Free NVIDIA NCA-GENM Exam Questions

Become NVIDIA Certified with updated NCA-GENM exam questions and correct answers

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Total 420 Questions | Updated On: Jun 17, 2026

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Question 1

You are responsible for maintaining a multimodal generative AI system that processes customer service requests by analyzing both text and voice inputs. The system must categorize the requests, generate appropriate responses, and then forward them to the relevant department. However, the system is struggling with the accurate categorization of requests when the voice data contains background noise. What is the best approach to improve the system’s performance in this scenario?

A : Replace the voice recognition module with a newer, more complex one.

B : Implement noise reduction techniques on the voice input before feeding it into the model.

C : Fine-tune the model using a larger multimodal dataset with diverse noise conditions.

D : Increase the amount of text data used for training the model.

Answer: B

Question 2

You are building a multimodal generative model that combines text and visual inputs to create detailed descriptions of images. The model frequently generates descriptions that are textually rich but visually inaccurate. How can you modify the loss function to improve the accuracy of the visual components in the generated descriptions?

A : Use a contrastive loss to maximize the similarity between text and image features

B : Apply an adversarial loss only to the text generation process

C : Add a perceptual loss that compares generated images with reference images

D : Increase the weight of the text loss in the combined loss function

Answer: A

Question 3

You are designing a multimodal AI system that integrates visual data from medical images (e.g., X-rays) with corresponding patient notes to assist in diagnosing conditions. The system often overlooks critical details in the images, resulting in inaccurate diagnoses. How can you modify the system to improve its accuracy by ensuring it pays closer attention to relevant parts of the medical images?

A : Increase the resolution of input images before processing

B : Use a pre-trained image classification model without fine-tuning it on medical images

C : Implement a spatial attention mechanism that highlights critical regions in the medical images

D : Apply data augmentation techniques to increase the diversity of the training data

Answer: C

Question 4

You are tasked with developing a Retrieval-Augmented Generation (RAG) system for a chatbot that helps users troubleshoot technical issues. The chatbot needs to understand queries about a wide range of topics and respond with accurate, contextually relevant information. Which approach would best improve the chatbot's performance in handling diverse queries?

A : Integrating a domain-specific knowledge graph with the RAG system

B : Using a hybrid retrieval model that combines both keyword-based search and semantic search

C : Implementing a multi-turn dialogue management system without retrieval

D : Fine-tuning a pre-trained LLM specifically on a technical support knowledge base

Answer: A

Question 5

Which of the following best describes how a pretrained modern LLM can be leveraged to solve various NLP tasks such as token classification, text classification, summarization, and question-answering?

A : A pretrained LLM can be fine-tuned on specific datasets to perform token classification, text classification, summarization, and question-answering with high accuracy.

B : Pretrained LLMs require manual feature engineering to perform tasks such as token classification and summarization.

C : A pretrained LLM must be used in conjunction with RNNs to achieve effective text classification and summarization.

D : Pretrained LLMs are only useful for text generation and cannot be adapted to tasks like token classification or summarization.

Answer: A

Page: 1 / 84

Total 420 Questions | Updated On: Jun 17, 2026

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