NVIDIA NCA-GENM 100% CORRECT ANSWERS EXAM INSTANT DOWNLOAD | UPDATED NCA-GENM: NVIDIA GENERATIVE AI MULTIMODAL

NVIDIA NCA-GENM 100% Correct Answers Exam Instant Download | Updated NCA-GENM: NVIDIA Generative AI Multimodal

NVIDIA NCA-GENM 100% Correct Answers Exam Instant Download | Updated NCA-GENM: NVIDIA Generative AI Multimodal

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NVIDIA Generative AI Multimodal Sample Questions (Q15-Q20):

NEW QUESTION # 15
You are using NeMo to fine-tune a pre-trained language model for a specific text generation task. You want to implement a custom data augmentation technique to improve the model's robustness. Which of the following approaches is most appropriate for integrating your custom augmentation within the NeMo framework?

  • A. Create a custom *Dataset* class that inherits from 'nemo.core.Dataset' and implements your augmentation within the '_getitem
  • B. Use a separate data processing pipeline outside of NeMo and save the augmented data to disk before training.
  • C. Modify the core NeMo library files to directly incorporate your augmentation logic.
  • D. Augment the data directly within the training loop, applying transformations to each batch before feeding it to the model. method.
  • E. Monkey-patch the existing NeMo data loading functions to inject your augmentation logic.

Answer: A

Explanation:
Creating a custom 'Dataset' class that inherits from 'nemo.core.Dataset' is the recommended and most maintainable way to integrate custom data augmentation in NeMo. This allows you to leverage NeMo's data loading and processing pipelines while seamlessly incorporating your specific augmentation logic within the '_getitem method. Modifying core NeMo files (A) is strongly discouraged. Using a separate pipeline (C) disconnects augmentation from the NeMo workflow. Monkey-patching (D) is brittle. Augmenting within the training loop (E) can be inefficient.


NEW QUESTION # 16
You are tasked with visualizing the performance of a Generative A1 model across different categories of input dat a. You need to show both the accuracy and the number of data points in each category. Which visualization technique would be MOST effective for this purpose?

  • A. A scatter plot showing the relationship between accuracy and sample size for each category.
  • B. A table showing the accuracy and sample size for each category.
  • C. A pie chart showing the accuracy for each category.
  • D. A bar chart showing the accuracy for each category, with error bars indicating the sample size.
  • E. A combination chart (e.g., bar and line) with bars showing the accuracy and a line showing the sample size.

Answer: E

Explanation:
A combination chart effectively displays two different types of data (accuracy and sample size) for each category. It allows for easy comparison and identification of trends. A pie chart is not suitable for showing multiple data points. Error bars don't effectively represent sample size in a standard bar chart. A scatter plot is more appropriate for showing the relationship between two continuous variables. A table lacks the visual impact of a chart.


NEW QUESTION # 17
You're tasked with building a system that generates personalized exercise recommendations based on user's text descriptions of their fitness goals and images of their current physical condition. Due to privacy concerns, you cannot directly access the user's raw images or text after initial processing. What technique can allow you to continue to train the model while respecting these privacy constraints?.

  • A. Data Augmentation
  • B. Federated Learning
  • C. Generative Adversarial Networks (GANs)
  • D. Transfer Learning
  • E. Reinforcement Learning

Answer: B

Explanation:
Federated learning allows training a model across multiple decentralized devices or servers holding local data samples, without exchanging them. This is perfect for privacy-sensitive scenarios as the raw data remains on the user's device. Transfer learning relies on pre- trained models, data augmentation modifies existing data, GANs generate new data (but still require initial data access), and reinforcement learning optimizes actions through interaction with an environment.


NEW QUESTION # 18
You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?

  • A. Using completely unrelated keywords to encourage the model to create something unique.
  • B. Decreasing the 'guidance_scale' to allow for more creative freedom.
  • C. Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
  • D. Specifying 'oil painting' or another artistic style to mask the lack of detail.
  • E. Using a very short and general prompt to allow the model more freedom.

Answer: C

Explanation:
Adding keywords specifically related to image quality ('photorealistic', 'high resolution', '8k', 'detailed') helps guide the model towards generating sharper and more detailed images. 'clip_skip' influences the model to incorporate more details into the output images. Adjusting 'clip_skip' along with quality prompt keywords will enhance the image quality.


NEW QUESTION # 19
You are fine-tuning a pre-trained multimodal model for a visual question answering (VQA) task. You notice that the model performs well on common questions but struggles with questions requiring reasoning about object relationships (e.g., 'Is the object to the left of the table bigger than the one on the table?'). What data augmentation technique would MOST likely improve performance on these challenging questions?

  • A. Adding Gaussian noise to the images.
  • B. Generating synthetic images with varying object arrangements and relationships, paired with corresponding questions and answers.
  • C. Rotating the images.
  • D. Replacing objects in the images with other objects.
  • E. Randomly cropping the images.

Answer: B

Explanation:
Generating synthetic data specifically designed to challenge the model's reasoning abilities about object relationships is the most effective way to improve performance on those types of questions. Cropping, noise, and object replacement might help with robustness, but not with relational reasoning. Rotation won't inherently improve reasoning about relationships.


NEW QUESTION # 20
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