PASS GUARANTEED NVIDIA - PROFESSIONAL NCA-GENM - NVIDIA GENERATIVE AI MULTIMODAL PASS4SURE PASS GUIDE

Pass Guaranteed NVIDIA - Professional NCA-GENM - NVIDIA Generative AI Multimodal Pass4sure Pass Guide

Pass Guaranteed NVIDIA - Professional NCA-GENM - NVIDIA Generative AI Multimodal Pass4sure Pass Guide

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NVIDIA Generative AI Multimodal Sample Questions (Q202-Q207):

NEW QUESTION # 202
You are developing a system to summarize patient medical records, which include doctor's notes (text), lab results (time-series data), and X-ray images. Which of the following techniques would be MOST effective in integrating these diverse data types to generate a coherent and comprehensive summary?

  • A. Convert all data types to text using OCR and other techniques, then train a single text summarization model.
  • B. Ignore the lab results and X-ray images and focus only on summarizing the doctor's notes.
  • C. Use separate summarization models, weigh them by their perceived information content and average.
  • D. Train separate summarization models for each data type and concatenate the resulting summaries.
  • E. Use a multimodal transformer model that can process text, time-series, and image data as input, creating a joint representation for summarization.

Answer: E

Explanation:
A multimodal transformer model is designed to handle different data types as input and learn relationships between them, generating a coherent and comprehensive summary. Other options may lead to loss of information or a disjointed summary.


NEW QUESTION # 203
You're training a multimodal model on text, image, and audio dat
a. During training, you encounter 'CUDA out of memory' errors. Your dataset is large, and you have a GPU with limited memory. Which of the following strategies would be MOST effective to mitigate this issue without significantly reducing model performance?

  • A. Implement gradient accumulation.
  • B. Reduce the batch size.
  • C. Use mixed-precision training (e.g., FP16 or BFI 6).
  • D. Increase the resolution of the input images.
  • E. Decrease the number of layers in the model.

Answer: A,B,C

Explanation:
Reducing the batch size (A) directly decreases memory consumption. Mixed-precision training (B) reduces the memory footprint of the model's weights and activations. Gradient accumulation (D) allows for a larger effective batch size without increasing memory usage per iteration. Decreasing the number of layers (C) can reduce memory usage, but it might also significantly reduce model performance. Increasing image resolution (E) increases memory usage.


NEW QUESTION # 204
Consider a scenario where you're integrating CLIP with a generative model to create images from text prompts. Which of the following best describes the primary role of CLIP in this process?

  • A. To act as a discriminator in a GAN setup.
  • B. To decode generated images back into text descriptions.
  • C. To directly generate images based on text prompts.
  • D. To optimize the hyperparameters of the generative model.
  • E. To encode text prompts into a vector representation that guides the image generation process.

Answer: E

Explanation:
CLIP (Contrastive Language-Image Pre-training) serves as an encoder to map text prompts into a vector space. This vector representation is then used to guide the generative model towards creating images that align with the semantic meaning of the text prompt. CLIP doesn't generate images directly, decode images to text or optimize hyperparameters.


NEW QUESTION # 205
You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

  • A. Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.
  • B. Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
  • C. Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.
  • D. Only utilize pre-trained open-source models
  • E. Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.

Answer: A,C,E

Explanation:
Differential privacy (A) adds noise to the data to protect individual records. On-premises deployment (B) maintains control over data access. Federated learning (D) trains the model on decentralized data without centralizing it. Avoiding client data entirely (C) may limit the model's effectiveness. NVIDIA Merlin and FLARE are tools that provide methods to create safe and private architecture. (E) is not always the best approach since the model might be very generalized and not adapted to specific tasks.


NEW QUESTION # 206
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

  • A. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
  • B. Randomly shuffle the training dataset to minimize bias.
  • C. Filter out all examples containing demographic information from the training dataset.
  • D. Increase the size of the training dataset to dilute the effect of any biased examples.
  • E. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.

Answer: A,E

Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.


NEW QUESTION # 207
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