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Amazon AIP-C01 Exam Syllabus Topics:

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

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Durch Amazon AIP-C01 Zertifizierungsprüfung wird sich viel Wandel bei Ihnen vollziehen. Beispielsweise werden Ihr Beruf und Leben sicher viel verbessert, weil die Amazon AIP-C01 Zertifizierungsprüfung sowieso eine ziemlich wichtige Prüfung ist. Aber so einfach ist es nicht, diese Prüfung zu bestehen.

Amazon AWS Certified Generative AI Developer - Professional AIP-C01 Prüfungsfragen mit Lösungen (Q77-Q82):

77. Frage
A cooking spice company is launching a marketing campaign that will invite customers to upload videos of themselves cooking with the company ' s spices to a platform that the company operates. The company is designing a solution for the video-sharing platform.
The solution must extract video transcripts and generate embeddings to help users search for videos based on spices, other ingredients, and cooking methods. The solution must integrate with the company ' s existing open-source chat system so users can interact with videos in real time. The solution must analyze user chat text for sentiment to determine the most positively received videos. Each month, the recipe videos that received the most positive feedback and the highest number of views will be displayed on the platform.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select THREE.)

Antwort: B,D,F

Begründung:
Options A, D, and E provide the required capabilities while minimizing custom infrastructure. Amazon Transcribe converts speech in uploaded media into text, Amazon Comprehend provides managed sentiment detection, and Bedrock Knowledge Bases can support embedding-based semantic retrieval over transcript content. DynamoDB is appropriate for lightweight, scalable counters such as views and positive-feedback totals and avoids operating a relational vector database solely for metrics. The Strands Agents SDK provides prebuilt HTTP-request tooling for invoking the existing chat API and supports tool-based and multi-agent orchestration patterns, reducing bespoke integration code. Option B adds Step Functions and Lambda when Strands can coordinate the required application interactions directly. Option C requires custom orchestration and error-management code. Option F introduces Aurora PostgreSQL and pgvector even though vector retrieval is already handled through the Bedrock knowledge-base layer. AWS Documentation


78. Frage
A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?

Antwort: C

Begründung:
Option A best meets the requirements because it directly implements a Retrieval Augmented Generation pattern for conversational recommendations using managed Amazon Bedrock capabilities and a scalable vector store. The company's source data already resides in Amazon S3, which aligns naturally with Amazon Bedrock Knowledge Bases ingestion workflows. A knowledge base can ingest book text, reviews, and metadata, generate embeddings using a supported embedding model, and persist those vectors in a purpose- built vector backend such as Amazon OpenSearch Service. This enables semantic retrieval that is well suited to conversation-driven intent, where user prompts are often descriptive and do not map cleanly to keyword filters.
The requirement to suggest books based on conversations implies the system must interpret natural language context and retrieve relevant passages, reviews, and metadata to ground the recommendation. Knowledge Bases provide managed orchestration for embedding creation and retrieval, which reduces development effort compared to building custom embedding pipelines. OpenSearch Service provides scalable vector search and k- nearest neighbors style similarity retrieval, which supports low-latency responses when properly indexed and sized.
For scaling to more than 10,000 concurrent users, the API layer design in option A is a common AWS pattern: Amazon API Gateway provides a managed front door with throttling and request handling, while AWS Lambda scales horizontally with demand and can invoke the knowledge base retrieval operations. This separates compute scaling from the vector store scaling and helps keep latency predictable under load.
Option B is not the best choice because DynamoDB is not the standard native vector store target for Amazon Bedrock Knowledge Bases in this context and would introduce additional implementation complexity around vector indexing and similarity search behavior. Option C requires substantial ML lifecycle work, model hosting, tuning, and continuous iteration to achieve quality recommendations at scale. Option D provides strong enterprise search, but it focuses on retrieval and FAQs rather than a managed RAG recommendation workflow grounded in embeddings and conversational context for generative responses.


79. Frage
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 input/output records.
Which solution will meet these requirements?

