2025 AUTHORITATIVE NCA-GENM: NVIDIA GENERATIVE AI MULTIMODAL EXAM SIMULATOR FEE

2025 Authoritative NCA-GENM: NVIDIA Generative AI Multimodal Exam Simulator Fee

2025 Authoritative NCA-GENM: NVIDIA Generative AI Multimodal Exam Simulator Fee

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NVIDIA Generative AI Multimodal Sample Questions (Q135-Q140):

NEW QUESTION # 135
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

  • A. Reducing the size of the input noise vector to the generator.
  • B. Implementing spectral normalization in both the generator and discriminator.
  • C. Using a deeper and wider generator network (e.g., with more layers and channels).
  • D. Using a simple Multi-Layer Perceptron (MLP) as the generator.
  • E. Using a more powerful discriminator architecture (e.g., with attention mechanisms).

Answer: B,C

Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between copyright images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.


NEW QUESTION # 136
You are developing a virtual assistant using NVIDIAACE. You want to ensure that the avatar's facial expressions and lip movements are synchronized with the generated speech in real-time. Which NVIDIA SDKs and ACE components are essential for achieving this?

  • A. Triton Inference Server for deploying all AI models, Riva for voice cloning, and Omniverse for character creation.
  • B. Riva for speech recognition, Triton Inference Server for model deployment, and Omniverse for 3D rendering.
  • C. NeMo for text-to-speech, Audio2Face for generating blendshape weights, and a real-time rendering engine (e.g., Unity or Unreal Engine) to drive the avatar.
  • D. CUDA for GPU acceleration, TensorRT for model optimization, and Audi02Emotion for expression generation.
  • E. Riva for speech synthesis, NeMo for language modeling, and Audi02Face for animation.

Answer: C

Explanation:
Achieving real-time synchronized facial animation requires a text-to-speech engine (NeMo), a system to generate blendshape weights from the audio (Audi02Face), and a rendering engine to display the animated avatar. Riva provides speech recognition, not necessarily synthesis in this case. While Omniverse is useful for 3D rendering (B,D), it isn't strictly required. CUDA and TensorRT (E) are foundational but don't directly address animation.


NEW QUESTION # 137
Consider a scenario where you are developing a multimodal model for medical diagnosis using patient medical history (text), X-ray images, and ECG data (time-series). A significant portion of the ECG data is missing due to sensor malfunction. Which of the following approaches would be MOST effective in handling the missing data and ensuring accurate diagnosis?

  • A. Replace the missing ECG data with the average values from the entire dataset.
  • B. Combine imputation of missing ECG data with a robust multimodal fusion technique.
  • C. Impute the missing ECG values using time-series imputation techniques (e.g., Kalman filtering or interpolation).
  • D. Train a separate model using only the available medical history and X-ray images, ignoring the ECG data altogether.
  • E. Employ a multimodal fusion technique that is robust to missing modalities, such as attention mechanisms that dynamically weight the available data sources.

Answer: B

Explanation:
Combining imputation with robust fusion is optimal. Imputation recovers some information from the missing data, while robust fusion ensures the model can still make accurate predictions even if the imputed data is not perfect. Ignoring the ECG data or simply replacing it with average values would likely lead to inaccurate diagnoses.


NEW QUESTION # 138
You are tasked with building a multimodal generative AI model to create marketing content from product images and descriptions. The image encoder uses a pre-trained ResNet50 model, and the text encoder uses a pre-trained BERT model. After initial training, the generated content frequently misinterprets the image. Which of the following strategies is MOST effective in improving the model's ability to correctly interpret the image within the multimodal context?

  • A. Increase the learning rate for the BERT model to prioritize text-based information.
  • B. Replace ResNet50 with a simpler image encoder like a shallow CNN to reduce computational complexity.
  • C. Freeze the weights of both the ResNet50 and BERT models to prevent overfitting.
  • D. Decrease the batch size during training.
  • E. Fine-tune the ResNet50 model with a dataset of images specifically related to the product domain, using a contrastive loss function that encourages representations of images and corresponding text to be close in the embedding space.

Answer: E

Explanation:
Fine-tuning ResNet50 with a relevant image dataset and a contrastive loss function directly addresses the issue of misinterpreting the image. Freezing weights prevents learning, increasing BERT's learning rate imbalances the model, and a simpler image encoder might lose crucial image details. Decreasing batch size can improve generalization but isn't the primary solution for image misinterpretation.


NEW QUESTION # 139
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?

  • A. Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.
  • B. Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.
  • C. Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
  • D. Normalize all text data to lowercase and remove punctuation.
  • E. Resize all images to a uniform size.

Answer: B,C,D,E

Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.


NEW QUESTION # 140
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