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NEW QUESTION # 33
A company has a recommendation system running on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.
The system experiences intermittent issues where some recommendations do not match customer preferences.
The company needs an observability solution to monitor operational metrics and detect patterns of performance degradation compared to established baselines. The solution must generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.
Which solution will meet these requirements?
Answer: A
Explanation:
Option C best satisfies the requirement for rapid, correlated detection of model-related performance degradation. Amazon CloudWatch Application Insights provides automated observability across application components running on Amazon EC2, identifying abnormal behavior patterns without requiring extensive manual configuration.
Using custom metrics for recommendation quality, token usage, and response latency allows the company to directly monitor FM behavior, not just infrastructure health. Applying dimensions such as request type and user segment enables fine-grained correlation between performance issues and specific customer interactions or workloads.
CloudWatch anomaly detection is critical because it establishes dynamic baselines from historical data and detects deviations automatically. This enables alerts to be generated within minutes when FM behavior changes unexpectedly, satisfying the 10-minute alerting requirement without static thresholds that can miss subtle degradations.
CloudWatch Logs Insights complements metrics by enabling rapid analysis of log patterns, error messages, or unusual request flows associated with degraded recommendations. Because all data remains within CloudWatch, correlation between metrics, logs, and alerts is straightforward and operationally efficient.
Option A focuses on infrastructure metrics and lacks behavioral baselining. Option B provides tracing but not automated anomaly detection. Option D adds significant operational overhead and ingestion complexity for a use case already well supported by CloudWatch-native features.
Therefore, Option C delivers the most effective, scalable, and low-overhead observability solution for detecting FM-related performance deviations.
NEW QUESTION # 34
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
Answer: D
Explanation:
Option C best satisfies the requirement to change routing decisions without redeploying code while supporting complex, frequently changing business logic at scale. AWS AppConfig is designed for centrally managing dynamic configuration (feature flags, rules, thresholds, and policy parameters) and deploying changes safely. It supports controlled deployments, validation, and rapid propagation of updated configuration values, which aligns with "real-time cost metrics that change hourly" and the need for "immediate propagation across thousands of concurrent requests." In this design, the Lambda function becomes the policy decision point. For each request, it evaluates user attributes (tier, transaction value), context (regulatory zone, Region), and live cost/performance thresholds stored in AppConfig to determine which Amazon Bedrock FM to invoke. Because the routing rules and FM identifiers are delivered as configuration, the company can switch models, adjust A/B testing weights, or update compliance routing rules by deploying new AppConfig configuration versions rather than pushing new application code. This reduces operational risk and accelerates iteration.
Exposing a single API Gateway endpoint also minimizes client complexity and keeps routing logic server- side, which is important when rules change frequently. Lambda can cache configuration between invocations (within the execution environment) to reduce repeated fetch overhead while still picking up changes quickly, enabling both low latency and rapid rule rollout under high concurrency.
Option A relies on Lambda environment variables, which are not intended for frequent real-time updates and typically require function configuration updates that are slower and operationally brittle. Option B uses mapping templates and stage variables, which are limited for complex rule evaluation and safe rollout patterns. Option D misuses authorizers for business routing, adds extra latency and complexity, and complicates observability and error handling by splitting decisioning from execution.
NEW QUESTION # 35
A medical company uses Amazon Bedrock to power a clinical documentation summarization system. The system produces inconsistent summaries when handling complex clinical documents. The system performed well on simple clinical documents.
The company needs a solution that diagnoses inconsistencies, compares prompt performance against established metrics, and maintains historical records of prompt versions.
Which solution will meet these requirements?
Answer: B
Explanation:
Option B best meets the requirements because it provides systematic diagnosis, measurable comparison, and historical traceability of prompt performance. By placing prompts under version control and testing them against complex clinical documents, the company can consistently reproduce issues, track regressions, and compare prompt behavior using quantifiable metrics such as factual accuracy, completeness, and consistency.
Automated testing ensures scalability and repeatability, while version history preserves prompt evolution over time.
Option A lacks objective metrics and does not address complex documents. Option C focuses on live traffic experimentation but does not inherently diagnose prompt inconsistencies or preserve detailed historical evaluations. Option D adds medical entity analysis but introduces unnecessary service coupling and does not provide robust prompt version history or automated comparative benchmarking. Therefore, Option B is the most complete and disciplined solution.
NEW QUESTION # 36
A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket.
Which solution will meet these requirements?
Answer: B
Explanation:
Option A is the correct answer because Amazon Bedrock Flows is purpose-built to orchestrate generative AI workflows that combine data access, deterministic transformations, and model invocation with minimal operational overhead. The requirements explicitly state that the workflow must retrieve content from Amazon S3, extract a specific portion of a predefined template, send that portion to an Amazon Bedrock model, and store the model's response back into the same S3 bucket. Amazon Bedrock Flows natively supports all of these steps.
By configuring S3 action nodes at the beginning and end of the flow, the workflow can retrieve the original communications and persist the reviewed output without custom code. The expression step allows deterministic parsing of a specific portion of the template, which is essential when only part of the message should be reviewed. This avoids relying on generative logic for parsing, which would be less predictable and harder to audit. The agent step is then used specifically for the review task, where the foundation model evaluates or modifies the extracted content.
Option B uses AWS Step Functions, which can achieve similar outcomes but requires more explicit orchestration logic and does not provide GenAI-native constructs such as expressions and agent steps in a single managed experience. Options C and D rely on Amazon Bedrock agents and AWS Lambda functions to handle parsing and data movement, which increases complexity, operational overhead, and maintenance burden.
Because Amazon Bedrock Flows directly integrates S3 actions, parsing expressions, and model review steps in a single managed workflow, Option A best meets the requirements with the least development and operational effort.
NEW QUESTION # 37
A financial services company wants to use Amazon Bedrock foundation models (FMs) to analyze call center recordings. When calls end, the call center stores recordings as MP3 files in an Amazon S3 bucket. The company needs to generate summaries and sentiment analysis for the recordings in a structured format as soon as new files are created. The recordings average 20 MB in size. Which combination of solutions will meet these requirements? (Select TWO.)
Answer: B,D
Explanation:
AWS Step Functions provides native service integrations that minimize code and operational overhead. For this workflow, Step Functions can directly invoke Amazon Transcribe and, upon completion, directly invoke Amazon Bedrock foundation models. Modern foundation models can be prompted to return outputs in JSON format , fulfilling the requirement for structured analysis without needing an intermediate Lambda function to format the request or response. To trigger the process " as soon as new files are created, " configuring Amazon S3 to send events to Amazon EventBridge is the recommended event-driven pattern. This allows for fine-grained routing and decouples the storage layer from the processing logic, ensuring the workflow scales reliably as call volume grows.
NEW QUESTION # 38
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