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NEW QUESTION # 43
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?
Answer: D
Explanation:
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.
NEW QUESTION # 44
A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture.
The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries.
Which solution will meet these requirements?
Answer: A
Explanation:
Option A is the most appropriate design because it provides scalable multi-agent orchestration, clear domain separation, and strong governance with minimal operational complexity. A supervisor-agent pattern is a standard AWS-recommended approach for multi-agent systems: one agent performs intent classification and routing, while specialized agents handle domain-specific tasks.
Isolating data with separate knowledge bases ensures that each specialized collaborator agent retrieves only the information relevant to its department. This improves response accuracy, reduces hallucinations, and supports privacy controls because clinical content, claims content, and scheduling content can have different access policies. IAM-based filtering ensures that each agent has permission only to the knowledge base it is authorized to use.
Routing patient inquiries through a supervisor agent supports high concurrency and extensibility. New departments or features can be added by introducing new collaborator agents and knowledge bases without redesigning the entire system. Because routing is handled centrally, changes in classification logic do not require updates across many independent supervisors.
Using RAG within each collaborator agent ensures that responses are grounded in department-approved information sources, which is critical in healthcare settings to reduce unsafe or incorrect guidance. This approach also improves performance because each retrieval scope is smaller and more relevant, supporting thousands of parallel interactions.
Option B introduces manual handoffs that do not scale. Option C relies on rule-based routing inside one general agent, which becomes brittle and difficult to govern as complexity grows. Option D mixes all departments into a single knowledge base and merges responses externally, increasing risk of incorrect domain answers and operational overhead.
Therefore, Option A best meets the scalability, correctness, and multi-agent onboarding requirements.
NEW QUESTION # 45
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: C
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 # 46
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in PostgreSQL.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
Option B best satisfies the requirements while minimizing development effort by combining managed semantic search capabilities with fully managed foundation models. AWS Generative AI guidance describes semantic search as a vector-based retrieval pattern where both documents and user queries are embedded into a shared vector space. Similarity search (such as k-nearest neighbors) then retrieves results based on meaning rather than exact keywords.
Amazon OpenSearch Service natively supports vector indexing and k-NN search at scale. This makes it well suited for large datasets such as 20 million restaurants and 200 million reviews while still achieving sub- second latency for the majority of queries. Because OpenSearch is a distributed, managed service, it automatically scales during peak traffic periods and provides cost-effective performance compared with building and tuning custom vector search pipelines on relational databases.
Using Amazon Bedrock to generate embeddings significantly reduces development complexity. AWS manages the foundation models, eliminates the need for custom model hosting, and ensures consistency by using the same FM for both document embeddings and query embeddings. This aligns directly with AWS- recommended semantic search architectures and removes the need for model lifecycle management.
Hourly updates to restaurant data can be handled efficiently through incremental re-indexing in OpenSearch without disrupting query performance. This approach cleanly separates transactional data storage from search workloads, which is a best practice in AWS architectures.
Option A does not meet the semantic search requirement because keyword-based search cannot reliably interpret complex natural language intent. Option C introduces scalability and performance risks by running large-scale vector similarity searches inside PostgreSQL, which increases operational complexity. Option D adds unnecessary ingestion and abstraction layers intended for retrieval-augmented generation, not high- throughput semantic search.
Therefore, Option B provides the optimal balance of performance, scalability, data freshness, and minimal development effort using AWS Generative AI services.
NEW QUESTION # 47
A multinational bank wants to implement a RAG solution on AWS to run queries on internal policy and compliance documents. To comply with data residency regulations, the company must ensure that critical customer data remains within a specific AWS Region. The bank wants to use foundation models (FMs) on AWS to reduce infrastructure costs and minimize model maintenance.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is the appropriate AWS-native approach because it keeps the authoritative documents in an Amazon S3 bucket located in the regulated Region while using managed Amazon Bedrock foundation-model infrastructure instead of maintaining containerized models. Amazon Bedrock supports private connectivity through AWS PrivateLink interface VPC endpoints. Applications inside the VPC can therefore reach Bedrock privately without requiring an internet gateway, NAT gateway, public IP address, or public internet path.
Amazon Bedrock Knowledge Bases can use Amazon S3 as a RAG data source. AWS requires the S3 bucket used by a knowledge base to be in the same Region as the Amazon Bedrock knowledge base. During ingestion, source content is converted into vector embeddings and stored in the configured vector store so similarity-based retrieval can locate relevant passages for augmentation. This creates an AWS-managed RAG architecture while preserving explicit regional placement of the underlying source documents.
Option B introduces AWS Outposts unnecessarily. More importantly, AWS documentation states that S3 on Outposts object data physically remains on the Outpost and "is not in an AWS Region." That conflicts with a requirement written specifically as keeping data within a particular AWS Region and also introduces additional infrastructure and operational cost.
C fails because "a Region close to" the regulated Region does not meet a requirement for the specified Region. Encryption does not override residency requirements. D requires the company to deploy and operate foundation-model containers, directly contradicting the objective of minimizing model infrastructure and maintenance.
Therefore, regional S3 storage combined with privately accessed managed Amazon Bedrock services provides the closest match to the stated residency, RAG, cost, and operational requirements.
NEW QUESTION # 48
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