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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q99-Q104):

NEW QUESTION # 99
A financial services company needs to build a document analysis system that uses Amazon Bedrock to process quarterly reports. The system must analyze financial data, perform sentiment analysis, and validate compliance across batches of reports. Each batch contains 5 reports. Each report requires multiple foundation model (FM) calls. The solution must finish the analysis within 10 seconds for each batch. Current sequential processing takes 45 seconds for each batch.
Which solution will meet these requirements?

Answer: B

Explanation:
Option B is the correct solution because it parallelizes independent foundation model inference tasks while maintaining orchestration, observability, and time-bound execution. AWS Generative AI best practices emphasize reducing end-to-end latency by parallelizing independent inference calls rather than scaling individual calls vertically.
In this scenario, each report requires multiple independent analyses such as financial extraction, sentiment analysis, and compliance validation. These tasks do not depend on each other's output, making them ideal candidates for parallel execution. AWS Step Functions provides a Parallel state that can invoke multiple AWS Lambda functions simultaneously, drastically reducing total processing time compared to sequential execution.
By invoking Amazon Bedrock from separate Lambda functions in parallel, the system can reduce batch execution time from 45 seconds to well under the 10-second requirement, assuming each inference call remains within acceptable latency bounds. Step Functions also provide built-in error handling, retries, and state tracking, which improves reliability without increasing complexity.
CloudWatch metrics allow teams to monitor both workflow execution time and individual model inference latency, enabling performance tuning and operational visibility. Configuring client-side timeouts ensures that slow or failed model invocations do not block the entire batch.
Option A still processes tasks sequentially and therefore cannot meet the strict latency requirement. Option C introduces queuing delays and sequential processing within each report, which increases total execution time.
Option D relies on container-based sequential processing and adds unnecessary operational overhead for a workload that is event-driven and latency-sensitive.
Therefore, Option B best meets the performance, scalability, and operational efficiency requirements for high- speed batch document analysis using Amazon Bedrock.


NEW QUESTION # 100
A financial services company is developing a customer service AI assistant application that uses a foundation model (FM) in Amazon Bedrock. The application must provide transparent responses by documenting reasoning and by citing sources that are used for Retrieval Augmented Generation (RAG). The application must capture comprehensive audit trails for all responses to users. The application must be able to serve up to
10,000 concurrent users and must respond to each customer inquiry within 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Option A is the correct solution because it relies on native Amazon Bedrock capabilities to deliver transparency, auditability, scalability, and low latency with minimal operational overhead. Amazon Bedrock Knowledge Bases provide a fully managed Retrieval Augmented Generation (RAG) implementation that automatically handles document ingestion, embedding, retrieval, and source attribution, enabling the application to cite authoritative content without building custom pipelines.
Enabling tracing for Amazon Bedrock Agents provides end-to-end visibility into agent reasoning steps, tool usage, and model interactions. This satisfies the requirement for comprehensive audit trails and supports regulatory review in financial services environments. Structured prompts further ensure that responses explicitly present reasoning and supporting evidence in a controlled, auditable format.
Using Amazon API Gateway and AWS Lambda allows the application to scale automatically to thousands of concurrent users without capacity planning. These services are designed for bursty workloads and can easily support the stated requirement of up to 10,000 concurrent users. Amazon CloudFront reduces latency by caching and accelerating content delivery, helping the application meet the strict 2-second response-time requirement.
Option B introduces a custom RAG pipeline with OpenSearch, increasing operational complexity and maintenance effort. Option C lacks native RAG integration and does not provide transparent reasoning or citation management. Option D focuses on offline compliance reporting rather than real-time transparency and low-latency responses.
Therefore, Option A best meets all requirements while minimizing infrastructure and operational overhead.


