[Sep-2026] Download Real NVIDIA NCA-GENL Exam Dumps Test Engine Exam Questions [Q54-Q74]

Rate this post

[Sep-2026] Download Real NVIDIA NCA-GENL Exam Dumps Test Engine Exam Questions

New NCA-GENL exam dumps Use Updated NVIDIA Exam

NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	Software development: Covers the programming practices and coding skills required to build, maintain, and deploy generative AI applications.
Topic 2	LLM integration and deployment: Addresses connecting LLMs into real-world applications and deploying them reliably across production environments.
Topic 3	Prompt engineering: Focuses on techniques for designing and refining input prompts to effectively guide LLM outputs toward desired results.
Topic 4	Data analysis and visualization: Covers interpreting datasets and presenting insights through visual tools to support informed model development decisions.
Topic 5	Fundamentals of machine learning and neural networks: Covers the core concepts of how machine learning models learn from data, including the structure and function of neural networks that underpin large language models.
Topic 6	Alignment: Addresses methods for ensuring LLM behavior is safe, accurate, and consistent with human intentions and values.
Topic 7	Data preprocessing and feature engineering: Covers preparing raw data through cleaning, transformation, and feature selection to make it suitable for model training.

Q54. 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.

Q55. In the development of Trustworthy AI, what is the significance of ‘Certification’ as a principle?

It ensures that AI systems are transparent in their decision-making processes.

It requires AI systems to be developed with an ethical consideration for societal impacts.

It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.

It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

Q56. How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?

RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.

RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.

RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.

RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.

Q57. In the evaluation of Natural Language Processing (NLP) systems, what do ‘validity’ and ‘reliability’ imply regarding the selection of evaluation metrics?

Validity involves the metric’s ability to predict future trends in data, and reliability refers to its capacity to integrate with multiple data sources.

Validity ensures the metric accurately reflects the intended property to measure, while reliability ensures consistent results over repeated measurements.

Validity is concerned with the metric’s computational cost, while reliability is about its applicability across different NLP platforms.

Validity refers to the speed of metric computation, whereas reliability pertains to the metric’s performance in high-volume data processing.

Q58. Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Long sequences

Embeddings

Class tokens

Translations

Q59. In large-language models, what is the purpose of the attention mechanism?

To measure the importance of the words in the output sequence.

To determine the order in which words are generated.

To capture the order of the words in the input sequence.

To assign weights to each word in the input sequence.

Q60. When implementing data parallel training, which of the following considerations needs to be taken into account?

The model weights are synced across all processes/devices only at the end of every epoch.

A master-worker method for syncing the weights across different processes is desirable due to its scalability.

A ring all-reduce is an efficient algorithm for syncing the weights across different processes/devices.

The model weights are kept independent for as long as possible increasing the model exploration.

Q61. Which of the following is a key characteristic of Rapid Application Development (RAD)?

Iterative prototyping with active user involvement.

Extensive upfront planning before any development.

Linear progression through predefined project phases.

Minimal user feedback during the development process.

Q62. What is the fundamental role of LangChain in an LLM workflow?

To act as a replacement for traditional programming languages.

To reduce the size of AI foundation models.

To orchestrate LLM components into complex workflows.

To directly manage the hardware resources used by LLMs.

Q63. In the field of AI experimentation, what is the GLUE benchmark used to evaluate performance of?

AI models on speech recognition tasks.

AI models on image recognition tasks.

AI models on a range of natural language understanding tasks.

AI models on reinforcement learning tasks.

Q64. In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?

Decision tree

Support vector machine

Generative adversarial network

K-means clustering

Q65. Which of the following is a feature of the NVIDIA Triton Inference Server?

Model quantization

Dynamic batching

Gradient clipping

Model pruning

Q66. Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

Quantization might help in saving power and reducing heat production.

It consists of removing a quantity of weights whose values are zero.

It leads to a substantial loss of model accuracy.

Helps reduce memory requirements and achieve better cache utilization.

It only involves reducing the number of bits of the parameters.

Q67. 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

Q68. When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

Subword tokenization reduces the model’s computational complexity by eliminating embeddings.

Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.

Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.

Subword tokenization removes punctuation and special characters to simplify text input.

Q69. Which metric is commonly used to evaluate machine-translation models?

F1 Score

BLEU score

ROUGE score

Perplexity

Q70. What is the prompt “Translate English to French: cheese =>” an example of?

Few-shot learning

Fine tuning a model

One-shot learning

Zero-shot learning

Q71. 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.

Q72. 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.

Q73. 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?

Zero-shot prompting with a generic task description.

Few-shot prompting with randomly selected examples.

Chain-of-thought prompting with step-by-step reasoning examples.

Retrieval-augmented generation with external mathematical databases.

Q74. You have access to training data but no access to test data. What evaluation method can you use to assess the performance of your AI model?

Cross-validation

Randomized controlled trial

Average entropy approximation

Greedy decoding