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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Exam Format: | Multiple response, Ordering, Multiple choice, Matching |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Exam Price: | 300 USD |
| Exam Duration: | 180 minutes |
| Passing Score: | 750 (scaled score 100–1000) |
| Available Languages: | Japanese, English, Simplified Chinese, Korean |
| Recommended Training: | AWS Skill Builder - Official Training AWS Certified Generative AI Developer - Professional Exam Guide |
| Exam Registration: | Pearson VUE Registration AWS Certification Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 2+ years of experience building production-grade applications on AWS or open-source technologies; 1+ year hands-on experience with generative AI implementation; knowledge of AWS compute, storage, networking, security, and deployment tools |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/examguides/ai-professional-01.html |
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NEW QUESTION # 85
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:
Comprehensive and Detailed 250 to 350 words of Explanation From AWS Generative AI concepts and services documents:
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 # 86
An insurance company is using Amazon Bedrock to build a claims processing application. The application must perform the following steps in sequence: analyze documents, extract data, and generate recommendations. Claims over $10,000 require an additional fraud analysis step before the application provides a recommendation.
Which solution will meet these requirements with the LEAST operational complexity?
Answer: B
Explanation:
Amazon Bedrock Flows-referred to as "Amazon Bedrock Prompt Flows" in the question-is the lowest- complexity solution because the workflow consists primarily of generative-AI processing stages and one straightforward conditional branch. Bedrock Flows provides a managed visual workflow environment in which nodes represent steps that invoke Amazon Bedrock or related resources. This eliminates the need to build a separate orchestration service merely to connect multiple model interactions.
A prompt node defines a prompt, receives values as input variables, invokes the configured model, and produces the model completion as its output. Therefore, separate prompt nodes can represent document analysis, structured data extraction, fraud analysis, and recommendation generation.
A condition node provides deterministic branching. AWS documents relational operators including > , > = , <
, < = , == , and != , and the node can direct its input to different downstream nodes based on the configured condition. The application can therefore test whether the claim amount is greater than $10,000. Claims above that threshold are routed through the fraud-analysis node before reaching the recommendation stage; other claims can go directly to recommendation generation.
A Step Functions implementation in A would work technically and is appropriate when orchestration spans a broad set of distributed AWS services. However, for a Bedrock-centric GenAI workflow, it adds another orchestration layer and therefore is not the least-complex solution. B unnecessarily introduces autonomous agent reasoning for deterministic sequential processing. D requires custom Lambda invocation code, error handling, routing logic, deployment, and maintenance.
Because the required workflow maps directly to Bedrock ' s managed prompt and condition nodes, C minimizes infrastructure and custom orchestration while preserving deterministic control over the $10,000 fraud-analysis requirement.
NEW QUESTION # 87
A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM.
Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.
Which combination of solutions will meet this requirement? (Select TWO.)
Answer: B,C
NEW QUESTION # 88
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?
Answer: B
Explanation:
When a foundation model fails to include specific required content or fails to integrate it coherently, prompt engineering techniques like output indicators or " wrappers " are highly effective. By explicitly defining where the promotional message should appear (e.g., " The response must end with the following message:
[PROMO TEXT] " ) or providing an example output structure, the developer reinforces the constraint within the model ' s generation path. This is more direct and less computationally expensive than generating multiple variants and reranking them (Option B) or adding complex post-processing layers (Option C). Guardrails (Option A) are intended for filtering harmful content rather than enforcing specific promotional copy insertion.
NEW QUESTION # 89
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?
Answer: A
Explanation:
Option A best matches the requirement because managed knowledge-base retrieval provides source attribution and lets access be governed through IAM, avoiding custom result-merging logic. Amazon Bedrock RetrieveAndGenerate returns citations tied to retrieved source references. One implementation detail matters under current AWS documentation: structured Knowledge Bases use Amazon Redshift as the query engine; Amazon Redshift federated queries can access Amazon RDS and Aurora operational databases. Therefore, the RDS-backed knowledge-base leg should be implemented through that supported structured-data path rather than interpreted as a direct native RDS vector connector. Options B, C, and D introduce extra agents or Lambda-based retrieval/merging, which increases development effort and makes unified citation handling more custom. A keeps both information sources behind managed retrieval and preserves provenance while IAM controls access. AWS Documentation
NEW QUESTION # 90
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