NCP-AAI Practice Questions: Agent Development Domain

Correct Answer: C

Explanation: Enabling dynamic batching in Triton Inference Server can significantly optimize performance by grouping multiple inference requests into a single batch, reducing overhead and improving throughput. Simply increasing batch size might not be effective if requests are not naturally aligned, and switching GPUs or reducing concurrent models are more drastic measures that may not address the core issue.

Correct Answer: B

Explanation: The Chain-of-Thought pattern is well-suited for breaking down complex queries into simpler, logical steps. NeMo's language models can be used to process each step in the sequence, ensuring coherent and contextually appropriate responses. Option A (ReAct) is more suited for reactive responses rather than step-by-step reasoning. Option C (Tree-of-Thoughts) is more complex and not necessary for straightforward task decomposition. Option D (LangGraph) does not directly integrate with NeMo's capabilities for this purpose.

Correct Answer: D

Explanation: Latency issues can often be addressed by optimizing the model with TensorRT-LLM, which enhances performance by leveraging NVIDIA's hardware acceleration. Option A is plausible but less likely unless memory errors are present. Option B addresses concurrency, not latency, and Option C would be valid if network diagnostics indicated bandwidth issues.

Correct Answer: A

Explanation: Option A is correct because implementing a monitoring system allows for real-time oversight and the ability to flag and address potential ethical violations. Option B is incorrect as NVIDIA AI Enterprise does not automatically enforce compliance policies; it provides a platform for deployment and management. Option C is incorrect because while NeMo and Triton offer robust features, they do not inherently manage ethical concerns. Option D is incorrect as rule-based systems are limited in their ability to handle complex ethical scenarios.

Correct Answer: B

Explanation: Option B is correct because incompatibility between the cognition module and the new planning module can lead to execution failures. Ensuring compatibility and proper integration is crucial when using frameworks like AutoGen. Option A could cause issues, but outdated libraries would more likely cause errors during the build process. Option C is less likely since knowledge integration issues typically lead to incomplete data access rather than execution failures. Option D affects performance and monitoring rather than execution correctness.

Correct Answer: B

Explanation: Option B is correct because converting models to TensorRT-LLM format leverages NVIDIA GPUs for optimized inference, improving performance without affecting accuracy. Option A might increase throughput but can lead to latency issues if the batch size is too large. Option C compromises model accuracy by reducing complexity. Option D could also reduce accuracy by opting for a less capable model.

Correct Answer: A

Explanation: Option A is correct because implementing a fallback mechanism ensures that unexpected inputs are safely handled by redirecting them to a human, maintaining compliance and safety. Option B involves post-interaction review, which doesn't prevent issues in real-time. Option C is more about response adaptability than safety. Option D limits the agent's capabilities and doesn't effectively address safety concerns.

Correct Answer: C

Explanation: Option C is correct because Triton Inference Server's dynamic batching allows multiple requests to be processed together, improving throughput and efficiency. Option A is incorrect as using a single GPU may not be sufficient for handling high concurrency. Option B is incorrect as model ensemble techniques focus on accuracy rather than concurrency optimization. Option D is incorrect because a queuing system would introduce delays and is not efficient for handling concurrent requests.

Correct Answer: D

Explanation: Option D is correct because TensorRT-LLM optimizes model performance specifically for low-latency inference, which is crucial for handling high throughput efficiently. Option A might increase throughput but could also increase latency. Option B is useful for complex inference tasks but not necessarily for minimizing latency. Option C helps with scalability but does not directly address latency optimization.

Correct Answer: B

Explanation: Option B is correct because the AIQ Toolkit is designed to preprocess and harmonize data from diverse sources, ensuring efficient and accurate knowledge integration. Limiting data sources (Option A) or relying on manual entry (Option C) would not leverage the toolkit's capabilities. A basic script (Option D) lacks the advanced features provided by NVIDIA's tools.