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NVIDIA NCA-GENL Exam
Generative AI LLMs (Page 3 )

Updated On: 9-Feb-2026
Page 3 of 20
PDF with 95 Questions

Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

  1. Training the model with additional data.
  2. Choosing another model architecture.
  3. Increasing the model's parameter count.
  4. Leveraging the system message.

Answer(s): D

Explanation:

Prompt engineering involves designing inputs to guide large language models (LLMs) to produce desired outputs without modifying the model itself. Leveraging the system message is a key technique, where a predefined instruction or context is provided to the LLM to set the tone, role, or constraints for its responses. NVIDIA's NeMo framework documentation on conversational AI highlights the use of system messages to improve the contextual accuracy of LLMs, especially in dialogue systems or task-specific applications. For instance, a system message like "You are a helpful technical assistant" ensures responses align with the intended role. Options A, B, and C involve model training or architectural changes, which are not part of prompt engineering.


Reference:

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-

guide/docs/en/stable/nlp/intro.html



What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

  1. Increase the clock speed of the CPU.
  2. Using techniques like memory pooling.
  3. Upgrade the GPU to a higher-end model.
  4. Increase the number of CPU cores.

Answer(s): B,C

Explanation:

Limited throughput between CPU and GPU often results from data transfer bottlenecks or inefficient resource utilization. NVIDIA's documentation on optimizing deep learning workflows (e.g., using CUDA and cuDNN) suggests the following:
Option B: Memory pooling techniques, such as pinned memory or unified memory, reduce data transfer overhead by optimizing how data is staged between CPU and GPU. Option C: Upgrading to a higher-end GPU (e.g., NVIDIA A100 or H100) increases computational capacity and memory bandwidth, improving throughput for data-intensive tasks. Option A (increasing CPU clock speed) has limited impact on CPU-GPU data transfer bottlenecks, and Option D (increasing CPU cores) is less effective unless the workload is CPU-bound, which is uncommon in GPU-accelerated deep learning.


Reference:

NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html NVIDIA GPU Product Documentation: https://www.nvidia.com/en-us/data-center/products/



What is 'chunking' in Retrieval-Augmented Generation (RAG)?

  1. Rewrite blocks of text to fill a context window.
  2. A method used in RAG to generate random text.
  3. A concept in RAG that refers to the training of large language models.
  4. A technique used in RAG to split text into meaningful segments.

Answer(s): D

Explanation:

Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM. According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.


Reference:

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user- guide/docs/en/stable/nlp/intro.html
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."



How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)

  1. A/B testing helps validate the impact of changes or updates to deep learning models by statistically analyzing the outcomes of different versions to make informed decisions for model optimization.
  2. A/B testing allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.
  3. A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.
  4. A/B testing guarantees immediate performance improvements in deep learning models without the need for further analysis or experimentation.
  5. A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.

Answer(s): A,B

Explanation:

A/B testing is a controlled experimentation technique used to compare two versions of a system to determine which performs better. In the context of deep learning, NVIDIA's documentation on model optimization and deployment (e.g., Triton Inference Server) highlights its use in evaluating model performance:
Option A: A/B testing validates changes (e.g., model updates or new features) by statistically comparing outcomes (e.g., accuracy or user engagement), enabling data-driven optimization decisions.
Option B: It is used to compare different model configurations or hyperparameters (e.g., learning rates or architectures) to identify the best setup for a specific task. Option C is incorrect because A/B testing focuses on model performance, not dataset selection. Option D is false, as A/B testing does not guarantee immediate improvements; it requires analysis. Option E is wrong, as A/B testing is widely used in deep learning for real-world applications.


Reference:

NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-

inference-server/user-guide/docs/index.html



You are working on developing an application to classify images of animals and need to train a neural model. However, you have a limited amount of labeled dat

  1. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?
  2. Dropout
  3. Random initialization
  4. Transfer learning
  5. Early stopping

Answer(s): C

Explanation:

Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre-trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.


Reference:

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user- guide/docs/en/stable/nlp/model_finetuning.html
NVIDIA Deep Learning AI: https://www.nvidia.com/en-us/deep-learning-ai/






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