suggestions and post-processing rather than retrieval quality. Option D introduces unnecessary operational complexity by merging results across multiple systems.
Therefore, Option B best meets the requirements for precision, performance, and semantic understanding in a legal research AI assistant.

질문 # 17
A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.
A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company's approved resources. The company does not want to make project-level modifications.
Which solution will meet these requirements?

A. Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference for code suggestions.
B. Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples.
Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the workspace context to retrieve suggestions from the Git repository.
C. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.
D. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.

정답：D

설명：
Option D is the correct solution because Amazon Q Developer customizations are designed to incorporate organization-approved knowledge and coding guidance without requiring per-project changes. A customization can point Amazon Q Developer to curated internal sources such as approved libraries, coding standards, architectural patterns, and proprietary techniques. This allows the assistant's suggestions to align with company policies and preferred implementations consistently across teams and repositories.
The key requirement is that the company does not want to make project-level modifications. Options A, B, and C all require adding files or repositories into the project workspace, which directly violates this constraint.
They also rely on developer behavior to "use workspace context," which is harder to enforce and can lead to inconsistent adherence to standards.
With a customization, the organization centrally manages and updates approved resources. This reduces operational overhead because updates to libraries, patterns, or guidelines propagate automatically to developers using the customization, without requiring changes to each project. This is especially valuable for a new team, where consistent enforcement of approved practices is important to reduce compliance risk, security issues, and inconsistent code style.
Additionally, customizations support governance by allowing the company to standardize how Amazon Q Developer responds, ensuring that suggestions reflect approved internal content rather than generic public patterns.
Therefore, Option D best satisfies the requirement for centralized enforcement of approved resources with minimal ongoing management and no project-level modifications.

질문 # 18
A company uses an application to process customer support tickets. The company wants to integrate AI- powered sentiment analysis and auto-response generation into the application by using Amazon Bedrock. The company wants to prioritize urgent issues and reduce initial response times by 40% compared to manual responses. The solution must process 100 concurrent webhook requests with response times under 500 ms.
The solution must maintain 99.9% availability across multiple AWS Regions and authenticate all incoming requests. The company must avoid any authentication failures. The company does not want to modify the existing application infrastructure, which includes several ticketing systems that use multiple webhook authentication methods. The solution must support scaling to handle occasional spikes up to 250,000 daily tickets during peak periods. Which solution will meet these requirements?

A. Set up an Amazon SQS queue in each Region to receive webhook messages. Use the SQS queue to invoke AWS Lambda functions that call Amazon Comprehend to perform sentiment analysis and Amazon Lex to generate responses. Use Amazon EventBridge to retry message delivery to the application API.
B. Use an Amazon API Gateway REST API with a Regional endpoint to receive webhook requests and invoke AWS Lambda functions. Configure Lambda authorizers to validate all the webhook authentication methods. Configure the Lambda functions to call Amazon Bedrock to perform sentiment analysis and generate responses. Store results in Amazon DynamoDB global tables to provide multi- Region availability.
C. Deploy an AWS AppSync GraphQL API to multiple Regions. Configure API tokens to authenticate incoming requests. Create GraphQL mutation resolvers that publish events to Amazon EventBridge.Configure EventBridge rules to invoke AWS Lambda functions that use Amazon Bedrock to perform sentiment analysis and generate responses. Use Amazon CloudFront to reduce latency.
D. Create AWS Lambda function URLs for each ticketing system. Configure the function URLs with the NONE authentication type. Configure separate Lambda functions to verify webhook signatures by using Hash-based Message Authentication Code (HMAC) validation in the function code. Deploy the functions to multiple Regions and use AWS Global Accelerator to route traffic. Use Amazon Bedrock to perform sentiment analysis and generate responses. Return responses through webhook callbacks.

정답：B

설명：
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.

질문 # 19
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?

A. Create a second knowledge base. Use Lambda and Amazon Comprehend to redact PII before syncing to the second knowledge base. Route users to the appropriate knowledge base based on Cognito group membership.
B. Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded. Use a second Lambda function with Amazon Comprehend to redact PII for engineers. Use S3 Lifecycle rules to remove reports older than 3 years.
C. Enable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.
D. Set up an S3 Lifecycle configuration to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. When users interact with the AI assistant, apply a guardrail configuration selected based on the user's Cognito user group to redact PII from responses when required.

정답：D

설명：
Option C is the correct solution because it enforces privacy controls at inference time, not at ingestion time, which is required when different user roles require different visibility into the same underlying data.
Using an S3 Lifecycle configuration ensures that documents older than 3 years are automatically removed, guaranteeing that the knowledge base references only compliant, recent medical reports. Scheduling Lambda- based syncs keeps the knowledge base aligned with the bucket contents without introducing complex per- upload orchestration.
The most important requirement is role-based PII exposure. Amazon Bedrock guardrails support dynamic application at inference time, allowing the system to select a guardrail configuration based on the authenticated user's Amazon Cognito group. Surgeons can receive full responses, while engineers receive responses with PII masked-without duplicating data or maintaining multiple knowledge bases.
This approach preserves a single source of truth for medical reports while enforcing privacy through response- level controls. It also maintains full auditability of access and redaction behavior.
Option A permanently removes PII and violates surgeon access requirements. Option B redacts data inconsistently and couples privacy logic to ingestion. Option D doubles storage, increases cost, and introduces data drift risk.
Therefore, Option C best meets privacy, compliance, scalability, and operational efficiency requirements.

질문 # 20
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?

A. Use AWS Glue Data Catalog with crawlers to maintain data sources. Store generated summaries in Amazon S3. Write object tags that include a source ID. Store Amazon Bedrock model invocation logs in Amazon S3. Enable S3 Object Lock on the S3 bucket that stores invocation logs. Use AWS CloudTrail log file integrity validation to provide tamper-evident immutability.
B. Use AWS AppConfig feature flags to implement data versioning. Restrict access to the model by using IAM condition keys. Maintain a versioned mapping file of source-to-output relationships in Amazon S3.
C. Use Amazon Comprehend to identify data sources in the documents. Store generated summaries in Amazon S3 and enable S3 Object Lock. Use Amazon CloudWatch metrics to generate reports about application throughput. Do not include logs for each invocation.
D. Store application outputs in Amazon DynamoDB. Apply item-level tags that include source attribution.
Write application events to Amazon CloudWatch Logs. Use IAM roles to provide audit traceability.

정답：A

설명：
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.

질문 # 21
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