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NVIDIA NCA-GENM Latest Exam Papers, Exam NCA-GENM Quick Prep

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NVIDIA Generative AI Multimodal Sample Questions (Q263-Q268):

NEW QUESTION # 263
You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?

A. Synthesizing new text descriptions using paraphrasing and back-translation techniques.
B. Using generative adversarial networks (GANs) to generate new furniture images from the existing dataset.
C. Randomly cropping and rotating the existing furniture images.
D. Focusing solely on increasing the size of the text dataset and ignoring image augmentation.
E. Combining A, B, and C.

Answer: E

Explanation:
Combining all techniques provides the best results. Image augmentations like cropping and rotation increase the variance of the image data. GANs create entirely new images, and text augmentation enhances the diversity of the input descriptions. Focusing only on one modality will likely limit the model's performance.

NEW QUESTION # 264
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

A. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.
B. Train a new classification head from scratch on top of the frozen pre-trained model.
C. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.
D. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.
E. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.

Answer: D

Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.

NEW QUESTION # 265
Which of the following techniques are MOST relevant to optimizing the energy efficiency of a large multimodal generative A1 model deployed on NVIDIA GPUs? (Select TWO)

A. Knowledge distillation, transferring the knowledge to a smaller model.
B. Using mixed precision training (e.g., FP16) to reduce memory usage and computation.
C. Increasing the size of the hidden layers in the transformer architecture.
D. Implementing model parallelism across multiple GPUs without optimizing communication overhead.
E. Adding more data augmentation techniques to the training process.

Answer: A,B

Explanation:
Knowledge distillation reduces the model size, directly decreasing energy consumption. Mixed precision training reduces memory bandwidth and computation, leading to significant energy savings. Increasing hidden layer size increases computational cost and energy consumption. Data augmentation can improve accuracy, but not energy efficiency. Poorly implemented model parallelism can increase communication overhead, negating any energy savings.

NEW QUESTION # 266
In a multimodal sentiment analysis task involving text and images, you find that your model performs well on datasets with clear emotional cues in both modalities but struggles on datasets where the sentiment is subtle or requires nuanced understanding. Which of the following techniques would be MOST helpful in improving the model's performance on these more challenging datasets?

A. Use a simpler model architecture.
B. Randomly flip the images during training
C. Implement a contrastive learning approach, training the model to distinguish between samples with similar and dissimilar sentiments.
D. Increase the size of the training dataset.
E. Reduce the learning rate.

Answer: C

Explanation:
Contrastive learning helps the model learn more robust and discriminative representations by explicitly training it to distinguish between samples with similar and dissimilar sentiments. This is particularly useful for nuanced sentiment analysis. Increasing the dataset size or reducing the learning rate may help to a lesser extent. A simpler model architecture would likely worsen the performance.

NEW QUESTION # 267
You're building a real-time voice cloning application using NVIDIA Riv
a. You need to ensure high-quality synthesized speech with minimal latency. Which of the following Riva configurations would provide the BEST trade-off between quality and speed?

A. Using a pre-trained, open-source text-to-speech model and a CPU-based vocoder, optimized for minimal memory footprint.
B. Using a large, high-capacity Tacotron 2 text-to-speech model and a high-resolution WaveGlow vocoder, deployed on a single, low-power GPU.
C. Using only the open source implementation and not NVIDIA Riva to implement a Voice Cloning application
D. Using a smaller, faster FastSpeech text-to-speech model and a parallel WaveGAN vocoder, deployed on a multi-GPU server with TensorRT optimization enabled.
E. Using a large, transformer-based text-to-speech model with aggressive quantization and pruning, deployed on a cloud-based TPIJ instance.

Answer: D

Explanation:
FastSpeech and parallel WaveGAN offer a good balance of speed and quality. Multi-GPU deployment and TensorRT optimization further reduce latency. Tacotron 2 and WaveGlow (A) provide higher quality but are slower. CPU-based vocoders (C) are generally too slow for real-time applications. TPIJs (D) are not directly supported by Riva. (E) does not leverage Riva features. Riva's TensorRT optimizatins would be ideal here. The best option would be a faster TTS model along with a Vocoder for real time synthesis.

NEW QUESTION # 268
......

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