[Oct-2025] Pass NVIDIA NCA-GENL Exam in First Attempt Guaranteed! [Q10-Q26]

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[Oct-2025] Pass NVIDIA NCA-GENL Exam in First Attempt Guaranteed!

Full NCA-GENL Practice Test and 53 unique questions with explanations waiting just for you, get it now!

Q10. 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.

Q11. Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

Ensuring that CPUs are running at full clock speed.

Subsampling datasets to provide rapid but approximate answers.

Using the GPU for parallel processing of data.

Enabling data processing to scale to multiple GPUs.

Providing more memory for data analysis.

Q12. What is the purpose of few-shot learning in prompt engineering?

To give a model some examples

To train a model from scratch

To optimize hyperparameters

To fine-tune a model on a massive dataset

Q13. Which metric is commonly used to evaluate machine-translation models?

F1 Score

BLEU score

ROUGE score

Perplexity

Q14. When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?

Chi-squared test

Paired t-test

Mann-Whitney U test

ANOVA test

Q15. In the context of a natural language processing (NLP) application, which approach is most effectivefor implementing zero-shot learning to classify text data into categories that were not seen during training?

Use rule-based systems to manually define the characteristics of each category.

Use a large, labeled dataset for each possible category.

Train the new model from scratch for each new category encountered.

Use a pre-trained language model with semantic embeddings.

Q16. When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?

Increasing the model’s parameter count to improve response quality.

Enabling dynamic batching to process multiple requests simultaneously.

Reducing the input sequence length to minimize token processing.

Switching to a CPU-based inference engine for better scalability.

Q17. Which calculation is most commonly used to measure the semantic closeness of two text passages?

Hamming distance

Jaccard similarity

Cosine similarity

Euclidean distance

Q18. You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is the rationale for using A/B testing with deep learning model performance?

A/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.

A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.

A/B testing ensures that the deep learning model is robust and can handle different variations of input data.

A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.

Q19. Which technology will allow you to deploy an LLM for production application?

Git

Pandas

Falcon

Triton

Q20. In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

Q, K, and V are randomly initialized weight matrices used for positional encoding.

K is responsible for computing the attention scores between the query and key vectors.

Q represents the query vector used to retrieve relevant information from the input sequence.

V is used to calculate the positional embeddings for each token in the input sequence.

Q21. In the context of machine learning model deployment, how can Docker be utilized to enhance the process?

To automatically generate features for machine learning models.

To provide a consistent environment for model training and inference.

To reduce the computational resources needed for training models.

To directly increase the accuracy of machine learning models.

Q22. Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Translations

Embeddings

Long sequences

Class tokens

Q23. What type of model would you use in emotion classification tasks?

Auto-encoder model

Siamese model

Encoder model

SVM model

Q24. What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.

BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.

BLEU scores evaluate the ‘precision’ of translations, while ROUGE scores focus on the ‘recall’ of summarized text.

BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.

Q25. 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)

A/B testing helps validate the impact of changes or updates to deep learning models bystatistically analyzing the outcomes of different versions to make informed decisions for model optimization.

A/B testing allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.

A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.

A/B testing guarantees immediate performance improvements in deep learning models without the need for further analysis or experimentation.

A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.

Q26. 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.

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