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| Section | Weight | Objectives |
|---|---|---|
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Performance optimization - Cost optimization and token efficiency |
| AI Safety, Security, and Governance | 20% | - Security controls for GenAI applications - Guardrails and responsible AI implementation |
| Foundation Model Integration, Data Management, and Compliance | 31% | - Foundation model integration and usage - Compliance and responsible AI practices - Data management and RAG architectures |
| Testing, Validation, and Troubleshooting | 11% | - Monitoring and observability - Troubleshooting GenAI systems - Model evaluation and testing |
| Implementation and Integration | 26% | - Prompt engineering and prompt management - Embedding FMs into applications and workflows - Agent-based AI systems |
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NEW QUESTION # 54
A financial services company is developing a real-time generative AI (GenAI) assistant to support human call center agents. The GenAI assistant must transcribe live customer speech, analyze context, and provide incremental suggestions to call center agents while a customer is still speaking. To preserve responsiveness, the GenAI assistant must maintain end-to-end latency under 1 second from speech to initial response display.
The architecture must use only managed AWS services and must support bidirectional streaming to ensure that call center agents receive updates in real time.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B is the only solution that satisfies all strict real-time, streaming, and latency requirements. Amazon Transcribe streaming with partial results allows transcription fragments to be delivered before the speaker finishes a sentence. This significantly reduces perceived latency and enables downstream processing to begin immediately, which is essential for maintaining sub-1-second end-to-end response times.
Using Amazon Bedrock's InvokeModelWithResponseStream API enables token-level or chunk-level streaming responses from the foundation model. This allows the GenAI assistant to begin delivering suggestions to call center agents incrementally instead of waiting for a full model response. This streaming inference capability is critical for interactive, real-time agent assistance use cases.
Amazon API Gateway WebSocket APIs provide fully managed, bidirectional communication between backend services and agent dashboards. This ensures that updates flow continuously to agents as new transcription fragments and model outputs become available, preserving real-time responsiveness without requiring custom socket infrastructure.
Option A introduces additional synchronous processing layers and storage writes that increase latency. Option C uses batch transcription and post-call processing, which cannot meet real-time requirements. Option D uses embeddings and asynchronous messaging, which are not suitable for live incremental suggestions and bidirectional streaming.
Therefore, Option B best aligns with AWS real-time GenAI architecture patterns by combining streaming transcription, streaming model inference, and managed bidirectional communication while maintaining low latency and operational simplicity.
NEW QUESTION # 55
A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.
The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:
response = bedrock_runtime.invoke_model(
modelId="anthropic.claude-v2",
body=json.dumps(payload)
)
The company needs the application to use the provisioned throughput and to resolve the throttling issues.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B is the correct solution because Amazon Bedrock provisioned throughput is only used when the application explicitly invokes the provisioned model ARN, not the base foundation model ID. In the provided code, the application is calling the standard model identifier (anthropic.claude-v2), which routes requests to on-demand capacity instead of the purchased provisioned throughput.
When the CreateProvisionedModelThroughput API is used, Amazon Bedrock returns a provisioned model ARN that represents the reserved capacity. Applications must reference this ARN in the modelId parameter when invoking the model. If the base model ID is used instead, Bedrock treats the request as on-demand traffic, which explains why CloudWatch metrics show unused provisioned capacity alongside throttled on- demand requests.
Option A would increase capacity but would not fix the root cause because the application is not using the provisioned resource at all. Option C adds resiliency but does not ensure usage of provisioned throughput and would still incur throttling. Option D changes the response delivery mechanism but does not affect capacity routing.
Therefore, Option B directly resolves the throttling issue by correctly routing traffic to the reserved capacity and ensures that the company benefits from the provisioned throughput it has purchased.
NEW QUESTION # 56
A company is building a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During peak usage periods, the application experiences intermittent API timeouts that cause issues such as broken response chunks and delayed data delivery. The application struggles to ensure that prompts remain within token limits when handling complex customer inquiries of varying lengths.
Users have reported truncated inputs and incomplete responses. The company has also observed foundation model (FM) invocation failures.
The company needs a retry strategy that automatically handles transient service errors and prevents overwhelming Amazon Bedrock during peak usage periods. The strategy must also adapt to changing service availability and support response streaming and token-aware request handling.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B best meets all requirements because it combines AWS-recommended resiliency patterns for transient failures with streaming-aware handling and adaptive protection against cascading retries during peak load. When timeouts and throttling occur, naive retries can amplify traffic and worsen outages. Exponential backoff with jitter is the standard AWS best practice because it spreads retry attempts over time, reduces synchronized retry storms, and lowers the probability of repeatedly colliding with service limits.
The requirement also states the strategy must "adapt to changing service availability" and "prevent overwhelming Amazon Bedrock." A circuit breaker pattern directly addresses this by temporarily stopping or reducing retries when failure rates exceed a threshold, allowing the system to degrade gracefully instead of continually hammering the service. This is a key mechanism to prevent cascading failures during throttling events.
