AWSProfessional

AWS Certified Generative AI Developer – Professional

AIP-C01

Design, build, evaluate and operate generative AI applications on AWS (Amazon Bedrock, agents, RAG).

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Duration
180 min
Exam questions
75
Passing score
750 / 1000
Exam fee
$300
Question formats:Multiple choiceMultiple responseOrderingMatching
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Question 1Foundation Model Integration, Data Management, and Compliance

An engineer adds a reranking configuration to the Retrieve calls made by a production service. The same configuration works from the engineer's own console session against the same knowledge base, but the service receives an access denied error on every reranked request while unreranked requests continue to succeed. The knowledge base, the Region and the request body are otherwise identical. What is the most likely cause?

  1. A.

    Reranking is available only through the console and the AWS Management Console session is therefore the only context in which a reranker model can be applied to knowledge base results, so the service must replicate the reordering in application code.

  2. B.

    The reranker model must be enabled per knowledge base rather than per caller, and the engineer enabled it for the console session only, so the service will succeed once the same model is attached to the knowledge base resource itself.

  3. C.

    Reranked requests exceed the maximum request size for the Retrieve operation because the candidate documents are echoed back into the request, and the service is receiving an access denied error as the generic response to an oversized request body.

  4. D.

    The service's execution role has permission to retrieve from the knowledge base but not to invoke the reranker model, which is a separate model invocation that requires its own permission.

Show answer

Answer: D

Reranking is a separate model invocation and needs its own permission on the caller's role.

  • A. Reranking is available through the API on Retrieve and RetrieveAndGenerate, not only in the console.
  • B. The reranker is specified in the request configuration rather than attached to the knowledge base resource.
  • C. Candidate documents are not echoed into a knowledge base Retrieve request, and size limits do not return access denied.
  • D. The reranker is a separate model invocation requiring its own permission on the calling role.

What's on the exam

5 domains · 20 task statements, straight from the official exam guide (as of Version 1.0 AIP-C01).

