AIP-C01認定デベロッパー、AIP-C01受験対策解説集

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Amazon AIP-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Plan and Design a Generative AI Application25%- Design the generative AI solution architecture
  • 1. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)
  • 2. Design for scalability, reliability, and cost-effectiveness
  • 3. Integrate with other AWS services (e.g., storage, databases, security)
  • 4. Design the end-to-end solution architecture
- Select the appropriate foundation models (FMs) and techniques
  • 1. Select model parameters and configurations
  • 2. Design prompt engineering strategies
  • 3. Consider fine-tuning vs. retrieval-augmented generation (RAG)
  • 4. Evaluate foundation models for the use case
- Identify and define the business and technical requirements for a generative AI application
  • 1. Identify the target audience and use cases
  • 2. Determine data requirements and availability
  • 3. Define functional and non-functional requirements
  • 4. Identify constraints and risks
Topic 2: Build and Implement a Generative AI Application45%- Implement data preprocessing and vectorization pipelines
  • 1. Implement document chunking and text processing
  • 2. Implement embeddings generation using AWS services
  • 3. Ingest and transform data for model consumption
  • 4. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
- Implement security, compliance, and responsible AI
  • 1. Implement IAM roles and policies for AI services
  • 2. Implement data encryption and privacy controls
  • 3. Ensure compliance with AI ethics and responsible use
  • 4. Implement content filtering and safety mechanisms
- Develop the application using AWS AI services
  • 1. Implement inference calls to Amazon Bedrock or other FMs
  • 2. Implement retrieval-augmented generation (RAG) patterns
  • 3. Implement prompt engineering and template management
  • 4. Integrate with knowledge bases
Topic 3: Optimize and Operationalize a Generative AI Application30%- Implement CI/CD and automation for AI applications
  • 1. Automate deployment of AI models and applications
  • 2. Manage model updates and rollback strategies
  • 3. Implement version control for prompts and models
  • 4. Implement testing strategies for generative AI applications
- Optimize costs and performance
  • 1. Optimize token usage and manage costs
  • 2. Implement caching strategies for frequent queries
  • 3. Implement auto-scaling for AI workloads
  • 4. Optimize model selection and parameter tuning
- Implement monitoring, logging, and evaluation
  • 1. Implement human-in-the-loop evaluation workflows
  • 2. Monitor model performance and application metrics
  • 3. Evaluate model outputs for quality and bias
  • 4. Implement logging for prompts and responses

>> AIP-C01認定デベロッパー <<

AWS Certified Generative AI Developer - Professional の試験認定 - AIP-C01 資格の王道

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Amazon AWS Certified Generative AI Developer - Professional 認定 AIP-C01 試験問題 (Q52-Q57):

質問 # 52
A medical company is building a generative AI (GenAI) application that uses Retrieval Augmented Generation (RAG) to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end-user latency, even as the document collection grows to millions of documents.
Which solution will meet these requirements with the LEAST operational overhead?

正解：C

解説：
Option A is the correct solution because hybrid search directly addresses the core retrieval failure modes while maintaining low latency and minimal operational overhead. In medical and scientific domains, exact terminology, abbreviations, and acronyms (for example, drug names, procedures, or conditions) are critical.
Pure vector similarity search often underweights these exact matches, leading to missed results and excessive semantically related but irrelevant documents.
Amazon OpenSearch Service natively supports hybrid search, which combines keyword-based retrieval (such as BM25) with vector similarity search. Keyword search ensures precise matching for exact terms and acronyms, while vector search captures semantic meaning and contextual similarity. By blending these approaches, the retrieval system improves both precision and recall without introducing additional infrastructure.
Hybrid search operates within the same OpenSearch index and query path, which preserves low end-user latency even at large scale. This is especially important as the document collection grows to millions of documents. Because OpenSearch handles scoring and ranking internally, no additional orchestration layers or post-processing steps are required.
Option B increases computational cost and latency while failing to address exact-term recall. Option C introduces a new service and ingestion pipeline, increasing operational overhead and latency. Option D adds model hosting, re-ranking infrastructure, and complexity that is unnecessary when OpenSearch provides native hybrid retrieval.
Therefore, Option A delivers the best balance of retrieval quality, scalability, latency, and operational simplicity for medical RAG workloads.


質問 # 53
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain- specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?

正解：A


質問 # 54
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company's data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application's decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?