Antwort: D

Begründung:
Option B is correct because it is the only option that satisfies all requirements: centralized cataloging, automatic data-source discovery, citation metadata, model input/output logging, and tamper-evident immutable audit retention. AWS Glue Data Catalog is designed as a central metadata repository, and AWS Glue crawlers can automatically discover and catalog new or updated data sources. AWS documentation states that crawlers can infer metadata and keep the Data Catalog up to date across sources such as Amazon S3, Amazon RDS, Amazon Redshift, and other supported stores. This directly addresses the requirement to catalog all data sources centrally and update them automatically.
Storing generated summaries in Amazon S3 and writing object tags that include source IDs is appropriate because S3 object tags are queryable metadata that can be used for organization, lifecycle policies, access controls, cost allocation, and downstream indexing. For GenAI traceability, the source ID tag can represent the citation or provenance link from the generated summary back to the original technical document.
Amazon Bedrock model invocation logging is also required here because the company must retain input
/output records for every model invocation. AWS documentation states that model invocation logging can collect invocation logs, model input data, and model output data for Amazon Bedrock invocations, with Amazon S3 as a supported delivery destination.
For immutable retention, S3 Object Lock provides write-once-read-many protection and helps prevent objects from being deleted or overwritten for a fixed period or indefinitely. CloudTrail log file integrity validation adds tamper evidence by using hashing and digital signing to detect whether log files were changed, deleted, or forged after delivery.
Option A explicitly omits logs for each invocation, so it fails the audit requirement. Option C lacks automatic source discovery and immutable audit retention. Option D uses configuration tooling, not a cataloging and audit architecture. Therefore, option B is the complete AWS-aligned solution.


80. Frage
A wildlife conservation agency operates zoos globally. The agency uses various sensors, trackers, and audiovisual recorders to monitor animal behavior. The agency wants to launch a generative AI (GenAI) assistant that can ingest multimodal data to study animal behavior.
The GenAI assistant must support natural language queries, avoid speculative behavioral interpretations, and maintain audit logs for ethical research audits.
Which solution will meet these requirements?

Antwort: B

Begründung:
Option B best meets the multimodal, ethical, and auditability requirements using managed AWS services designed for research-grade GenAI systems. Multimodal data such as audio, video, sensor telemetry, and tracking data must be curated and summarized before being consumed by a foundation model. Amazon SageMaker Processing and Amazon Transcribe provide scalable, managed preprocessing for audiovisual and textual data.
By ingesting summarized, validated observations into Amazon Bedrock Knowledge Bases, the GenAI assistant can answer natural language queries using grounded, evidence-based context instead of raw sensor signals. This significantly reduces the risk of speculative or anthropomorphic interpretations.
Amazon Bedrock guardrails are critical for preventing speculative behavioral claims, enforcing scientific and ethical constraints at inference time. Guardrails provide a validated, auditable safety layer that custom Lambda-based filters cannot reliably replicate.
AWS AppConfig enables controlled prompt management and change governance, ensuring that research prompts remain consistent and reviewable. AWS CloudTrail captures all access, query, and configuration changes, supporting ethical research audits and regulatory reviews.
Option A lacks grounding and speculative safeguards. Option C focuses on text analytics and does not properly handle multimodal reasoning or safety enforcement. Option D relies heavily on custom logic and introduces unnecessary operational risk.
Therefore, Option B provides the most robust, ethical, and auditable GenAI architecture for wildlife behavior research.


81. Frage
A GenAI developer is using Amazon Bedrock AgentCore to build an agentic AI application. The application orchestrates multiple foundation models (FMs) across development and production environments. The application experiences intermittent failures during tool invocation phases. Each incident requires an average of 3 hours to diagnose because of complex error patterns in the FM chains.
The GenAI developer needs a solution that can identify AI-specific error patterns in tool invocation chains in less time than the existing manual process. The solution must support collaboration between development and security teams. The solution must provide capabilities to protect sensitive customer data.
Which solution will meet these requirements?

Antwort: D

Begründung:
Option D is correct because AgentCore Observability is purpose-built for tracing and troubleshooting agentic workloads. AgentCore traces capture the complete execution path, including processing steps, external service calls, tool invocations with inputs and outputs, execution times, decision points, exceptions, recovery attempts, and final responses. CloudWatch Generative AI Observability provides preconfigured views for agent, model, tool, latency, token, and error behavior, allowing development and security teams to investigate failures from shared telemetry instead of correlating several independent logging systems. AWS Distro for OpenTelemetry provides standardized instrumentation for non-runtime agents and related application components. CloudWatch also provides sensitive-data protection capabilities for observability data. Options A and C provide infrastructure-focused monitoring but require more manual correlation and custom dashboards. Option B does not provide the same production-grade centralized AgentCore telemetry. AWS Documentation


82. Frage
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