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NEW QUESTION # 112
A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system ' s suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?
Answer: A
Explanation:
To achieve the ultra-low latency requirement of less than 500 ms, the system must utilize streaming capabilities at every stage. Using Amazon Transcribe streaming with small (100-ms) audio chunks ensures that transcription begins immediately as the customer speaks. On the model side, Amazon Bedrock's InvokeModelWithResponseStream allows the application to receive tokens as they are generated, rather than waiting for the entire completion, which is critical for real-time interactions. Amazon DynamoDB is the ideal choice for storing supervisor ratings during a live call because it provides the single-digit millisecond latency required for high-frequency writes without impacting the application ' s performance. Options involving batch processing or SQS queuing are unsuitable for sub-500 ms interactive requirements.
NEW QUESTION # 113
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses.
The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes.
Which solution will meet these requirements?
Answer: A
Explanation:
Option B is correct because the workload has predictable short peak periods, strict low-latency requirements, and lower demand between spikes. Amazon SageMaker Serverless Inference is designed for intermittent or unpredictable traffic because SageMaker manages the infrastructure and the customer pays based on usage rather than continuously running idle instances. AWS documentation describes serverless inference as suitable when traffic is intermittent or unpredictable and when users do not want to manage instances or scaling policies.
Provisioned concurrency is the key part of this answer. For latency-sensitive serverless inference workloads, provisioned concurrency keeps the required number of serverless instances initialized and ready to respond.
AWS documentation states that SageMaker Serverless Inference integrates with Application Auto Scaling so provisioned concurrency can be scaled up or down based on a target metric or schedule. This fits a daily 2- hour peak window because the company can configure enough provisioned concurrency for 500 concurrent requests during peak periods and scale down when demand decreases.
Deploying each model to a separate serverless endpoint is also important. The categorization, extraction, and response-generation models likely have different memory, latency, and concurrency needs. Separate endpoints allow independent memory sizing, maximum concurrency configuration, monitoring, and scaling for each task. AWS API documentation also shows that serverless endpoint configuration includes maximum concurrency and provisioned concurrency settings, with provisioned concurrency required to be less than or equal to maximum concurrency.
Option A is less suitable because multi-model endpoints are usually best when many models can share infrastructure, but model loading and shared capacity can add latency risk. Option C assumes Amazon Bedrock provisioned throughput for models, but the company is developing specialized NLP models and needs task-specific deployment controls. Request batching can also add latency. Option D uses asynchronous inference, which is inappropriate for a less-than-500-ms synchronous response requirement. Therefore, option B best satisfies latency, concurrency, accuracy isolation, and resource optimization.
NEW QUESTION # 114
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.
The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.
Which solution will meet these requirements?
Answer: A
Explanation:
Option A is the correct solution because it provides proactive, model-aware token management with fine- grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not natively enforce proactive, model-specific token limits across multiple applications or business units.
By implementing model-specific tokenizers in AWS Lambda, the company can estimate input and output token usage before sending requests to Amazon Bedrock. This enables early detection of requests that are approaching or exceeding model limits and allows the application to block, truncate, or reroute requests proactively rather than reacting to failures.
Publishing token usage metrics to Amazon CloudWatch enables real-time monitoring and alerting at scale, easily supporting more than 5,000 requests per minute. Storing detailed token usage data in Amazon DynamoDB allows the company to attribute usage and costs to specific applications, teams, or business units-an essential requirement for regulatory reporting and internal chargeback.
Option B is incorrect because Amazon Bedrock Guardrails do not currently provide token quota enforcement or proactive token alerts. Option C is reactive and only analyzes failures after they occur. Option D throttles requests but cannot enforce token-based limits or provide per-model cost attribution.
Therefore, Option A best satisfies proactive alerting, scalability, compliance reporting, and cost allocation requirements with acceptable operational effort.
NEW QUESTION # 115
A financial services company processes more than 10,000 customer inquiries every day through a multi-agent GenAI application that uses Amazon Bedrock AgentCore. The application agents invoke several custom tools. During peak usage periods, users report that the custom tools experience up to 40% failure rates. The tools perform inconsistently for different teams at the company.
A GenAI developer must implement an observability solution that provides end-to-end visibility into agent interactions and tool behavior. The solution must use built-in Amazon Bedrock capabilities and must not require custom instrumentation. The GenAI developer needs a solution that requires minimal performance overhead.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon Bedrock AgentCore Observability is the best fit because the requirement is specifically for built-in Amazon Bedrock visibility into agent interactions, tool behavior, and failures without custom instrumentation.
AWS documentation states that AgentCore provides built-in metrics, logs, and traces for AgentCore modular services, and that this data can be viewed in Amazon CloudWatch. AgentCore observability also supports sessions, traces, and spans, where traces represent full request-response cycles and spans represent operations inside a trace. This directly matches the need to analyze multi-agent interactions, tool invocations, latency, throttling, and error behavior during peak traffic.
Option A is also correct because Amazon Bedrock AgentCore integrates with AWS X-Ray for distributed tracing of AI agents and tools. AWS X-Ray support helps follow requests across service boundaries and identify performance bottlenecks in agent and tool execution. CloudWatch provides curated GenAI observability views for AgentCore and can show operational health, metrics, traces, sessions, and error breakdowns. This gives the developer end-to-end visibility with minimal additional overhead because the core telemetry is service-provided rather than extracted through a custom pipeline.
Option B is incorrect because Container Insights focuses on container infrastructure and would not provide native visibility into Bedrock AgentCore agent reasoning paths or tool-level traces. Polling APIs with Lambda adds custom logic and operational overhead. Option C is incorrect because a custom ETL pipeline, DynamoDB storage, and QuickSight reporting do not satisfy the "built-in Amazon Bedrock capabilities" and
"minimal overhead" requirements. Option D starts correctly with AgentCore Observability, but then adds custom Lambda processing and Grafana, which introduces unnecessary custom instrumentation and processing. Therefore, option A is the most AWS-aligned solution.
NEW QUESTION # 116
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 117
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