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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Passing Score: | 750 (scaled score 100โ1000) |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner |
| Exam Duration: | 180 minutes |
| Exam Price: | 300 USD |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Exam Format: | Matching, Ordering, Multiple choice, Multiple response |
| Available Languages: | English, Japanese, Simplified Chinese, Korean |
| Recommended Training: | AWS Certified Generative AI Developer - Professional Exam Guide AWS Skill Builder - Official Training |
| 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 # 66
A company is planning to deploy multiple generative AI (GenAI) applications to five independent business units that operate in multiple countries in Europe and the Americas. Each application uses Amazon Bedrock Retrieval Augmented Generation (RAG) patterns with business unit-specific knowledge bases that store terabytes of unstructured data.
The company must establish well-architected, standardized components for security controls, observability practices, and deployment patterns across all the GenAI applications. The components must be reusable, versioned, and governed consistently.
Which solution will meet these requirements?
Answer: B
Explanation:
Option B best meets the requirement for reusable, versioned, and consistently governed components across multiple business units because it implements "platform-level standardization" through infrastructure as code plus automated compliance enforcement before deployment. Standardized CloudFormation templates provide reusable building blocks for security controls (identity, networking boundaries, encryption), observability practices (metrics, logs, traces), and RAG deployment patterns (knowledge base integration, ingestion pipelines, retrieval controls). This aligns with AWS guidance to operationalize well-architected patterns through repeatable templates rather than ad hoc implementations.
A centralized repository enables version control, change review, and governance of templates across all five business units. This satisfies the "versioned" and "reusable" requirements and provides a single source of truth for approved architectures. Integrating a CI/CD pipeline ensures that deployments are consistent and automated, reducing drift between business units and Regions.
CloudFormation Guard is most effective when used as a preventive control in the pipeline, not only after deployment. By running Guard rules during build or pre-deploy stages, the organization can enforce mandatory security and observability configurations and block noncompliant changes before they reach production. This supports consistent governance while still enabling business units to deploy quickly.
Option A performs compliance validation after deployment, which allows policy violations to be deployed first and remediated later. Option C provides governed provisioning but requiring console-based deployment reduces automation and can slow standardized CI/CD adoption; it also adds an additional governance layer that is not required to meet the stated needs. Option D is not enforceable and does not provide reusable, versioned, governed components.
Therefore, Option B provides the strongest, most scalable, and most consistently governed approach for standardized GenAI deployments across business units.
NEW QUESTION # 67
A company is using Amazon Bedrock to design an application to help researchers apply for grants. The application is based on an Amazon Nova Pro foundation model (FM). The application contains four required inputs and must provide responses in a consistent text format. The company wants to receive a notification in Amazon Bedrock if a response contains bullying language. However, the company does not want to block all flagged responses.
The company creates an Amazon Bedrock flow that takes an input prompt and sends it to the Amazon Nova Pro FM. The Amazon Nova Pro FM provides a response.
Which additional steps must the company take to meet these requirements? (Select TWO.)
Answer: A,C
Explanation:
The correct answers are A and D because they collectively satisfy the requirements for structured inputs, consistent output formatting, and non-blocking detection of bullying language.
Option A is required because Amazon Bedrock Prompt Management enables prompt templates with explicit input variables and defined output formats. By defining the four required inputs as variables, the company ensures that every invocation of the Amazon Nova Pro FM receives the correct structured inputs. Specifying the output format ensures consistent responses, which is essential for a grants application workflow. Adding the managed prompt to the prompts node of the flow allows Bedrock Flows to invoke the model using this standardized configuration.
Option D addresses the requirement to receive notifications when bullying language is detected without blocking responses. Amazon Bedrock guardrails support content filters with configurable actions. By applying the insults content filter and setting the response action to detect, the system flags responses containing bullying or insulting language while still allowing the response to be returned. This enables monitoring, alerting, and auditing without interrupting application functionality.
Option B is incorrect because setting the filter response to block contradicts the requirement not to block all flagged responses. Option C introduces a prompt router, which is unnecessary because the application uses a single Amazon Nova Pro FM. Option E incorrectly attempts to enforce input variables and output formatting through an inference profile, which does not provide prompt-level variable enforcement or formatting guarantees.
Therefore, A and D together provide structured prompt management and non-blocking safety detection with minimal operational complexity.
NEW QUESTION # 68
A company wants to select a new FM for its AI assistant. A GenAI developer needs to generate evaluation reports to help a data scientist assess the quality and safety of various foundation models FMs. The data scientist provides the GenAI developer with sample prompts for evaluation. The GenAI developer wants to use Amazon Bedrock to automate report generation and evaluation.
Which solution will meet this requirement?
Answer: B
Explanation:
Option B is correct because it uses the managed evaluation capability in Amazon Bedrock that is intended specifically for comparing foundation models using a consistent prompt set and producing structured results with minimal custom tooling. In a Bedrock evaluation workflow, you provide an input dataset of prompts, typically in JSON Lines format so each line represents one evaluation record. Storing the JSONL file in Amazon S3 allows Bedrock to read the dataset at scale and write standardized evaluation outputs back to S3 for downstream analysis, sharing, and retention.
The key requirement is to assess both quality and safety across multiple models. A Bedrock evaluation job can use a judge model to score the generated outputs against defined criteria. This approach supports repeatable, apples-to-apples comparisons because the same judge model and scoring rubric can be applied to every candidate foundation model. The candidate models are configured as generators, meaning each evaluation job run uses one selected FM to produce answers for the same prompt set, and the judge model evaluates those answers. That matches the requirement to generate evaluation reports that help a data scientist select the best FM.
Option A does not use Bedrock evaluation jobs, and a knowledge base plus RetrieveAndGenerate is a RAG pattern, not an evaluation framework. It would produce responses but not standardized scoring and reporting suitable for model selection. Option C is incorrect because Bedrock evaluation outputs are delivered to S3, not directly to a BI destination, and selecting the candidate FM as the evaluator conflicts with the intended pattern of using a stable judge model. Option D misuses knowledge bases and retrieval evaluation types when the requirement is prompt-based model assessment rather than evaluating retrieval quality.
NEW QUESTION # 69
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: D
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 # 70
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?
Answer: C
Explanation:
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.
NEW QUESTION # 71
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