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NEW QUESTION # 58
A global research company is building a RAG-enabled AI system that uses Amazon Bedrock Knowledge Bases. The company stores documents in Amazon S3 and indexes the documents into an Amazon OpenSearch Serverless vector collection.
When the company evaluates the system, the company identifies three issues. Queries return outdated documents when users request only recent research. Medical research queries return both medical research documents and engineering domain documents. Users can retrieve documents that were authored by researchers who the users should not have access to based on company policies.
The company wants to improve the system so that retrieval becomes more precise and contextually appropriate.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B is correct because the retrieval problem requires filtering before semantic similarity search, not merely returning metadata after retrieval. Amazon Bedrock Knowledge Bases can apply filters to metadata associated with source documents, so timestamp, author, and research-domain attributes can restrict the candidate set before vector results are returned. This prevents old, wrong-domain, or unauthorized-author documents from entering the retrieved context. AWS also documents S3 knowledge-base metadata files and filterable metadata attributes. Option A only returns metadata with already-selected vector results, so irrelevant documents can still be retrieved. Option C introduces DynamoDB and a two-stage custom retrieval path. Option D adds Comprehend and Aurora, increasing operational complexity. The key design principle is to make the required attributes filterable in the vector retrieval layer. AWS Documentation
NEW QUESTION # 59
A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model.
The system must respond in near real time for high-confidence results. The system must process low- confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is the best answer because it implements a decoupled, queue-based moderation pipeline that invokes the expensive model only when the low-latency model is not confident enough. Amazon SQS is designed to decouple distributed application components and support asynchronous processing. AWS documentation describes SQS as a fully managed message queuing service that enables decoupling and scaling of microservices, distributed systems, and serverless applications. This matches the requirement for high resiliency and sudden demand spikes because incoming requests can be buffered in a durable queue rather than overwhelming the model-processing layer.
Using AWS Fargate to process queue messages is also appropriate because Fargate provides serverless container compute for Amazon ECS or Amazon EKS workloads. It allows the company to run scalable processing workers without managing EC2 capacity directly. AWS Prescriptive Guidance includes architectures that use API Gateway, Amazon SQS, and AWS Fargate to process events asynchronously, which supports the same decoupled processing model required in this question.
The two-stage queue design also optimizes cost. The small, low-latency classifier is used first for all requests.
Only requests with confidence below 0.65 are placed into the second queue and processed by the larger model. This avoids running the larger model for every moderation request. High-confidence results can be completed quickly by the first-stage processor, while uncertain results are isolated into an asynchronous second-stage workflow.
Option A is incorrect because it synchronously calls the larger model for low-confidence results, which violates the requirement to process low-confidence requests asynchronously. Option B is incorrect because it invokes both models for every request, increasing cost and eliminating the benefit of confidence-based escalation. Option D requires managing EC2 instances and uses keyword heuristics instead of model confidence, so it is less resilient and less aligned with Bedrock-based moderation. Therefore, option C is correct.
NEW QUESTION # 60
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians.
The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 61
A company uses Amazon Bedrock to deploy an application that generates technical documentation for users across multiple AWS Regions and in multiple languages. Users frequently submit semantically similar questions in different languages, which results in increased inference costs and response latency.
The company needs a caching solution that significantly reduces inference costs, provides low-latency responses globally, maintains cache freshness with a 5-minute TTL, and minimizes custom cache key generation and application-managed caching logic.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is the technically supportable choice under current AWS documentation. Amazon Bedrock prompt caching does not provide semantic caching for differently worded or cross-language questions; it reuses cached prompt content through matching prompt prefixes or defined cache checkpoints. Therefore, Option B ' s assumption that Bedrock prompt caching automatically recognizes semantically equivalent multilingual queries is incorrect, even though many supported models provide a 5-minute prompt-cache TTL. ElastiCache for Redis OSS supports application-controlled cache entries and TTLs, while Global Datastore provides managed cross-Region replication and low-latency geographic reads. A multilingual semantic fingerprint can normalize equivalent requests to a shared cache key before inference. Options A and D similarly lack built-in semantic equivalence handling, while DAX is specifically a DynamoDB caching layer. Thus, C is the viable architecture among the listed choices.
NEW QUESTION # 62
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company ' s data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application ' s decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?
Answer: A
Explanation:
Option C provides the strongest combination of residency controls, data classification, retention protection, and auditability. Keeping source data in Region-specific S3 buckets and processing it according to geographic origin establishes a clear architectural boundary around data belonging to Europe, North America, and Asia.
Amazon S3 Object Lock provides write-once-read-many protection and can prevent protected object versions from being overwritten or deleted, which is particularly valuable for regulated records and audit evidence.
Compliance mode provides especially strong retention enforcement.
Amazon Macie provides the required classification capability. Macie performs automated sensitive-data discovery against Amazon S3 and produces classification results describing whether analyzed objects contain sensitive information. It also calculates sensitivity scores and labels for monitored S3 buckets, supporting governance and compliance workflows.
AWS CloudTrail supplies auditable records of AWS API activity. When CloudTrail log-file integrity validation is enabled, CloudTrail uses SHA-256 hashing and digital signatures so modifications or deletions of delivered logs can be detected. Combined with protected S3 storage, this provides a strong evidentiary audit trail.
AWS now also supports geographic cross-Region inference profiles for Amazon Bedrock that keep inference processing within defined geographic boundaries such as the US, EU, and APAC. However, option A merely states "cross-Region inference" and lacks the required classification service while relying on manual compliance tracking. Geographic residency must be explicitly controlled rather than assumed.
B does not provide a data-classification mechanism. D creates significantly greater administrative burden and depends on custom monitoring and manual reporting. Therefore, C best satisfies the combined residency, classification, audit, and compliance requirements.
NEW QUESTION # 63
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