NVIDIA - Latest Training Dumps

NO.15 You are training a text-to-image diffusion model and observe that the generated images often exhibit a ‘washed-out’ or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?

Decrease the number of diffusion steps used during training.

Increase the weight of the perceptual loss function in the training objective.

Reduce the batch size used during training to minimize memory consumption.

Apply more aggressive data augmentation techniques to the training dataset.

Reduce the learning rate for the U-Net architecture within the diffusion model.

NO.16 Consider the following Python code snippet used for processing image and text data for a multimodal model:

What is the primary limitation of the text encoding method used in this code, and how could it be improved for use in a real-world multimodal model?

The text encoding is efficient but incompatible with common deep learning architectures.

The text encoding only supports ASCII characters and does not account for word embeddings or sequence length variations. Use a tokenizer like BERT or SentencePiece to generate embeddings and pad sequences to a fixed length

The text encoding is overly complex and should be simplified to reduce computational overhead.

The text encoding is suitable for small datasets but will not scale to larger datasets.

It adequately addresses the complexities inherent in natural language, making it suitable for a variety of multimodal models.

NO.17 Consider a scenario where you are building an autoencoder using a U-Net architecture. What loss function is generally considered MOST suitable for training this autoencoder, particularly when the goal is to generate high-quality images?

Cross-entropy loss

Mean Squared Error (MSE) loss

Binary Cross-entropy loss

Structural Similarity Index Measure (SSIM) loss

Hinge Loss

NO.18 You’re using a diffusion model to generate high-resolution images. You notice that the generated images often contain artifacts and inconsistencies. Which of the following techniques could help improve the image quality?

Increasing the number of diffusion steps during training.

Using a smaller image size during training.

Employing classifier-free guidance during sampling.

Training with a larger batch size.

Decreasing the number of diffusion steps during sampling.

NO.19 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)

Knowledge distillation, transferring the knowledge to a smaller model.

Increasing the size of the hidden layers in the transformer architecture.

Using mixed precision training (e.g., FP16) to reduce memory usage and computation.

Adding more data augmentation techniques to the training process.

Implementing model parallelism across multiple GPUs without optimizing communication overhead.

NO.20 You are building a video summarization system that uses both visual (frame content) and audio (speech transcripts) information. You’ve noticed that the system tends to prioritize segments with clear speech but often misses important visual events that are not explicitly mentioned in the audio. How can you improve the system to better incorporate visual cues into the summarization process? (Select all that apply)

Train a separate model to detect important visual events (e.g., action recognition) and incorporate its output into the summarization process.

Increase the weight given to audio features in the summarization model.

Use a multimodal attention mechanism that allows the model to attend to both visual and audio features simultaneously.

Fine-tune the audio transcription model to be more accurate.

위의 모든 것.

NO.21 You are tasked with building a system that generates realistic images based on both textual descriptions and a semantic segmentation map. The segmentation map provides spatial information about the objects present in the scene. Which of the following generative architectures is MOST appropriate for this multimodal task?

Vanilla Generative Adversarial Network (GAN)

Variational Autoencoder (VAE)

Conditional Generative Adversarial Network (cGAN) with both text and segmentation map as conditions.

Autoregressive model like PixelCNN

Diffusion model without conditioning

NO.22 You’re fine-tuning a pre-trained multimodal model for a specific downstream task. You notice that while the model’s performance on the training data is excellent, it performs poorly on unseen dat a. What regularization technique, beyond standard weight decay, is MOST likely to improve the model’s generalization ability in this scenario, and what is its purpose?

Batch Normalization: To accelerate training and reduce internal covariate shift.

Dropout: To randomly deactivate neurons during training, preventing co-adaptation and improving robustness.

Layer Normalization: To normalize activations across features, stabilizing training.

Early Stopping: To halt training when performance on a validation set degrades.

Gradient Clipping: To prevent exploding gradients, stabilizing training.

NO.23 You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)

Increase the learning rate.

Apply weight decay (L2 regularization).

Use dropout regularization.

Decrease the batch size.

Increase the size of the training data.

NO.24 Consider a scenario where you are developing a multimodal A1 system to translate sign language videos into text. The system utilizes a CNN for processing video frames and an RNN for generating the text sequence. During evaluation, you observe that the system struggles to accurately translate signs that involve complex hand movements or subtle facial expressions. What are the MOST effective strategies to improve performance in this specific scenario? (Select TWO)

Reduce the frame rate of the input video to decrease computational load.

Increase the depth of the CNN to capture more fine-grained visual features.

Incorporate a 3D CNN architecture to better capture spatial-temporal information from the video.

Replace the RNN with a simpler feed forward neural network for faster processing.

Augment the training data with variations of the sign language videos, including different camera angles, lighting conditions, and signers.

NO.25 Consider a multimodal emotion recognition system that uses both facial expressions (images) and speech (audio). You want to fuse the information from these two modalities at the decision level. Which of the following techniques would be MOST suitable for decision-level fusion?

Concatenate the feature vectors extracted from the images and audio, then train a single classifier.

