ENHANCE YOUR SUCCESS RATE WITH TRAININGQUIZ'S NVIDIA NCA-GENM EXAM QUESTIONS

Enhance Your Success Rate with TrainingQuiz's NVIDIA NCA-GENM Exam Questions

Enhance Your Success Rate with TrainingQuiz's NVIDIA NCA-GENM Exam Questions

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NVIDIA Generative AI Multimodal Sample Questions (Q288-Q293):

NEW QUESTION # 288
You are analyzing a dataset of customer reviews for a Generative A1-powered product. You want to identify the key themes and topics that customers are discussing. Which technique would be MOST appropriate for this task?

  • A. Sentiment analysis to determine the overall positive or negative sentiment.
  • B. Clustering analysis to group customers based on demographics.
  • C. Regression analysis to predict customer satisfaction scores.
  • D. Time series analysis to track review volume over time.
  • E. Topic modeling (e.g., LDA, NMF) to discover underlying themes in the reviews.

Answer: E

Explanation:
Topic modeling is specifically designed to discover the underlying themes and topics within a collection of documents (in this case, customer reviews). LDA (Latent Dirichlet Allocation) and NMF (Non-negative Matrix Factorization) are common topic modeling techniques. Sentiment analysis provides an overall sentiment score but doesn't identify specific themes. Regression analysis is for prediction, not theme discovery. Clustering groups customers, not review topics. Time series analysis tracks changes over time, not themes.


NEW QUESTION # 289
When building a multimodal chatbot that handles both text and voice inputs, what are the primary challenges related to data alignment and synchronization that you need to address?

  • A. Matching the semantic meaning between text and voice inputs when paraphrasing is used.
  • B. All of the above.
  • C. Handling variations in speech rate, accent, and background noise in voice inputs.
  • D. Ensuring that the text and voice encoders use the same vocabulary.
  • E. None of the above.

Answer: B

Explanation:
All the options mentioned are challenges related to data alignment and synchronization. The vocabulary of encoders, handling paraphrasing between text and voice, and dealing with variations in speech are critical aspects of building a robust multimodal chatbot.


NEW QUESTION # 290
You're building a multimodal model that takes images and text as input. You notice that your model is heavily biased towards the text modality, essentially ignoring the visual input. Which of the following strategies could you employ to address this modality imbalance? (Select TWO)

  • A. Increase the learning rate for the text encoder.
  • B. Remove the text modality entirely.
  • C. Implement a gating mechanism that dynamically adjusts the contribution of each modality based on the input.
  • D. Reduce the size of the visual encoder.
  • E. Use a modality-specific loss function, weighting the loss from the visual modality more heavily.

Answer: C,E


NEW QUESTION # 291
You are building a system that uses a Generative A1 model that combines images and natural language prompts to create photorealistic images. The training process is computationally intensive. Which NVIDIA technology is best suited to accelerate the training of this Generative A1 model, especially if it is distributed across multiple GPUs?

  • A. NVIDIA NCCL
  • B. NVIDIA DALI
  • C. NVIDIA TensorRT
  • D. NVIDIA NeMo
  • E. NVIDIA optiX

Answer: A

Explanation:
NVIDIA NCCL (NVIDIA Collective Communications Library) is designed for efficient multi-GPU and multi-node communication. It enables faster distributed training of deep learning models by optimizing data transfers and synchronization between GPUs, thereby accelerating the entire training process. While OptiX is for ray tracing, TensorRT is for inference optimization, NeMo is for conversational A1 model development, and DALI is for data loading and preprocessing, NCCL is specifically designed for accelerating distributed training.


NEW QUESTION # 292
You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a 'cat' as a 'dog'. What is the MOST critical step to address this issue?

  • A. Increase the training data size with more diverse images.
  • B. Apply image sharpening filters to the input images.
  • C. Use a more complex model architecture.
  • D. Implement a mechanism to verify the generated descriptions against an external knowledge base or object recognition system.
  • E. Fine-tune the model using a smaller learning rate.

Answer: D

Explanation:
Factually incorrect descriptions indicate a lack of grounding in real-world knowledge. Verifying against an external knowledge base (B) directly addresses this issue. Increasing data size (A) might help, but it's not guaranteed. Fine-tuning (C) and increasing model complexity (D) might not solve the grounding problem. Image sharpening (E) is irrelevant to factual accuracy.


NEW QUESTION # 293
......

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