  1. 1.1Analyze requirements and design GenAI solutions.
    • Skill 1.1.1: Create comprehensive architectural designs that align with specific business needs and technical constraints (for example, by using appropriate FMs, integration patterns, deployment strategies).
    • Skill 1.1.2: Develop technical proof-of-concept implementations to validate feasibility, performance characteristics, and business value before proceeding to full-scale deployment (for example, by using Amazon Bedrock).
    • Skill 1.1.3: Create standardized technical components to ensure consistent implementation across multiple deployment scenarios (for example, by using the AWS Well-Architected Framework, AWS WA Tool Generative AI Lens).
  2. 1.2Select and configure FMs.
    • Skill 1.2.1: Assess and choose FMs to ensure optimal alignment with specific business use cases and technical requirements (for example, by using performance benchmarks, capability analysis, limitation evaluation).
    • Skill 1.2.2: Create flexible architecture patterns to enable dynamic model selection and provider switching without requiring code modifications (for example, by using AWS Lambda, Amazon API Gateway, AWS AppConfig).
    • Skill 1.2.3: Design resilient AI systems to ensure continuous operation during service disruptions (for example, by using AWS Step Functions circuit breaker patterns, Amazon Bedrock Cross-Region Inference for models that have limited regional availability, cross-Region model deployment, graceful degradation strategies).
    • Skill 1.2.4: Implement FM customization deployment and lifecycle management (for example, by using Amazon SageMaker AI to deploy domain-specific fine-tuned models, parameter-efficient adaptation techniques such as low-rank adaptation [LoRA] and adapters for model deployment, SageMaker Model Registry for versioning and to deploy customized models, automated deployment pipelines to update models, rollback strategies for failed deployments, lifecycle management to retire and replace models).
  3. 1.3Implement data validation and processing pipelines for FM consumption.
    • Skill 1.3.1: Create comprehensive data validation workflows to ensure data meets quality standards for FM consumption (for example, by using AWS Glue Data Quality, SageMaker Data Wrangler, custom Lambda functions, Amazon CloudWatch metrics).
    • Skill 1.3.2: Create data processing workflows to handle complex data types including text, image, audio, and tabular data with specialized processing requirements for FM consumption (for example, by using Amazon Bedrock multimodal models, SageMaker Processing, AWS Transcribe, advanced multimodal pipeline architectures).
    • Skill 1.3.3: Format input data for FM inference according to model-specific requirements (for example, by using JSON formatting for Amazon Bedrock API requests, structured data preparation for SageMaker AI endpoints, conversation formatting for dialog-based applications).
    • Skill 1.3.4: Enhance input data quality to improve FM response quality and consistency (for example, by using Amazon Bedrock to reformat text, Amazon Comprehend to extract entities, Lambda functions to normalize data).
  4. 1.4Design and implement vector store solutions.
    • Skill 1.4.1: Create advanced vector database architectures specifically for FM augmentation to enable efficient semantic retrieval beyond traditional search capabilities (for example, by using Amazon Bedrock Knowledge Bases for hierarchical organization, Amazon OpenSearch Service with the Neural plugin for Amazon Bedrock integration for topic-based segmentation, Amazon RDS with Amazon S3 document repositories, Amazon DynamoDB with vector databases for metadata and embeddings).
    • Skill 1.4.2: Develop comprehensive metadata frameworks to improve search precision and context awareness for FM interactions (for example, by using S3 object metadata for document timestamps, custom attributes for authorship information, tagging systems for domain classification).
    • Skill 1.4.3: Implement high-performance vector database architectures to optimize semantic search performance at scale for FM retrieval (for example, by using OpenSearch sharding strategies, multi-index approaches for specialized domains, hierarchical indexing techniques).
    • Skill 1.4.4: Use AWS services to create integration components to connect with resources (for example, document management systems, knowledge bases, internal wikis for comprehensive data integration in GenAI applications).
    • Skill 1.4.5: Design and deploy data maintenance systems to ensure that vector stores contain current and accurate information for FM augmentation (for example, by using incremental update mechanisms, real-time change detection systems, automated synchronization workflows, scheduled refresh pipelines).
  5. 1.5Design retrieval mechanisms for FM augmentation.
    • Skill 1.5.1: Develop effective document segmentation approaches to optimize retrieval performance for FM context augmentation (for example, by using Amazon Bedrock chunking capabilities, Lambda functions to implement fixed-size chunking, custom processing for hierarchical chunking based on content structure).
    • Skill 1.5.2: Select and configure optimal embedding solutions to create efficient vector representations for semantic search (for example, by using Amazon Titan embeddings based on dimensionality and domain fit, by evaluating performance characteristics of Amazon Bedrock embedding models, by using Lambda functions to batch generate embeddings).
    • Skill 1.5.3: Deploy and configure vector search solutions to enable semantic search capabilities for FM augmentation (for example, by using OpenSearch Service with vector search capabilities, Amazon Aurora with the pgvector extension, Amazon Bedrock Knowledge Bases with managed vector store functionality).
    • Skill 1.5.4: Create advanced search architectures to improve the relevance and accuracy of retrieved information for FM context (for example, by using OpenSearch for semantic search, hybrid search that combines keywords and vectors, Amazon Bedrock reranker models).
    • Skill 1.5.5: Develop sophisticated query handling systems to improve the retrieval effectiveness and result quality for FM augmentation (for example, by using Amazon Bedrock for query expansion, Lambda functions for query decomposition, Step Functions for query transformation).
    • Skill 1.5.6: Create consistent access mechanisms to enable seamless integration with FMs (for example, by using function calling interfaces for vector search, Model Context Protocol [MCP] clients for vector queries, standardized API patterns for retrieval augmentation).
  6. 1.6Implement prompt engineering strategies and governance for FM interactions.
    • Skill 1.6.1: Create effective model instruction frameworks to control FM behavior and outputs (for example, by using Amazon Bedrock Prompt Management to enforce role definitions, Amazon Bedrock Guardrails to enforce responsible AI guidelines, template configurations to format responses).
    • Skill 1.6.2: Build interactive AI systems to maintain context and improve user interactions with FMs (for example, by using Step Functions for clarification workflows, Amazon Comprehend for intent recognition, DynamoDB for conversation history storage).
    • Skill 1.6.3: Implement comprehensive prompt management and governance systems to ensure consistency and oversight of FM operations (for example, by using Amazon Bedrock Prompt Management to create parameterized templates and approval workflows, Amazon S3 to store template repositories, AWS CloudTrail to track usage, Amazon CloudWatch Logs to log access).
    • Skill 1.6.4: Develop quality assurance systems to ensure prompt effectiveness and reliability for FMs (for example, by using Lambda functions to verify expected output, Step Functions to test edge cases, CloudWatch to test prompt regression).
    • Skill 1.6.5: Enhance FM performance to refine prompts iteratively and improve response quality beyond basic prompting techniques (for example, by using structured input components, output format specifications, chain-of-thought instruction patterns, feedback loops).
    • Skill 1.6.6: Design complex prompt systems to handle sophisticated tasks with FMs (for example, by using Amazon Bedrock Prompt Flows for sequential prompt chains, conditional branching based on model responses, reusable prompt components, integrated pre-processing and post-processing steps).

Outline reproduced from the vendor's public exam guide for study reference.Official guide

AIP-C01 practice — frequently asked questions

Are these real AIP-C01 exam questions?

No. Every question on CertifyAIx is original, written by us against Amazon Web Services's publicly available AIP-C01 exam guide to rehearse the skills it lists. None are actual exam questions, and CertifyAIx is not affiliated with or endorsed by Amazon Web Services.

How many AIP-C01 practice questions are there?

836 practice questions, including 3 full-length timed mock exams and 78 domain papers of up to 25 questions (mixed and by topic). Every question has a detailed explanation of why the right answer wins and why each distractor loses.

Is the content up to date with the current AIP-C01 exam guide?

The questions are written against the AIP-C01 exam guide dated Version 1.0 AIP-C01, and we revise them when Amazon Web Services updates the guide.

What question formats are covered?

The same formats the real AIP-C01 uses: Multiple choice, Multiple response, Ordering, Matching. Each is rendered and graded the way the exam does it.

How long is the AIP-C01 exam and how many questions does it have?

According to Amazon Web Services's published exam details: 75 questions, 180 minutes, passing score 750 / 1000. Our mock exams use the same time limit and question count. Always confirm current details with Amazon Web Services before booking.

Can I practise AIP-C01 for free?

Yes. 3 papers are free, with up to 10 questions a day on the free plan and no card needed. Pro unlocks every paper and mock exam with no daily limit.

Does CertifyAIx guarantee that I will pass?

No practice material can guarantee a result. CertifyAIx helps you find and close your weak areas — accuracy by exam-guide domain and topic shows what to study next.