NCA-GENL

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The dumps for NCA-GENL exam was last updated on Jul 07,2025 .

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Question#1

Which of the following is a key characteristic of Rapid Application Development (RAD)?

A. Iterative prototyping with active user involvement.
B. Extensive upfront planning before any development.
C. Linear progression through predefined project phases.
D. Minimal user feedback during the development process.

Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs. NVIDIA’s documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models.
Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility.
Option C describes a linear waterfall approach, not RAD.
Option D is false, as RAD relies heavily on user feedback.
Reference: NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html

Question#2

When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

A. Zero-shot prompting with a generic task description.
B. Few-shot prompting with randomly selected examples.
C. Chain-of-thought prompting with step-by-step reasoning examples.
D. Retrieval-augmented generation with external mathematical databases.

Explanation:
Chain-of-thought (CoT) prompting is an advanced prompt engineering technique that significantly enhances a large language model’s (LLM) performance on complex reasoning tasks, such as multi-step mathematical problems. By including examples that explicitly demonstrate step-by-step reasoning in the prompt, CoT guides the model to break down the problem into intermediate steps, improving accuracy and robustness. NVIDIA’s NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks requiring logical or sequential reasoning, as it leverages the model’s ability to mimic structured problem-solving. Research by Wei et al. (2022) demonstrates that CoT outperforms other methods for mathematical reasoning.
Option A (zero-shot) is less effective for complex tasks due to lack of guidance.
Option B (few-shot with random examples) is suboptimal without structured reasoning.
Option D (RAG) is useful for factual queries but less relevant for pure reasoning tasks.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."

Question#3

When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

A. ReLU is a linear function while sigmoid is non-linear.
B. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.
C. ReLU and sigmoid both have a range of 0 to 1.
D. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.

Explanation:
ReLU (Rectified Linear Unit) and sigmoid are activation functions used in neural networks. According to NVIDIA’s deep learning documentation (e.g., cuDNN and TensorRT), ReLU, defined as f(x) = max(0, x), is computationally efficient because it involves simple thresholding, avoiding expensive exponential calculations required by sigmoid, f(x) = 1/(1 + e^(-x)). Sigmoid outputs values in the range [0, 1], making it suitable for predicting probabilities in binary classification tasks. ReLU, with an unbounded positive range, is less suited for direct probability prediction but accelerates training by mitigating vanishing gradient issues.
Option A is incorrect, as ReLU is non-linear (piecewise linear).
Option B is false, as ReLU is more efficient and not inherently more accurate.
Option C is wrong, as ReLU’s range is
[0, ∞), not
[0, 1].
Reference: NVIDIA cuDNN Documentation: https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html
Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

Question#4

Which Python library is specifically designed for working with large language models (LLMs)?

A. NumPy
B. Pandas
C. HuggingFace Transformers
D. Scikit-learn

Explanation:
The HuggingFace Transformers library is specifically designed for working with large language models (LLMs), providing tools for model training, fine-tuning, and inference with transformer-based architectures (e.g., BERT, GPT, T5). NVIDIA’s NeMo documentation often references HuggingFace Transformers for NLP tasks, as it supports integration with NVIDIA GPUs and frameworks like PyTorch for optimized performance.
Option A (NumPy) is for numerical computations, not LLMs.
Option B (Pandas) is for data manipulation, not model-specific tasks.
Option D (Scikit-learn) is for traditional machine learning, not transformer-based LLMs.
Reference: NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

Question#5

In the context of transformer-based large language models, how does the use of layer normalization mitigate the challenges associated with training deep neural networks?

A. It reduces the computational complexity by normalizing the input embeddings.
B. It stabilizes training by normalizing the inputs to each layer, reducing internal covariate shift.
C. It increases the model’s capacity by adding additional parameters to each layer.
D. It replaces the attention mechanism to improve sequence processing efficiency.

Explanation:
Layer normalization is a technique used in transformer-based large language models (LLMs) to stabilize and accelerate training by normalizing the inputs to each layer. According to the original transformer paper ("Attention is All You Need," Vaswani et al., 2017) and NVIDIA’s NeMo documentation, layer normalization reduces internal covariate shift by ensuring that the mean and variance of activations remain consistent across layers, mitigating issues like vanishing or exploding gradients in deep networks. This is particularly crucial in transformers, which have many layers and process long sequences, making them prone to training instability. By normalizing the activations (typically after the attention and feed-forward sub-layers), layer normalization improves gradient flow and convergence.
Option A is incorrect, as layer normalization does not reduce computational complexity but adds a small overhead.
Option C is false, as it does not add significant parameters.
Option D is wrong, as layer normalization complements, not replaces, the attention mechanism.
Reference: Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Exam Code: NCA-GENL         Q & A: 51 Q&As         Updated:  Jul 07,2025

 

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Exam Code: NCA-GENL
Q & A: 51 Q&As
Updated:  Jul 07,2025

 

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