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NEW QUESTION # 133
A logistics company is using Amazon Bedrock to build an autonomous routing agent that coordinates with APIs that support warehouse, shipping, and international customs operations. The agent must meet the following requirements:
* Break requests into reasoning steps.
* Retry failed tool calls with backoff.
* Stop retrying after three consecutive failures.
* Require human approval for shipments that are valued over $100,000.
* Use MCP to provide access to tools and new integrations without requiring code changes.
Which combination of solutions will meet these requirements? (Select THREE.)
Answer: D,E,F
Explanation:
Amazon Bedrock AgentCore Gateway is specifically designed to expose existing APIs, Lambda functions, services, and MCP servers to agents through a standardized Model Context Protocol interface. AWS documents that Gateway can transform REST APIs represented by OpenAPI specifications into MCP- compatible tools and provide agents with a unified endpoint for tool discovery and invocation. This directly satisfies the requirement to add or expose warehouse, shipping, and customs capabilities through MCP without repeatedly modifying agent integration code.
AWS Step Functions is appropriate for deterministic workflow orchestration. Task states can represent individual reasoning or tool-execution stages, while built-in Retry configuration supports IntervalSeconds , MaxAttempts , and BackoffRate . These settings provide controlled retries with exponential backoff and a defined maximum retry budget rather than requiring bespoke retry code. AWS explicitly documents MaxAttempts as the mechanism that stops retry behavior after the configured limit is reached.
A Choice state supplies deterministic conditional routing. The shipment value can be evaluated and requests above $100,000 can be sent to an approval branch. The approval task can use the Step Functions callback/task- token integration, which pauses workflow execution until an external process-such as a human approval system-returns success or failure. AWS specifically identifies human approval as a callback-task use case.
D is weaker because critical retry and approval policy should not depend solely on probabilistic orchestration prompts. E and G introduce unnecessary custom operational logic. F misuses Guardrails: Bedrock Guardrails governs model input/output safety and policy filtering; it is not a general-purpose human workflow orchestrator for shipment authorization.
NEW QUESTION # 134
A healthcare company is using Amazon Bedrock to build a GenAI application to analyze patient feedback data from CSV files, JSON documents, and text files. The company needs to make the data available for a RAG solution that requires high data quality to prevent hallucinations. The GenAI application will use the data to make accurate clinical recommendations. The application must be highly scalable to handle data in near real time. Data attrition is also high.
Before the company feeds data to a foundation model (FM), the company needs to validate data completeness, detect anomalies, remove personally identifiable information (PII), and monitor quality metrics. The application must be serverless, provide automated rule recommendations, and generate quality scores for regulatory compliance.
Which solution will meet these requirements?
Answer: C
Explanation:
AWS Glue Data Quality is the correct core service because it directly addresses the distinctive requirements that separate this question from ordinary ETL validation. AWS describes Glue Data Quality as a managed, serverless capability for measuring and monitoring dataset quality. It uses Data Quality Definition Language (DQDL) for rules, can automatically analyze datasets and recommend rules, generates a Data Quality score after rule evaluation, supports machine-learning-based anomaly detection, and integrates with Amazon CloudWatch.
Completeness can be evaluated through DQDL rules such as IsComplete . AWS rule recommendations can automatically generate completeness, uniqueness, value-domain, and column-length rules based on observed data. Anomaly detection learns historical statistics and identifies values outside predicted bounds; AWS can additionally recommend future data-quality rules based on detected anomalies.
Glue Data Quality can publish pass/fail metrics into the Glue Data Quality CloudWatch namespace, making the results usable for monitoring, alarms, and regulatory-quality dashboards.
There is one important technical issue in the wording of option B: AWS Glue Data Quality itself does not remove PII. The complete AWS Glue implementation would pair Glue Data Quality with the AWS Glue Detect PII transform. AWS documents that this transform can identify sensitive entities and then mask, redact, hash, or otherwise process identified PII. Therefore, B is clearly the intended and strongest listed answer, but a production implementation should add the Glue PII transform to fully satisfy the question ' s PII-removal requirement.
A requires extensive custom validation. C does not provide the requested automated rule recommendations and managed quality-score capabilities. D handles PII detection but lacks automated DQDL recommendations, ML anomaly detection, and native data-quality scoring.
NEW QUESTION # 135
A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5-100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B best satisfies the requirements because it directly applies Retrieval Augmented Generation principles using managed Amazon Bedrock Knowledge Bases, which are designed to handle large, complex documents while preserving contextual relationships. Financial reports often interleave tables with explanatory narrative, and accurate analysis depends on keeping those elements logically connected. By segmenting documents based on their structural layout-for example, sections, subsections, tables, and surrounding commentary-the knowledge base can retrieve semantically relevant chunks that maintain this relationship during inference.
