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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 180 minutes |
| Exam Price: | $300 USD |
| Available Languages: | English |
| Exam Format: | Ordering, Multiple Response, Matching, Multiple Choice |
| Real Exam Qty: | 85 |
| Passing Score: | 750 (on a scale of 100-1000) |
| Related Certifications: | AWS Certified AI Practitioner |
| 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/ |
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NEW QUESTION # 51
A GenAI developer is building a Retrieval Augmented Generation (RAG)-based customer support application that uses Amazon Bedrock foundation models (FMs). The application needs to process 50 GB of historical customer conversations that are stored in an Amazon S3 bucket as JSON files. The application must use the processed data as its retrieval corpus. The application's data processing workflow must extract relevant data from customer support documents, remove customer personally identifiable information (PII), and generate embeddings for vector storage. The processing workflow must be cost-effective and must finish within 4 hours.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Option D is the best solution because it delivers a fully managed, scalable pipeline with minimal infrastructure management while meeting the 50 GB and 4-hour constraint. AWS Step Functions provides a serverless orchestration layer that can coordinate parallel processing steps, retries, and error handling without managing clusters or tuning long-running compute.
Using Amazon Comprehend for PII detection fulfills the requirement to remove customer PII in a managed and consistent way. Step Functions can coordinate Comprehend calls at scale and route sanitized outputs into the embedding step. Generating embeddings with Amazon Bedrock keeps the entire workflow within AWS managed services, eliminates the need to maintain custom embedding models, and supports consistent vector representations for downstream retrieval.
Direct integration with Amazon OpenSearch Serverless provides a low-operations vector store that can handle large-scale indexing and similarity search without cluster sizing, node maintenance, or shard management.
This aligns strongly with the requirement for least operational overhead and supports growth beyond the initial 50 GB corpus. Step Functions can batch and parallelize ingestion into OpenSearch Serverless to meet the 4-hour completion goal in a cost-effective manner by controlling concurrency, chunk sizes, and failure handling.
Option A can be difficult and costly at this scale because Lambda concurrency and per-invocation overhead can become complex to tune for 50 GB within 4 hours. Option B introduces SageMaker Processing and embedding model management, increasing operational complexity. Option C requires EMR cluster management and tuning, which is the opposite of minimal overhead.
Therefore, Option D is the most operationally efficient, scalable, and managed approach to build the required PII-sanitized embedding pipeline for a RAG corpus.
NEW QUESTION # 52
A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture.
The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A best meets the requirements because it applies an AWS-aligned multi-agent pattern that cleanly separates responsibilities: a supervisor agent performs intent classification and orchestration, while specialized collaborator agents handle domain-specific tasks using the right knowledge sources. This structure is well suited for healthcare workflows where clinical questions, scheduling, and insurance processes require different policies, terminology, and data access boundaries.
The requirement for appropriate domain-specific responses is addressed by routing each user query to a department-focused collaborator agent that is grounded with its own department-specific knowledge base.
Using Retrieval Augmented Generation with the correct knowledge base improves factual alignment and reduces cross-department leakage (for example, avoiding claims content in a clinical answer). It also supports better prompt grounding and more consistent tone and constraints per department.
The requirement to isolate data maps to using separate knowledge bases per agent and enforcing access through IAM controls, ensuring that each agent can retrieve only from the authorized datasets. This is important for minimizing unintended exposure of sensitive or irrelevant departmental data and supports governance and compliance needs.
For scalability and thousands of parallel interactions, this architecture minimizes contention and bottlenecks. Each collaborator agent can scale independently because requests are distributed across multiple agents and multiple retrieval backends. Operationally, onboarding new features is also simpler: the company can add a new collaborator agent (for example, "billing disputes" or "pharmacy refills") with its own knowledge base and policies without redesigning the entire assistant.
Option B introduces unnecessary complexity with multiple supervisors and manual handoffs. Option C overloads a single agent with broad instructions and rule-based routing, which increases prompt complexity and reduces maintainability as features grow. Option D creates high operational complexity and risks inconsistent outputs when merging responses from parallel supervisors, and it weakens data isolation by using a shared knowledge base across agents.
NEW QUESTION # 53
A university is building an AI-powered application that includes several sub-applications. The sub- applications include AI assistants, assignment graders, and internal analytics applications. The university is defining and testing multiple prompts by using various foundation models (FMs). The university wants to compare variants of each prompt and choose the variant that yield outputs that are best-suited for specified use cases. The university requires a version control solution for the prompts. The university must be able to test prompt variations and collect audit trails for prompt changes and usage. The solution must also maintain consistency while allowing the prompts to integrate into the main application. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: A,D
Explanation:
Amazon Bedrock Prompt Management is the purpose-built service for prompt lifecycle management. it provides native version control , allowing developers to test and compare variants side-by-side. Use of parameterized variables ensures that a single prompt structure can be consistently reused across different sub-applications (assistants vs. graders) while still being tailored to the specific context. To " integrate into the main application " with minimal overhead, Amazon Bedrock Flows provide a managed orchestration layer.
Flows allow developers to link managed prompts with AWS services (like knowledge bases or Lambda functions) without writing complex state-machine logic in Step Functions (Option B). This combination ensures consistent, auditable, and easily deployable prompt assets across the university ' s diverse use cases.
NEW QUESTION # 54
A healthcare company uses a multi-agent system on Amazon Bedrock AgentCore. The system uses multiple FMs to process 2,000 medical report documents daily. The documents range from 5 to 50 pages each.
The company discovers that the system is not performing complete analysis on documents that exceed 30 pages. Documents over 30 pages miss critical information from later sections. The results are truncated, but there are no explicit errors.
The company must resolve this issue while maintaining response times under 10 seconds. The company must keep processing costs low.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B is correct because the symptoms indicate context-window pressure rather than a compute-memory or Lambda-timeout problem. Large documents should be split into manageable chunks while preserving semantic continuity across boundaries. Amazon Bedrock Knowledge Bases supports chunking strategies, including semantic chunking and overlap-oriented approaches, so relevant context can be retained without sending an entire long report to the model at once. Monitoring InputTokenCount provides evidence of context utilization and helps identify when requests approach model limits. Diagnostic logging can then locate where truncation occurs. Option A increases timeouts but does not enlarge the model context window. Option C adds sequential orchestration that can jeopardize the 10-second target. Option D increases infrastructure resources, but host memory does not change a foundation model's maximum context window and adds unnecessary cost. AWS Documentation
NEW QUESTION # 55
A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company ' s engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company ' s marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements?
Answer: A
NEW QUESTION # 56
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2026 Latest Real4dumps AIP-C01 PDF Dumps and AIP-C01 Exam Engine Free Share: https://drive.google.com/open?id=15H37OodUrNH1lLY69DMfYcmEKkNP3Dv-