AIP-C01 Deutsche, AIP-C01 Prüfungen

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Amazon AWS Certified Generative AI Developer - Professional AIP-C01 Prüfungsfragen mit Lösungen (Q89-Q94):

89. Frage
A company is developing a generative AI (GenAI) application that analyzes customer service calls in real time and generates suggested responses for human customer service agents. The application must process
500,000 concurrent calls during peak hours with less than 200 ms end-to-end latency for each suggestion. The company uses existing architecture to transcribe customer call audio streams. The application must not exceed a predefined monthly compute budget and must maintain auto scaling capabilities.
Which solution will meet these requirements?

Antwort: A

Begründung:
Option B is the correct solution because it aligns with AWS guidance for building high-throughput, ultra-low- latency GenAI applications while maintaining predictable costs and automatic scaling. Amazon Bedrock provides access to foundation models that are specifically optimized for real-time inference use cases, including conversational and recommendation-style workloads that require responses within milliseconds.
Low-latency models in Amazon Bedrock are designed to handle very high request rates with minimal per- request overhead. Purchasing provisioned throughput ensures that sufficient model capacity is reserved to handle peak loads, eliminating cold starts and reducing request queuing during traffic surges. This is critical when supporting up to 500,000 concurrent calls with strict latency requirements.
Automatic scaling policies allow the application to dynamically adjust capacity based on demand, ensuring cost efficiency during off-peak hours while maintaining performance during peak usage. This directly supports the requirement to stay within a predefined monthly compute budget.
Option A fails because batch processing and complex reasoning models introduce higher latency and are not suitable for real-time suggestions. Option C introduces significantly higher operational and cost overhead due to dedicated GPU instances and manual scaling responsibilities. Option D is optimized for batch workloads and cannot meet the sub-200 ms latency requirement.
Therefore, Option B provides the best balance of performance, scalability, cost control, and operational simplicity using AWS-native GenAI services.


90. Frage
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?

Antwort: A

Begründung:
Option B is the best solution because hierarchical chunking is specifically designed to preserve broader semantic context while still enabling precise retrieval at paragraph or sub-paragraph granularity. The problem described-answers citing irrelevant sections when a query spans multiple paper sections-often occurs when chunks are either too small (losing cross-paragraph context) or too "flat" (retrieving isolated snippets without their surrounding rationale).
In a scientific paper, related information is frequently distributed across methodology, results, and discussion.
Flat, fixed-size chunking (Option A) can split these logically connected ideas into separate chunks, causing retrieval to surface fragments that match a term but not the full intent. Semantic chunking (Option C) improves boundary placement, but it does not inherently provide a multi-resolution structure that helps preserve section-level continuity at massive scale.
Hierarchical chunking solves this by creating parent chunks (larger context windows) that capture broader section context and child chunks (smaller units) that retain retrieval precision. When the retriever identifies relevant child chunks, it can also bring in the associated parent context so the foundation model sees the surrounding methodological or discussion framing. The defined overlaps further reduce the risk that key transitions or references are split across chunks.
This approach is well suited for a corpus of 25 million papers because it improves relevance without requiring a custom reranking model or a manual preprocessing pipeline. It remains operationally efficient because it is configured at the knowledge base level rather than implemented through custom code per document.
Option D introduces high operational complexity and inconsistent document handling at scale. Therefore, Option B best meets the requirement to preserve semantic context across related paragraphs and improve citation relevance across scientific paper sections.


91. Frage
A company uses an AI assistant application to summarize the company's website content and provide information to customers. The company plans to use Amazon Bedrock to give the application access to a foundation model (FM).
The company needs to deploy the AI assistant application to a development environment and a production environment. The solution must integrate the environments with the FM. The company wants to test the effectiveness of various FMs in each environment. The solution must provide product owners with the ability to easily switch between FMs for testing purposes in each environment.
Which solution will meet these requirements?