Amazon Bedrock Knowledge Bases support contextual chunking strategies that go beyond simple fixed-size segmentation. This is critical for financial documents, where a metric in a table may be explained in adjacent paragraphs or footnotes. Context-aware chunking ensures that retrieved content includes both the numeric data and its interpretation, enabling the foundation model to generate accurate, grounded responses. Including citations further improves analyst trust and auditability by allowing users to trace answers back to specific source sections, which is a common requirement in financial environments.
Scalability is another key requirement. Knowledge Bases manage embedding generation, indexing, and retrieval orchestration as a managed service, which allows the solution to scale across large document collections without requiring custom infrastructure or model hosting. This approach also supports efficient updates as new quarterly reports are added, ensuring the retrieval layer remains current.
Option A does not scale well because processing entire 5-100 page documents in a single prompt increases token usage, latency, and cost while risking context truncation. Option C relies on fixed-size chunking triggered at query time, which often breaks semantic relationships in structured financial content. Option D introduces unnecessary architectural complexity by splitting structured and unstructured data into separate applications, increasing operational overhead without providing better contextual retrieval than a unified RAG approach.
NEW QUESTION # 136
A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company's employees. The application must display AI- generated responses character by character as the responses are generated. The application needs to support thousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish.
Which solution will meet these requirements?
Answer: A
Explanation:
This requirement explicitly calls for character-by-character streaming, long-running responses, low latency, and massive concurrency, which aligns directly with Amazon Bedrock streaming inference patterns.
Amazon Bedrock provides the InvokeModelWithResponseStream API specifically for streaming partial model outputs as tokens are generated. This enables near-instant feedback to users instead of waiting for the full response to complete, which is essential when responses last up to 45 seconds.
Amazon API Gateway WebSocket APIs are purpose-built for bidirectional, low-latency, server-initiated communication, allowing the backend to push characters or tokens to clients in real time. This eliminates inefficient polling and supports thousands of concurrent open connections.
AWS Lambda integrates natively with WebSocket APIs and scales automatically with connection volume, enabling a fully managed, serverless architecture. This approach maintains security, centralized authentication, throttling, and observability while avoiding direct client access to Bedrock APIs.
Option B introduces polling latency and unnecessary API overhead and does not provide true streaming.
Option C violates AWS security best practices by exposing Bedrock directly to clients and does not scale securely. Option D only serves completed responses and cannot meet the real-time streaming requirement.
Therefore, Option A is the only solution that fully satisfies streaming behavior, concurrency, latency, and managed-service constraints.
NEW QUESTION # 137
A company is building a video analysis platform on AWS. The platform will analyze a large video archive by using Amazon Rekognition and Amazon Bedrock. The platform must comply with predefined privacy standards. The platform must also use secure model I/O, control foundation model (FM) access patterns, and provide an audit of who accessed what and when.
Which solution will meet these requirements?
Answer: A
Explanation:
Option B is the correct solution because it delivers end-to-end governance, security, and auditability across Amazon Bedrock, Amazon Rekognition, and the underlying data layer while meeting strict privacy and compliance requirements.
Using IAM attribute-based access control (ABAC) allows the company to control access to foundation models and data based on department, role, or workload attributes rather than static permissions. This is critical for controlling FM access patterns at scale. Enforcing specific ModelId and GuardrailIdentifier values with IAM condition keys ensures that only approved models and guardrails are used, which directly supports secure model I/O and governance requirements.
Configuring VPC endpoints for Amazon Bedrock ensures that all model invocations remain on private AWS network paths, reducing data exfiltration risk and supporting privacy standards. AWS CloudTrail captures both management and data events, providing a definitive audit trail of who accessed which resources and when. Sending logs to CloudTrail Lake enables centralized, long-term, queryable auditing across services.
Amazon S3 server access logging adds file-level visibility into video archive access, which is essential for compliance and forensic analysis. Amazon CloudWatch alarms provide near real-time detection of anomalous or unauthorized activity across Amazon Bedrock, Amazon Rekognition, and AWS KMS.
Option A focuses primarily on model-level tracing but lacks comprehensive IAM governance and S3 access auditing. Option C provides partial controls but lacks identity-aware auditing and model governance. Option D focuses on anomaly detection and classification but does not explicitly control FM access patterns.
Therefore, Option B best satisfies all stated requirements in a unified, auditable, and security-first architecture.
NEW QUESTION # 138
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