Because the application uses response streaming and experiences broken chunks, the retry strategy must be streaming-aware. A streaming response handler that detects chunk delivery timeouts and buffers already received chunks prevents the user from losing progress when a connection drops. Resuming from the last successfully received chunk minimizes redundant generation and reduces additional load on the model compared with restarting the entire stream. This supports better user experience and better service efficiency during intermittent failures.
Token-aware request handling is supported in this architecture because the application can apply token budgeting before invoking the model (for example, trimming or summarizing excessive context) while still preserving streaming output behavior. Option B provides the correct backbone for this by focusing on adaptive control and robust streaming recovery.
Option A is too simplistic and risks retry storms. Option C combines conflicting elements (global token limit, cached completions for streaming) and includes impractical "request only missing chunks" behavior that is not a reliable property of streamed generative output. Option D includes useful ideas (load shedding) but relies on static caps and does not provide as strong adaptive retry control as circuit breaking.
Therefore, Option B is the most correct and operationally safe strategy for peak-load Bedrock streaming workloads.
NEW QUESTION # 57
A company is using Amazon Bedrock to build a GenAI assistant that answers employee questions based on internal documentation. The company stores documents in Amazon S3, Atlassian Confluence, and an internal wiki system. The GenAI assistant must retrieve relevant content and provide grounded responses.
The solution must meet the following requirements:
* Integrate multiple document sources into a single retrieval layer.
* Support semantic search rather than keyword-only queries.
* Minimize custom ingestion and synchronization logic.
* Ensure that retrieved content can be directly used to augment the GenAI assistant ' s foundation model (FM).
Which solution will meet these requirements?
Answer: B
Explanation:
Amazon Bedrock Knowledge Bases is designed specifically for managed retrieval-augmented generation. A knowledge base connects source repositories to an embedding model and vector store. During ingestion, source content is transformed into numerical vector embeddings. At retrieval time, the query is similarly represented so the knowledge base can compare semantic similarity and return passages that are conceptually relevant rather than depending exclusively on exact keyword matches.
Amazon Bedrock supports managed data-source connectors for multiple repositories. The AWS documentation lists Amazon S3, Confluence, Microsoft SharePoint, Salesforce, web crawling, and custom data sources among supported connection patterns. These connectors reduce the amount of ingestion, crawling, and synchronization code an organization needs to maintain.
For Atlassian Confluence specifically, Bedrock can crawl supported Confluence content and supports incremental synchronization for added, modified, or deleted material. Current managed Confluence integration also supports crawling pages, blog posts, and attachments, subject to the documented connector limitations.
This architecture supplies the retrieval layer needed to augment an FM with source-grounded context. It centralizes retrieval semantics while letting the application use Bedrock ' s managed ingestion and retrieval capabilities instead of implementing its own embedding pipelines.
B explicitly uses keyword-oriented mappings and therefore does not meet the semantic-search requirement. C could technically implement RAG, but Lambda-based embedding generation, synchronization, and custom retrieval logic create precisely the operational burden the company wants to avoid. D is not an appropriate semantic RAG architecture because ordinary DynamoDB queries do not automatically produce vector-based semantic retrieval from unstructured documentation.
For an internal wiki not covered by a built-in connector, the knowledge-base custom data-source capability can be used while keeping the overall retrieval interface centralized. Therefore, A provides the closest fit to every stated architectural requirement.
NEW QUESTION # 58
A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Option A best satisfies the requirement to capture multi-hop, highly interconnected relationships with minimal operational overhead. Traditional vector similarity search excels at finding semantically similar text but is not optimized for reasoning over explicit entity-to-entity relationships, especially when analysts need indirect, multi-hop connections (for example, fund # holding # issuer # sector # regulation). Graph-based retrieval is designed specifically for these kinds of relationship traversals.
GraphRAG combines retrieval-augmented generation with graph-aware context selection. By representing entities and their relationships in a graph store, the system can traverse multiple hops to assemble a holistic set of relevant facts. This improves completeness and reduces the chance that the model misses indirect relationships that are essential for accurate investment guidance.
Amazon Neptune Analytics provides a managed graph analytics environment capable of efficiently traversing and analyzing complex relationship networks. When integrated with Amazon Bedrock Knowledge Bases, it reduces custom engineering by providing managed ingestion, retrieval, and orchestration patterns suitable for GenAI applications. This lowers operational overhead compared to building and maintaining custom multi- stage retrieval logic.
Meeting the sub-3-second requirement is also more feasible with a graph-optimized engine because multi-hop traversals can be executed efficiently compared to chaining multiple vector searches and joining results in an application layer. The managed nature of Knowledge Bases and Neptune Analytics reduces maintenance, scaling, and operational burden while enabling strong performance.
Option B and C require extensive custom logic and orchestration, increasing complexity and latency. Option D is not designed for graph-style multi-hop exploration and would require significant custom indexing and retrieval logic.
Therefore, Option A is the most AWS-aligned and operationally efficient approach for multi-hop relationship- aware RAG with strong performance.
NEW QUESTION # 59
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