AIP-C01問題無料、AIP-C01テスト内容

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AIP-C01問題無料, AIP-C01テスト内容, AIP-C01日本語練習問題, AIP-C01日本語学習内容, AIP-C01模擬対策問題

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購入前にJPNTestが提供した無料のAIP-C01問題集をダウンロードできます。自分の練習を通して、試験のまえにうろたえないでしょう。JPNTestを選択して専門性の訓練が君のAIP-C01試験によいだと思います。

Amazon AIP-C01 Exam Overview:

Certification Vendor: Amazon Web Services (AWS)
Exam Name: AWS Certified Generative AI Developer - Professional
Exam Number: AIP-C01
Certificate Validity Period: 3 years
Available Languages: English
Exam Duration: 180 minutes
Exam Format: Multiple Choice, Multiple Response, Matching, Ordering
Passing Score: 750 (on a scale of 100-1000)
Real Exam Qty: 85
Related Certifications: AWS Certified AI Practitioner
Exam Price: $300 USD
Sample Questions: Amazon AIP-C01 Sample Questions
Exam Way: Online proctored exam or test center (Pearson VUE)
Pre Condition: Recommended 3+ years of experience in software development and 1+ year of experience building applications with generative AI on AWS. AWS Cloud Practitioner certification or equivalent cloud knowledge recommended.
Official Syllabus URL: https://aws.amazon.com/certification/certified-generative-ai-developer-professional/

>> AIP-C01問題無料 <<

タイトル：AWS Certified Generative AI Developer - Professional試験テストエンジン、AIP-C01予備資料、AWS Certified Generative AI Developer - Professional模擬試験

21世紀の情報化時代の急流の到来につれて、人々はこの時代に適応できるようにいつも自分の知識を増加していてますが、まだずっと足りないです。IT業種について言えば、AmazonのAIP-C01認定試験はIT業種で欠くことができない認証ですから、この試験に合格するのはとても必要です。この試験が難しいですから、試験に合格すれば国際的に認証され、受け入れられることができます。そうすると、美しい未来と高給をもらう仕事を持てるようになります。JPNTestというサイトは世界で最も信頼できるIT認証トレーニング資料を持っていますから、JPNTestを利用したらあなたがずっと期待している夢を実現することができるようになります。１００パーセントの合格率を保証しますから、AmazonのAIP-C01認定試験を受ける受験生のあなたはまだ何を待っているのですか。速くJPNTestというサイトをクリックしてください。

Amazon AIP-C01 認定試験の出題範囲：

トピック 出題範囲
トピック 1
  • GenAIアプリケーションの運用効率と最適化：この分野は、コスト最適化戦略、レイテンシとスループットのパフォーマンスチューニング、およびGenAIアプリケーション向けの包括的な監視システムの導入を網羅しています。
トピック 2
  • 基盤モデルの統合、データ管理、およびコンプライアンス：この領域では、GenAIアーキテクチャの設計、基盤モデルの選択と構成、データパイプラインとベクトルストアの構築、検索メカニズムの実装、および迅速なエンジニアリングガバナンスの確立を扱います。
トピック 3
  • テスト、検証、およびトラブルシューティング：この領域では、基盤モデルの出力の評価、品質保証プロセスの実装、およびプロンプト、統合、検索システムなどのGenAI固有の問題のトラブルシューティングを扱います。
トピック 4
  • 実装と統合：この領域では、エージェント型AIシステムの構築、基盤モデルの展開、GenAIとエンタープライズシステムの統合、FM APIの実装、およびAWSツールを使用したアプリケーション開発に焦点を当てています。
トピック 5
  • AIの安全性、セキュリティ、ガバナンス：この領域では、入出力の安全管理、データセキュリティとプライバシー保護、コンプライアンスメカニズム、透明性と公平性を含む責任あるAI原則を扱います。

Amazon AWS Certified Generative AI Developer - Professional 認定 AIP-C01 試験問題 (Q109-Q114):

質問 # 109
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?

