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NEW QUESTION # 135
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company ' s approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)
Answer: A,C,D
NEW QUESTION # 136
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: B
NEW QUESTION # 137
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 # 138
Example Corp provides a personalized video generation service that millions of enterprise customers use.
Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is the correct solution because Amazon Q Business is explicitly designed to provide secure, governed access to enterprise data while preserving customer ownership and control. Each customer maintains their own Amazon Q Business index, which ensures that data never leaves the customer's control boundary unless explicitly shared through approved access mechanisms.
By designating Example Corp as a data accessor, customers can allow controlled, auditable access to their indexed content through secure APIs. This model satisfies strict data governance requirements, including data ownership, access transparency, and revocation capability. Customers do not need to expose raw data or deploy infrastructure in Example Corp's environment.
Amazon Q Business provides high semantic accuracy through managed indexing, ranking, and retrieval optimizations. Because real-time access is not required, this approach avoids the complexity and latency challenges of live federated retrieval while still delivering fast query performance suitable for customer experience use cases.
Option B introduces unnecessary operational complexity by requiring real-time MCP servers per customer.
Option C requires customers to manage Amazon Bedrock knowledge bases and enable cross-account access, which increases integration complexity and governance risk. Option D requires shared Amazon Kendra indexes across accounts, which complicates access control and data ownership boundaries.
Therefore, Option A provides the cleanest, lowest-overhead architecture that meets data governance, accuracy, performance, and scalability requirements while minimizing operational burden for both Example Corp and its customers.
NEW QUESTION # 139
A university recently digitized a collection of archival documents, academic journals, and manuscripts. The university stores the digital files in an AWS Lake Formation data lake.
The university hires a GenAI developer to build a solution to allow users to search the digital files by using text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection based on text and metadata that is associated with the journal abstracts. The metadata of the digitized files does not contain keywords. The solution must match similar abstracts to one another based on the similarity of their text. The data lake contains fewer than 1 million files.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option D is the best choice because it delivers true semantic search with the smallest operational footprint by combining a fully managed embedding service with an automatically scaling vector-capable database. The university's requirement is explicitly semantic: the metadata has no keywords, and the system must match abstracts based on similarity of meaning. This is a direct fit for an embeddings-based approach, where each abstract is converted into a vector representation and searched using vector similarity. Amazon Titan Embeddings in Amazon Bedrock provides a managed way to generate these vectors without hosting or maintaining an ML model, eliminating the operational work of model provisioning, patching, scaling, and lifecycle management.
For storage and retrieval, Amazon Aurora PostgreSQL Serverless with the pgvector extension supports vector storage and similarity search while minimizing infrastructure operations. Aurora Serverless reduces capacity planning and scaling tasks because it can automatically adjust to changes in workload, which is valuable for a university search application with variable usage patterns. With fewer than 1 million files, a PostgreSQL-based vector store is commonly operationally simpler than running a dedicated search cluster, while still meeting the requirement to query using both text-derived similarity and associated metadata filters stored alongside the vectors.
Option A can also enable vector search, but operating an OpenSearch domain typically introduces additional concerns such as domain sizing, shard strategy, cluster scaling, and performance tuning for k-NN workloads.
Option C increases operational overhead the most because it requires deploying and operating a sentence- transformer model endpoint in SageMaker AI, including scaling, monitoring, and model management. Option B does not meet the semantic similarity requirement reliably because topic extraction is not equivalent to embedding-based semantic matching, especially when the metadata lacks keywords and the system must compare abstracts by meaning.
Therefore, D best satisfies semantic search needs with the least operational overhead.
NEW QUESTION # 140
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