正解：D

解説：
This scenario requires strict data residency, regional processing, classification, and auditable decision trails, which Option C addresses using AWS-native governance services.
Region-specific Amazon S3 buckets enforce geographic data boundaries. Amazon S3 Object Lock ensures immutability of stored data and logs, supporting regulatory retention and non-repudiation requirements. Pre- processing data within the same Region before invoking Amazon Bedrock ensures that inference and data handling do not cross continental boundaries.
Amazon Macie provides managed, automated data classification for sensitive data types such as PII and financial records, fulfilling the classification requirement without custom tooling.
AWS CloudTrail immutable logs provide comprehensive audit trails of all API calls, model invocations, and data access events, ensuring traceability of AI decision-making processes.
Option A violates residency rules through cross-Region inference. Option B does not provide data classification. Option D introduces high operational overhead and relies on manual compliance reporting.
Therefore, Option C is the most compliant, scalable, and operationally efficient solution for regionally governed GenAI workloads.


質問 # 55
A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real- time personally identifiable information (PII) filtering and audit trails to automatically report compliance.
Which solution will meet these requirements?

正解：A

解説：
Option A is the most comprehensive and architecturally aligned solution for meeting end-to-end data lineage, real-time PII filtering, and automated compliance reporting requirements in a medical GenAI system built on Amazon Bedrock. Each requirement maps directly to a managed AWS service that is purpose-built for governance, security, and compliance.
AWS Glue Data Catalog is designed to register datasets across multiple sources and maintain metadata that supports lineage tracking. By cataloging all inputs that flow into the Bedrock-based system, the organization can trace how data moves from ingestion through processing and storage, which is essential for regulatory audits in healthcare environments.
For real-time PII filtering, Amazon Bedrock Guardrails provide native PII detection and filtering during model inference. Guardrails operate inline with model invocation, ensuring sensitive information is blocked or redacted before responses are returned to users. This satisfies the requirement for real-time protection rather than post-processing analysis.
AWS CloudTrail delivers a complete audit trail of all Amazon Bedrock API calls, including InvokeModel requests and configuration changes. Storing these logs in Amazon S3 enables long-term retention and supports compliance audits. CloudTrail ensures traceability of who accessed the system, when, and how it was used.
To strengthen compliance monitoring, Amazon Macie continuously scans stored data for sensitive information and automatically classifies findings. Publishing Macie findings to Amazon CloudWatch Logs and visualizing them through dashboards enables near-real-time visibility into compliance posture and supports automated reporting workflows.
The other options fall short. Option B performs PII filtering at the application edge rather than at inference time and relies on scheduled analysis instead of real-time enforcement. Option C focuses on replication and document processing rather than inline GenAI governance. Option D uses services that are not designed for PII detection in text-based GenAI workflows and lacks native lineage tracking.
Therefore, A best fulfills all stated requirements using AWS-recommended governance and security capabilities.


質問 # 56
A company is building a serverless application that uses AWS Lambda functions to help students around the world summarize notes. The application uses Anthropic Claude through Amazon Bedrock. The company observes that most of the traffic occurs during evenings in each time zone. Users report experiencing throttling errors during peak usage times in their time zones.
The company needs to resolve the throttling issues by ensuring continuous operation of the application. The solution must maintain application performance quality and must not require a fixed hourly cost during low traffic periods.
Which solution will meet these requirements?

正解：A

解説：
Option C is the correct solution because it resolves throttling while preserving performance and avoiding fixed costs during low-traffic periods. Amazon Bedrock supports on-demand inference with usage-based pricing, making it well suited for applications with time-zone-dependent traffic spikes.
Throttling during peak hours typically occurs when inference requests exceed available regional capacity.
Cross-Region inference allows Amazon Bedrock to automatically distribute requests across multiple AWS Regions, reducing contention and preventing throttling without requiring reserved or provisioned capacity.
This approach ensures continuous operation while maintaining low latency for users in different geographic locations.
Invocation logging and native metrics such as InvocationThrottles, InputTokenCount, and OutputTokenCount provide visibility into usage patterns and capacity constraints. Monitoring these metrics enables teams to validate that traffic distribution is working as intended and that performance remains consistent during peak periods.
Option A introduces fixed hourly costs by relying on provisioned throughput, which directly violates the requirement to avoid unnecessary spend during low-traffic periods. Option B introduces regional failover complexity and reactive behavior instead of proactive load distribution. Option D does not address the root cause of throttling, as distributing traffic across model versions within the same Region does not increase available capacity.
Therefore, Option C best aligns with AWS Generative AI best practices for scalable, cost-efficient, global serverless applications.


質問 # 57
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