  • A. Keep the restaurant data in PostgreSQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.
  • B. Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, feature, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
  • C. Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM.
    Perform k-nearest neighbors (k-NN) searches to find semantically similar results.
  • D. Migrate the restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline. Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

正解：D

解説：
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.


質問 # 110
A pharmaceutical company is developing a Retrieval Augmented Generation application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?

  • A. Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain 1,000 tokens and child chunks that contain 200 tokens. Set a 50-token overlap between chunks.
  • B. Configure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.
  • C. Configure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size and a
    10% overlap between chunks. Use an appropriate Amazon Bedrock embedding model.
  • D. Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.

正解：A

解説：
Option B is the best fit because hierarchical chunking is designed to preserve local detail while keeping broader document context available during retrieval, which directly addresses the problem of questions spanning methodology, results, and discussion. In large scientific papers, a single answer often depends on linked paragraphs across adjacent sections. If the knowledge base retrieves only small, isolated chunks, the RAG system can cite text that is semantically close to a query term but not contextually correct, producing inconsistent answers and irrelevant citations.
With hierarchical chunking, the knowledge base creates child chunks that are small enough for high- precision vector similarity matching, such as 200 tokens, which improves the likelihood that the retrieved text is tightly related to the user's query. At the same time, each child chunk is associated with a larger parent chunk, such as 1,000 tokens, which retains the surrounding narrative and section-level context. This structure helps the retrieval pipeline return passages that include the relevant subsection plus the explanatory framing that prevents misinterpretation, which is especially important in scientific writing where methods, results, and discussion are interdependent.
The configured overlap further reduces boundary effects where key statements split across chunks. This improves continuity for paragraphs that bridge sections, such as a results paragraph that references the methodological setup or a discussion paragraph interpreting a specific metric.
Option A can improve consistency slightly, but fixed-size chunking still risks separating related paragraphs and does not provide a built-in mechanism to retrieve broader context linked to precise matches. Option C can create more meaningful boundaries, but it does not guarantee the parent-level context that hierarchical chunking provides at retrieval time. Option D increases operational burden and is not practical at the scale of
25 million


質問 # 111
A company needs a system to automatically generate study materials from multiple content sources. The content sources include document files (PDF files, PowerPoint presentations, and Word documents) and multimedia files (recorded videos). The system must process more than 10,000 content sources daily with peak loads of 500 concurrent uploads. The system must also extract key concepts from document files and multimedia files and create contextually accurate summaries. The generated study materials must support real- time collaboration with version control.
Which solution will meet these requirements?

  • A. Use Amazon Bedrock Data Automation (BDA) with Amazon SageMaker AI endpoints to host content extraction and summarization models. Use Amazon Bedrock Guardrails to extract content from all file types. Store document files in Amazon Neptune for time series analysis. Collaborate by using Amazon Bedrock Chat for real-time messaging.
  • B. Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to process batches of content files. Fine-tune foundation models (FMs) in Amazon Bedrock to classify documents across all content types. Store the processed data in Amazon ElastiCache (Redis OSS) by using Cluster Mode with sharding. Use Prompt management in Amazon Bedrock for version control.
  • C. Use Amazon Bedrock Data Automation (BDA) with foundation models (FMs) to process document files. Integrate BDA with Amazon Textract for PDF extraction and with Amazon Transcribe for multimedia files. Store the processed content in Amazon S3 with versioning enabled. Store the metadata in Amazon DynamoDB. Collaborate in real time by using AWS AppSync GraphQL subscriptions and DynamoDB.
  • D. Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to orchestrate document file processing. Use Amazon Bedrock Knowledge Bases to process all multimedia. Store the content in Amazon DocumentDB with replication. Collaborate by using Amazon SNS topic subscriptions. Track changes by using Amazon Bedrock Agents.

