Score Higher on Verified AIP-C01 | AWS Certified Generative AI Developer - Professional Exam Questions with Answers

Option C is the correct solution because it uses Amazon Bedrock cross-Region inference profiles, which are explicitly designed to support regional data residency, private connectivity, and resilience with minimal operational overhead.

By using a Europe-scoped inference profile, the application ensures that all inference requests are routed only within European Regions where the FM is deployed, such as eu-central-1 and eu-west-3. This satisfies data residency requirements while still providing resilience and load distribution across Regions.

Configuring an Amazon Bedrock VPC endpoint ensures that all traffic remains on the AWS private network. No public endpoints are used, which aligns with the company’s private networking requirements.

Extending existing SCPs to allow inference profile usage ensures that employees can access the FM only in approved Regions, maintaining governance across the Control Tower environment.

Options A and B introduce unnecessary custom routing layers and EC2 management. Option D moves away from Amazon Bedrock entirely and increases operational complexity.

Therefore, Option C is the only solution that satisfies private access, regional confinement, governance controls, and low operational overhead.

Option A best meets the requirements because it applies an AWS-aligned multi-agent pattern that cleanly separates responsibilities: a supervisor agent performs intent classification and orchestration, while specialized collaborator agents handle domain-specific tasks using the right knowledge sources. This structure is well suited for healthcare workflows where clinical questions, scheduling, and insurance processes require different policies, terminology, and data access boundaries.

The requirement for appropriate domain-specific responses is addressed by routing each user query to a department-focused collaborator agent that is grounded with its own department-specific knowledge base . Using Retrieval Augmented Generation with the correct knowledge base improves factual alignment and reduces cross-department leakage (for example, avoiding claims content in a clinical answer). It also supports better prompt grounding and more consistent tone and constraints per department.

The requirement to isolate data maps to using separate knowledge bases per agent and enforcing access through IAM controls , ensuring that each agent can retrieve only from the authorized datasets. This is important for minimizing unintended exposure of sensitive or irrelevant departmental data and supports governance and compliance needs.

For scalability and thousands of parallel interactions , this architecture minimizes contention and bottlenecks. Each collaborator agent can scale independently because requests are distributed across multiple agents and multiple retrieval backends. Operationally, onboarding new features is also simpler: the company can add a new collaborator agent (for example, “billing disputes” or “pharmacy refills”) with its own knowledge base and policies without redesigning the entire assistant.

Option B introduces unnecessary complexity with multiple supervisors and manual handoffs. Option C overloads a single agent with broad instructions and rule-based routing, which increases prompt complexity and reduces maintainability as features grow. Option D creates high operational complexity and risks inconsistent outputs when merging responses from parallel supervisors, and it weakens data isolation by using a shared knowledge base across agents.

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.