Question 1

You are creating a virtual assistant agent that needs to handle an increasingly wide range of tasks over an extended period. What is the primary benefit of combining external storage (like RAG) with fine-tuning (embodied memory) in this context?

Options :

A : To enhance long-term reasoning capabilities and adaptability

B : To accelerate the agent’s initial response time

C : To ensure the agent doesn’t make any errors

D : To eliminate the need for external knowledge

Answer: A

Question 2

You are designing the architecture for a RAG (Retrieval-Augmented Generation) system, and you are concerned about ensuring data freshness and minimizing latency. Which of the following is the most important consideration when designing the architecture?

Options :

A : Employing a consolidated architecture with a large service handling all data retrieval and LLMinteraction. This ensures consistent performance and simplifies debugging.

B : Using a synchronous, block-level approach, where the LLM continuously monitors the database forupdates and retrieves the entire dataset with each prompt.

C : Implementing a single, centralized database for all data, updated with a synchronous polling mechanismfor the LLM to retrieve the latest information.

D : Use a loosely coupled, event-driven micro-service architecture where separate services handle dataindexing, retrieval, and LLM prompting.

Answer: D

Question 3

You are designing an AI agent for summarizing medical documents that include images and text as well. It must extract key information and recognize dates. Which feature is most critical for ensuring the agent performs well across multiple input and output formats?

Options :

A : Use of guardrails to filter out hallucinated content

B : Retry logic implementation to ensure robustness during API failures

C : Chain-of-thought prompting for reasoning accuracy

D : Multi-modal model integration to handle both text and vision inputs

Answer: D

Question 4

In a global financial firm, an AI Architect is building a multi-agent compliance assistant using an agentic AI framework. The system must manage short-term memory for multi-turn interactions and long-term memory for persistent user and policy context. It should enable contextual recall and adaptation across sessions using NVIDIA’s tool stack. Which architectural approach best supports these requirements?

Options :

A : Leverage NVIDIA NeMo Framework with modular memory management, integrating conversationalstate tracking, knowledge graphs, and vector store retrieval, while using LoRA-tuned models to adaptresponses overtime.

B : Leverage RAPIDS cuDF for memory tracking by streaming multi-turn conversation logs as GPUresident data frames, assuming transactional history can be recalled and reasoned over using dataframeoperations.

C : Rely exclusively on TensorRT to encode all prior knowledge into compiled model weights, allowinginference-only execution with no external memory dependencies across sessions.

D : Leverage NVIDIA Triton Inference Server with dynamic batching to cache session-level inputsbetween inference calls, and use an external Redis store for long-term memory.

Answer: A

Question 5

A health assistant agent has been running on production environment for several weeks. The compliance team wants to audit how personal health data has been processed. Which operational feature supports this requirement?

Options :

A : Adding more prompt examples to clarify privacy rules

B : Masking all output with a profanity and PII detector

C : Increasing model temperature for diverse interpretations

D : Enabling full session logging with audit trail metadata

Answer: D

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