正解：C

解説：
Option B best fulfills all functional, scalability, and collaboration requirements by combining purpose-built AWS services with Amazon Bedrock capabilities. Amazon Bedrock Data Automation is designed to orchestrate large-scale, multimodal data processing pipelines and integrates naturally with foundation models for summarization and concept extraction. Using BDA to process document files ensures consistent preprocessing and model invocation at scale, which is essential for handling more than 10,000 sources per day with high concurrency.
Integrating Amazon Textract for PDFs enables accurate extraction of structured and unstructured text from scanned and digital documents, while Amazon Transcribe is the appropriate service for converting recorded videos into text for downstream semantic analysis. These services are optimized for their respective media types and feed clean, normalized inputs into Bedrock foundation models, improving the quality of contextual summaries.
Storing processed content in Amazon S3 with versioning enabled directly addresses the requirement for version control. S3 versioning provides immutable object history and rollback capabilities without additional complexity. Metadata storage in Amazon DynamoDB supports high-throughput, low-latency access patterns and scales automatically to handle peak upload concurrency.
Real-time collaboration is achieved through AWS AppSync GraphQL subscriptions combined with DynamoDB. AppSync enables real-time updates to connected clients whenever study materials are created or modified, making it well suited for collaborative editing and live synchronization. DynamoDB streams integrate seamlessly with AppSync to propagate changes efficiently.
The other options misuse services or fail to meet key requirements. Amazon SNS does not support collaborative state synchronization, Amazon DocumentDB is not optimized for versioned document storage, Amazon Neptune is unsuitable for document-centric workloads, and Amazon ElastiCache is not designed for durable storage or version control. Option B aligns with AWS best practices for scalable, multimodal generative AI systems built on Amazon Bedrock.


質問 # 112
Example Corp provides a personalized video generation service that millions of enterprise customers use.
Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?

  • A. Ensure that each customer configures an Amazon Bedrock knowledge base. Allow cross-account querying so Example Corp can retrieve structured data for prompt augmentation.
  • B. Ensure that each customer sets up an Amazon Q Business index that includes the customer's internal data. Ensure that each customer designates Example Corp as a data accessor to allow Example Corp to retrieve relevant content by using a secure API to enrich prompts at runtime.
  • C. Use federated search with Model Context Protocol (MCP) by deploying real-time MCP servers for each customer. Retrieve data in real time during prompt generation.
  • D. Configure Amazon Kendra to crawl customer data sources. Share the resulting indexes across accounts so Example Corp can query each customer's Amazon Kendra index to retrieve augmentation data.

正解：B

解説：
Option A is the correct solution because Amazon Q Business is explicitly designed to provide secure, governed access to enterprise data while preserving customer ownership and control. Each customer maintains their own Amazon Q Business index, which ensures that data never leaves the customer's control boundary unless explicitly shared through approved access mechanisms.
By designating Example Corp as a data accessor, customers can allow controlled, auditable access to their indexed content through secure APIs. This model satisfies strict data governance requirements, including data ownership, access transparency, and revocation capability. Customers do not need to expose raw data or deploy infrastructure in Example Corp's environment.
Amazon Q Business provides high semantic accuracy through managed indexing, ranking, and retrieval optimizations. Because real-time access is not required, this approach avoids the complexity and latency challenges of live federated retrieval while still delivering fast query performance suitable for customer experience use cases.
Option B introduces unnecessary operational complexity by requiring real-time MCP servers per customer.
Option C requires customers to manage Amazon Bedrock knowledge bases and enable cross-account access, which increases integration complexity and governance risk. Option D requires shared Amazon Kendra indexes across accounts, which complicates access control and data ownership boundaries.
Therefore, Option A provides the cleanest, lowest-overhead architecture that meets data governance, accuracy, performance, and scalability requirements while minimizing operational burden for both Example Corp and its customers.


質問 # 113
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. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.
  • D. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.

正解：C

解説：
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.


質問 # 114
......

AIP-C01テスト内容: https://jpntest.com/shiken/AIP-C01-mondaishu

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