[Sep-2025] Updated NVIDIA NCA-GENL Dumps – PDF & Online Engine [Q24-Q38]

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[Sep-2025] Updated NVIDIA NCA-GENL Dumps – PDF & Online Engine

NCA-GENL.pdf – Questions Answers PDF Sample Questions Reliable

Q24. What is ‘chunking’ in Retrieval-Augmented Generation (RAG)?

Rewrite blocks of text to fill a context window.

A method used in RAG to generate random text.

A concept in RAG that refers to the training of large language models.

A technique used in RAG to split text into meaningful segments.

Q25. Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Translations

Embeddings

Long sequences

Class tokens

Q26. Why do we need positional encoding in transformer-based models?

To represent the order of elements in a sequence.

To prevent overfitting of the model.

To reduce the dimensionality of the input data.

To increase the throughput of the model.

Q27. Which calculation is most commonly used to measure the semantic closeness of two text passages?

Hamming distance

Jaccard similarity

Cosine similarity

Euclidean distance

Q28. Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

Training the model with additional data.

Choosing another model architecture.

Increasing the model’s parameter count.

Leveraging the system message.

Q29. Which technology will allow you to deploy an LLM for production application?

Git

Pandas

Falcon

Triton

Q30. When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?

Automatic parallelization of Python code across CPU cores.

GPU-accelerated columnar data processing with zero-copy memory access.

Integration with cloud-based storage for distributed data access.

Conversion of Pandas DataFrames to SQL tables for faster querying.

Q31. What is a Tokenizer in Large Language Models (LLM)?

A method to remove stop words and punctuation marks from text data.

A machine learning algorithm that predicts the next word/token in a sequence of text.

A tool used to split text into smaller units called tokens for analysis and processing.

A technique used to convert text data into numerical representations called tokens for machine learning.

Q32. In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?

Normalizing all text to a single script using transliteration.

Applying Unicode normalization to standardize character encodings.

Removing all non-Latin characters to simplify the input.

Converting text to phonetic representations for cross-lingual alignment.

Q33. In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

Multi-head attention reduces the model’s memory footprint by sharing weights across heads.

Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.

Multi-head attention eliminates the need for positional encodings in the input sequence.

Multi-head attention simplifies the training process by reducing the number of parameters.

Q34. Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

Achieving the highest possible level of prediction accuracy in AI models.

Implementing complex algorithms to enhance AI’s problem-solving capabilities.

Developing AI systems with autonomy from human decision-making.

Ensuring AI systems have explicable decision-making processes.

Q35. What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.

Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.

Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.

Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.

Q36. In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?

Model size

Accuracy on a validation set

Training duration

Number of layers

Q37. Which Python library is specifically designed for working with large language models (LLMs)?

NumPy

Pandas

HuggingFace Transformers

Scikit-learn

Q38. Which tool would you use to select training data with specific keywords?

ActionScript