NEW QUESTION # 101
A company needs a system to automatically generate study materials from multiple content sources. The content sources include document files (PDF files, PowerPoint presentations, and Word documents) and multimedia files (recorded videos). The system must process more than 10,000 content sources daily with peak loads of 500 concurrent uploads. The system must also extract key concepts from document files and multimedia files and create contextually accurate summaries. The generated study materials must support real- time collaboration with version control.
Which solution will meet these requirements?

Answer: A

Explanation:
Option B best fulfills all functional, scalability, and collaboration requirements by combining purpose-built AWS services with Amazon Bedrock capabilities. Amazon Bedrock Data Automation is designed to orchestrate large-scale, multimodal data processing pipelines and integrates naturally with foundation models for summarization and concept extraction. Using BDA to process document files ensures consistent preprocessing and model invocation at scale, which is essential for handling more than 10,000 sources per day with high concurrency.
Integrating Amazon Textract for PDFs enables accurate extraction of structured and unstructured text from scanned and digital documents, while Amazon Transcribe is the appropriate service for converting recorded videos into text for downstream semantic analysis. These services are optimized for their respective media types and feed clean, normalized inputs into Bedrock foundation models, improving the quality of contextual summaries.
Storing processed content in Amazon S3 with versioning enabled directly addresses the requirement for version control. S3 versioning provides immutable object history and rollback capabilities without additional complexity. Metadata storage in Amazon DynamoDB supports high-throughput, low-latency access patterns and scales automatically to handle peak upload concurrency.
Real-time collaboration is achieved through AWS AppSync GraphQL subscriptions combined with DynamoDB. AppSync enables real-time updates to connected clients whenever study materials are created or modified, making it well suited for collaborative editing and live synchronization. DynamoDB streams integrate seamlessly with AppSync to propagate changes efficiently.
The other options misuse services or fail to meet key requirements. Amazon SNS does not support collaborative state synchronization, Amazon DocumentDB is not optimized for versioned document storage, Amazon Neptune is unsuitable for document-centric workloads, and Amazon ElastiCache is not designed for durable storage or version control. Option B aligns with AWS best practices for scalable, multimodal generative AI systems built on Amazon Bedrock.


NEW QUESTION # 102
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?

Answer: D

Explanation:
Option A is the only choice that simultaneously addresses all three requirements: (1) higher output consistency for identical inputs, (2) sub-1-second performance, and (3) validated safety controls that block unsafe or hallucinated recommendations.
Provisioned throughput in Amazon Bedrock reserves capacity for the chosen model, which helps stabilize latency and reduces the chance of throttling or variable response times across Regions. This is important for a mobile app with strict latency goals and users distributed across multiple Regions. While provisioned throughput primarily improves performance predictability, it also reduces variability caused by contention during peak demand.
Amazon Bedrock guardrails provide validated safety controls to filter or block unsafe content. Semantic denial rules are appropriate for preventing dangerous brewing guidance (for example, excessively high temperatures) and for reducing hallucinated instructions that violate safety policies. Guardrails can be enforced consistently regardless of prompt-chain complexity, providing a uniform safety layer around the model outputs.
Amazon Bedrock Prompt Management supports controlled prompt versioning and approval workflows. By standardizing prompts, controlling changes, and ensuring the same prompt version is used for identical inputs, the company improves output stability and reduces drift caused by unmanaged prompt edits. Combined with strict configuration control (including fixed inference parameters such as temperature where appropriate), this improves repeatability and increases the likelihood of achieving the 99.5% consistency target.
Option B improves observability and experimentation but does not provide strong safety enforcement or latency stabilization. Option C improves performance through caching and tracing but does not provide validated safety controls and does not directly address cross-Region output consistency. Option D may improve retrieval but does not enforce safety controls or ensure repeatable outputs.
Therefore, Option A best meets the stability, performance, and safety requirements using AWS-native controls.


NEW QUESTION # 103
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 I/O records. Which solution will meet these requirements?

Answer: B

Explanation:
AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for
" tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.


NEW QUESTION # 104
......

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