Antwort: C

Begründung:
Option C best satisfies the requirement for flexible FM testing across environments while minimizing operational complexity and aligning with AWS-recommended deployment practices. Amazon Bedrock supports invoking on-demand foundation models through the FoundationModel abstraction, which allows applications to dynamically reference different models without requiring dedicated provisioned capacity. This is ideal for experimentation and A/B testing in both development and production environments.
Using a single AWS CDK application ensures infrastructure consistency and reduces duplication.
Environment-specific configuration, such as selecting different foundation model IDs, can be externalized through parameters, context variables, or environment-specific configuration files. This allows product owners to easily switch between FMs in each environment without modifying application logic.
A single AWS CodePipeline with distinct deployment stages for development and production is an AWS best practice for multi-environment deployments. It enforces consistent build and deployment steps while still allowing environment-level customization. AWS CodeBuild deploy actions enable automated, repeatable deployments, reducing manual errors and improving governance.
Option A increases complexity by introducing multiple pipelines and relies on provisioned models, which are not necessary for FM evaluation and experimentation. Provisioned throughput is better suited for predictable, high-volume production workloads rather than frequent model switching.
Option B creates unnecessary operational overhead by duplicating CDK applications and pipelines, making long-term maintenance more difficult.
Option D directly conflicts with infrastructure-as-code best practices by manually recreating development resources, which increases configuration drift and reduces reliability.
Therefore, Option C provides the most flexible, scalable, and AWS-aligned solution for testing and switching foundation models across development and production environments.


92. Frage
A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds.
The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention.
Which solution will meet these requirements?

Antwort: A

Begründung:
Option A is the most complete solution because it provides a fully automated canary strategy with staged traffic shifts, metric-based decisioning, and automatic rollback, all using managed AWS services. The requirement emphasizes automation, health-based traffic progression, and zero manual intervention to revert if performance degrades.
AWS Step Functions is well suited for orchestrating controlled deployment workflows with deterministic stages, waits, and conditional branches. By shifting traffic in stages and pausing for observation windows, the system can evaluate real-time inference latency and error rates before promoting more traffic to the new model version. Amazon CloudWatch provides the necessary real-time metrics and alarms for latency and error monitoring.
Invoking a Lambda function to evaluate CloudWatch metrics enables dynamic logic: increase traffic if thresholds remain healthy, reduce traffic or roll back if error rates rise or latency exceeds limits. Step Functions can halt the deployment by stopping progression or triggering rollback steps immediately, meeting the requirement for automated revert without human action.
Amazon EventBridge provides reliable automation triggers when a new model version is released, ensuring the deployment process is event-driven and repeatable.
Option B depends on "external logic," which introduces operational risk and does not guarantee automatic rollback without custom systems. Option C incorrectly uses SageMaker endpoint variants to represent Bedrock model versions, which is not the intended integration model. Option D is overly indirect and operationally complex, using log pipelines and automation runbooks instead of direct metric-based traffic control.
Therefore, Option A best meets the requirements for automated gradual traffic shifting, real-time monitoring, and automatic rollback for Amazon Bedrock model deployments in a canary strategy.


93. Frage
Example Corp provides a personalized video generation service that millions of enterprise customers use.
Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?

Antwort: A

Begründung:
Option A is the correct solution because Amazon Q Business is explicitly designed to provide secure, governed access to enterprise data while preserving customer ownership and control. Each customer maintains their own Amazon Q Business index, which ensures that data never leaves the customer's control boundary unless explicitly shared through approved access mechanisms.
By designating Example Corp as a data accessor, customers can allow controlled, auditable access to their indexed content through secure APIs. This model satisfies strict data governance requirements, including data ownership, access transparency, and revocation capability. Customers do not need to expose raw data or deploy infrastructure in Example Corp's environment.
Amazon Q Business provides high semantic accuracy through managed indexing, ranking, and retrieval optimizations. Because real-time access is not required, this approach avoids the complexity and latency challenges of live federated retrieval while still delivering fast query performance suitable for customer experience use cases.
Option B introduces unnecessary operational complexity by requiring real-time MCP servers per customer.
Option C requires customers to manage Amazon Bedrock knowledge bases and enable cross-account access, which increases integration complexity and governance risk. Option D requires shared Amazon Kendra indexes across accounts, which complicates access control and data ownership boundaries.
Therefore, Option A provides the cleanest, lowest-overhead architecture that meets data governance, accuracy, performance, and scalability requirements while minimizing operational burden for both Example Corp and its customers.


94. Frage
......

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