NCA-GENL Practice Questions: Large Language Models (LLMs) Architecture Domain

Published: June 23, 2025 | 10 min read

Test your NCA-GENL knowledge with 5 practice questions from the Large Language Models (LLMs) Architecture domain. Includes detailed explanations and answers.

NCA-GENL Practice Questions

Master the Large Language Models (LLMs) Architecture Domain

Test your knowledge in the Large Language Models (LLMs) Architecture domain with these 5 practice questions. Each question is designed to help you prepare for the NCA-GENL certification exam with detailed explanations to reinforce your learning.

Question 1

What is a critical consideration when implementing Retrieval-Augmented Generation (RAG) using NVIDIA's AI Enterprise solutions to ensure efficient context window management?

A) Maximize the size of the context window to include as much data as possible.

B) Use vector embeddings to prioritize relevant information retrieval.

C) Focus on increasing the number of retrieval queries per request.

D) Minimize the context window size to reduce computational load.

Show Answer & Explanation

Correct Answer: B

Explanation: Using vector embeddings to prioritize relevant information retrieval is crucial in RAG implementations to ensure that only the most pertinent data is included within the context window. This approach optimizes the use of limited context space, improving the model's performance without unnecessarily increasing computational demands. Maximizing the context window size without regard to relevance can lead to inefficient processing and potential information overload. Increasing the number of queries might increase computational load and latency, while minimizing context size could lead to loss of important information.

Question 2

In the context of training large language models using NVIDIA NeMo, which technique is employed to efficiently handle very large datasets without exceeding memory limits?

A) Gradient Accumulation

B) Layer Normalization

C) Multi-head Attention

D) Positional Encoding

Show Answer & Explanation

Correct Answer: A

Explanation: Gradient accumulation is a technique used during training to handle large datasets by accumulating gradients over multiple mini-batches before performing a weight update. This allows the model to effectively simulate a larger batch size without requiring a proportional increase in memory. Layer normalization, multi-head attention, and positional encoding are architectural components of transformer models rather than techniques for handling large datasets.

Question 3

When deploying a large language model using NVIDIA Triton Inference Server, which strategy is most effective for minimizing latency?

A) Using high batch sizes to process more requests simultaneously.

B) Utilizing dynamic batching to adjust batch sizes based on incoming requests.

C) Deploying the model on a single GPU to ensure consistent performance.

D) Reducing the model size by pruning layers.

Show Answer & Explanation

Correct Answer: B

Explanation: Dynamic batching in Triton Inference Server allows the system to adjust the batch size in real-time based on the incoming request load, which helps in minimizing latency while maintaining throughput. Option A might increase latency due to larger computation times. Option C limits scalability, and option D might affect model accuracy.

Question 4

What is the primary advantage of using mixed precision training when training LLMs with NVIDIA AI Enterprise?

A) Increased model accuracy

B) Reduced training time and memory usage

C) Simplified model architecture

D) Improved model interpretability

Show Answer & Explanation

Correct Answer: B

Explanation: Mixed precision training uses both 16-bit and 32-bit floating-point numbers to reduce memory usage and increase computational efficiency, leading to faster training times without sacrificing model accuracy. This is particularly beneficial when training large models on NVIDIA GPUs. Options A, C, and D are not directly related to the benefits of mixed precision training.

Question 5

In the context of deploying LLMs, what is a primary concern when using prompt injection prevention techniques?

A) Maintaining high throughput

B) Ensuring model interpretability

C) Preventing malicious or unintended inputs

D) Maximizing model accuracy

Show Answer & Explanation

Correct Answer: C

Explanation: Prompt injection prevention focuses on safeguarding models from malicious or unintended inputs that could lead to undesired behavior or outputs. This is crucial for maintaining the integrity and reliability of AI systems. Options A, B, and D, while important, are not the primary focus of prompt injection prevention techniques.

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About NCA-GENL Certification

The NCA-GENL certification validates your expertise in large language models (llms) architecture and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.