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  1. Home
  2. NVIDIA Certification
  3. NCA-GENM Exam
  4. NVIDIA.NCA-GENM.v2026-10-09.q63 Dumps
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Question 21

You are training a multimodal model with text and audio inputs. You notice that the audio modality dominates the training process, and the text modality is not contributing significantly to the final performance. Which of the following strategies can you use to address this modality imbalance? (Select TWO)

Correct Answer: A,C
Increasing the learning rate for the text encoder can help the text modality learn more effectively Applying a modality-specific weighting scheme to the loss function allows you to explicitly control the contribution of each modality to the overall loss, giving more weight to the underperforming text modality. Decreasing the batch size for audio data might have a small impact, but it's not a primary strategy for addressing modality imbalance. Removing the audio modality is not a desirable solution, as it eliminates valuable information. Increasing the size of audio dataset will even more dominate_ So, the most effective strategies are increasing the learning rate for text and weighting the loss function.
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Question 22

You have trained a text-to-image diffusion model. During inference, you notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques could you apply to improve the image quality without retraining the model?

Correct Answer: A
Increasing the number of diffusion steps during sampling allows the model to refine the generated image more thoroughly, leading to finer details and reduced blurriness. The guidance scale controls how closely the generated image adheres to the input text prompt; increasing it typically improves adherence but can sometimes reduce diversity. Batch size primarily affects computational efficiency. Reducing the learning rate is relevant during training, not inference. Adding model layers requires retraining.
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Question 23

Which technique is commonly used to speed up AI model training and inference on hardware accelerators?

Correct Answer: A
Quantization reduces the numerical precision used to represent model weights and activations - for example, converting FP32 weights to INT8 - which decreases memory bandwidth requirements and allows hardware accelerators (GPU Tensor Cores, dedicated INT8 inference engines) to execute more operations per cycle, directly speeding up both training (in its mixed-precision form) and, especially, inference. Post-training quantization and quantization-aware training are the two dominant approaches, with the latter simulating quantization effects during training to better preserve accuracy at reduced bit-widths. NVIDIA's TensorRT relies heavily on quantization (alongside layer fusion and kernel auto-tuning) to accelerate deployed inference.
The distractors describe techniques that serve entirely different purposes: data augmentation (B) increases training data diversity to improve generalization, not computational speed - it typically adds preprocessing overhead rather than reducing it. Model enlargement (C) does the opposite of speeding up computation; larger models require more FLOPS and memory, increasing latency. Dropout (D) is a regularization technique applied during training to prevent overfitting by randomly zeroing activations - it has no role in inference- time speed (and is typically disabled at inference) and does not meaningfully accelerate training compute either.
Quantization is frequently paired with pruning and kernel/operator fusion as the three core techniques for hardware-accelerated performance optimization.
Reference: Performance Optimization domain - quantization, TensorRT, inference acceleration techniques.
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Question 24

You are tasked with generating realistic images of human faces using a GAN. However, you notice that the generated images often contain artifacts, such as distorted facial features or unrealistic textures. Which of the following techniques would be most effective in improving the realism and quality of the generated faces?

Correct Answer: C
StyleGAN architecture, with its AdalN and mapping network, is specifically designed to control and manipulate the style attributes of generated images, leading to more realistic and high-quality outputs, particularly for complex structures like human faces. AdalN helps in normalizing feature statistics based on style codes, enabling fine-grained control over the visual appearance.
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Question 25

When training a Variational Autoencoder (VAE) for generating new data points, which of the following objectives does the VAE optimize?

Correct Answer: D
A VAE optimizes all three objectives. It aims to maximize the likelihood of the input data given the latent representation (reconstruction accuracy), minimize the KL divergence to ensure the latent space is well-structured and smooth, and maximize the similarity between the input and reconstructed data to ensure effective encoding and decoding.
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