Train separate classifiers for images and audio, then average their output probabilities for each emotion class.

Train separate classifiers for images and audio, then use a weighted average of their output probabilities based on the confidence scores of each classifier.

Train separate classifiers for images and audio, then use the output of the image classifier as input to the audio classifier-

Train a single transformer to process both images and audio in sequence.

NO.26 What advantage does multimodal learning have over unimodal learning?

It requires fewer data samples for learning.

It can capture more complex patterns and relationships in data.

It is more reliable than unimodal learning.

It is easier to collect multimodal data than unimodal data.

NO.27 In the development of Trustworthy AI, what is the significance of ‘Certification’ as a principle?

It ensures that AI systems are transparent in their decision-making processes.

It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.

It requires AI systems to be developed with an ethical consideration for societal impacts.

It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

NO.28 Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?

The text data is not in IJTF-8 encoding.

The image files are corrupted or in an unsupported format.

The batch size is too large.

The learning rate is set too high.

The system doesn’t have CUDA drivers installed.

NO.29 You are developing a multimodal system for medical diagnosis using MRI images and patient history text. Your initial model performs poorly on patients with rare conditions. Which of the following data augmentation techniques would be MOST effective in improving the model’s performance on these under-represented cases?

Randomly cropping and rotating MRI images.

Back-translating the patient history text to generate paraphrased versions-

Using a Generative Adversarial Network (GAN) to synthesize new MRI images conditioned on the patient history text, specifically targeting the rare conditions-

Adding Gaussian noise to the pixel values of MRI images.

Replacing named entities in the patient history text with other random entities-

NO.30 You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model’s robustness and accuracy?

Removing all patients with missing data to create a clean dataset.

Imputing missing values using simple methods like mean imputation or filling with a constant value.

Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.

Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.

Training seperate models for each avalible modality.

NO.31 You are working with a multimodal model that combines text and video data for action recognition. The text data consists of descriptions of the actions, and the video data consists of sequences of frames. You want to fuse these modalities at a late fusion stage. Which of the following approaches BEST describes late fusion?

Concatenating the raw pixel values of video frames with the word embeddings of the text descriptions.

Training separate models for text and video data and averaging their predictions.

Training a single model with both text and video data as input and using a shared embedding space.

Training separate models for text and video data and concatenating their learned feature representations before feeding them into a final classifier.

Applying attention mechanisms to weigh different parts of the text and video data before feeding them into a shared model.

NO.32 You are analyzing the performance of a Generative A1 model and notice that it is overfitting to the training dat a. Which techniques can you apply to mitigate overfitting and improve the model’s generalization performance? Select all that apply:

Increase the size of the training dataset.

Decrease the model’s complexity (e.g., reduce the number of layers or parameters).

Add L1 or L2 regularization to the model’s loss function.

Use dropout layers during training.

Increase the learning rate.

NO.33 Consider a multimodal A1 system that generates recipes based on images of ingredients. The system uses attention maps to highlight the relevant ingredients in the image. You observe that the attention maps are often noisy and highlight irrelevant parts of the image, leading to incorrect recipes. Which of the following strategies could BEST improve the quality and interpretability of the attention maps?

Increase the size of the convolutional filters in the image encoder.

Apply L1 regularization to the attention weights to encourage sparsity.

Add more layers to the attention module.

Use a stronger image encoder, such as a larger ResNet or a Vision Transformer.

All of the above can improve the quality and interpretability of the attention maps.

NO.34 You are fine-tuning a pre-trained multimodal model for a new task. You have limited computational resources. Which of the following fine-tuning strategies would be the MOST computationally efficient while still achieving good performance?

Fine-tune all the layers of the model.

Freeze all layers except the classification head and fine-tune only the classification head.

Freeze the lower layers of the model and fine-tune the upper layers and the classification head.

Train a new random model from scratch for the task, which will avoid the need to load the pre-trained model.

Randomize the model to train, if it improves the training rate.

NO.35 Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?

Image segmentation and pixel-wise classification.

Image inpainting and super-resolution tasks.

Encoding high-dimensional text data for multimodal embeddings.

Medical image analysis, such as tumor detection.

Facilitating efficient feature extraction and upsampling for detailed image generation.

NO.36 You are deploying a multimodal generative A1 model using Triton Inference Server. The model takes both image and text inputs. Which of the following approaches is most suitable for handling the preprocessing and postprocessing steps within Triton?

Performing all preprocessing and postprocessing on the client-side before sending the data to Triton and after receiving the results.

Implementing the preprocessing and postprocessing logic within the model itself as part of the neural network architecture.

Using Triton’s ensemble models to chain preprocessing, the core generative model, and postprocessing models together.

Writing custom C++ code to handle preprocessing and postprocessing within Triton’s backend.

Relying solely on Triton’s automatic data type conversion capabilities without implementing any explicit preprocessing or postprocessing.

카테고리 NVIDIA

Best NCA-GENM Exam Dumps for the Preparation of Latest Exam Questions [Q15-Q36]