2025 Realistic Verified NCA-GENM exam dumps Q&As – NCA-GENM Free Update [Q161-Q175]

26 Jul, 2025 By admin NCA-GENM, NVIDIA 0 Comments

2025 Realistic Verified NCA-GENM exam dumps Q&As – NCA-GENM Free Update [Q161-Q175]

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2025 Realistic Verified NCA-GENM exam dumps Q&As – NCA-GENM Free Update

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Q161. Assume you have trained a text-to-image diffusion model using a large dataset of landscape photographs. You now want to adapt this model to generate images of photorealistic portraits. Which of the following fine-tuning strategies is most likely to yield the best results with the least amount of training data and time?

Retrain the entire diffusion model from scratch using the portrait dataset.

Fine-tune only the CLIP model with portrait-related text descriptions and corresponding images.

Fine-tune the IJ-Net architecture of the diffusion model while keeping the CLIP model fixed.

Fine-tune both the CLIP model and the U-Net architecture with the portrait dataset, using a smaller learning rate than the initial training.

Only fine tune the final layer of the IJ-Net model with the portrait dataset.

Q162. You are tasked with deploying a generative A1 model trained with NeMo using Triton Inference Server. You want to leverage TensorRT for optimized inference. Which of the following steps is crucial to ensure compatibility and optimal performance?

Export the NeMo model to ONNX format before deploying it to Triton+.

Ensure that the Triton server is running on a CPU-only instance for maximum compatibility.

Bake the Triton server into a Docker container that includes all NeMo dependencies.

Convert the NeMo model to a TorchScript representation for TensorRT optimization.

Directly deploy the NeMo model as a Python backend within Triton without any conversion.

Q163. You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?

Using TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.

Using word embeddings (e.g., Word2Vec, GloVe) for text, pre-trained CNN features (e.g., from ResNet, Inception) for images, and embedding user profiles using a graph embedding technique.

Using bag-of-words for text, histogram of oriented gradients (HOG) for images, and simple numerical features (e.g., number of followers) for user profiles.

Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.

Using a combination of TF-IDF for text, pixel values for images, and numerical features for user profile information. Then apply PCA for dimensionality reduction.

Q164. You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?

Switching to a smaller, less accurate ASR model to reduce computational load.

Enabling batching and concurrency in the Riva server configuration to process multiple requests simultaneously.

Disabling automatic punctuation and capitalization to simplify the ASR process.

Increasing the audio chunk size sent to the Riva server to reduce the number of requests.

Deploying the Riva server on a CPU-only instance to reduce cost.

Q165. You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

Using short, concise prompts to minimize ambiguity.

Using overly verbose and descriptive prompts to maximize detail.

Using prompts consisting only of keywords related to the desired image.

Using random prompts to explore the model’s creative capabilities.

Using negative prompts to explicitly exclude unwanted features or styles.

Q166. You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model’s generalization ability? (Select TWO)

Increasing the learning rate.

Decreasing the amount of training data.

Using weight decay (L2 regularization).

Applying data augmentation techniques (e.g., random synonym replacement, back-translation).

Decreasing batch size.

Q167. You are building a multimodal model to classify news articles using both text and images. The text data is processed using spaCy, and image data is processed using Keras. You’ve noticed that the model is heavily biased towards the text dat a. Which of the following techniques would be MOST effective in addressing this modality imbalance?

Applying TF-IDF to the text data to reduce the impact of common words.

Using data augmentation techniques on the image dataset, such as random rotations and flips.

Implementing modality-specific weighting in the loss function, giving a higher weight to the image loss.

Reducing the dimensionality of the image feature vectors using Principal Component Analysis (PCA).

Normalizing the length of text sequences to a fixed size before feeding into the model.

Q168. You are building a Generative A1 model that generates captions for images. You want to evaluate the quality of the generated captions.
Which evaluation metrics are MOST suitable for this task?

Accuracy and Precision.

BLEU, ROUGE, and CIDEr.

Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).

Fl-score and AUC.

Cosine Similarity and Euclidean Distance.

Q169. You’re training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?

Reducing the number of layers in the Transformer.

Using a smaller embedding dimension.

Incorporating positional encodings and increasing the attention window size.

Removing the self-attention mechanism.

Using only audio as input.

Q170. You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)

BLEU score between the generated recipe and a reference recipe.

Precision and recall of the ingredients mentioned in the generated recipe compared to a ground truth ingredient list.

Human evaluation of the generated recipe’s clarity, coherence, and accuracy.

Inception Score of the input image.

Calculating the cosine similarity between the word embeddings of the generated and reference recipes.

Q171. 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?

To directly generate images based on text prompts.

To encode text prompts into a vector representation that guides the image generation process.

To decode generated images back into text descriptions.

To optimize the hyperparameters of the generative model.

To act as a discriminator in a GAN setup.

Q172. You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?

Increase the batch size used for training.

Use a simpler generator architecture.

Implement a perceptual loss function that compares high-level features of generated and real images.

Decrease the learning rate of the discriminator.

Train the model for fewer epochs.

Q173. You are building a multimodal application that takes an image and a text prompt as input to generate a modified image. The image is of a cat, and the text prompt is ‘wearing a hat’. Which of the following models or techniques would be MOST suitable for achieving this task efficiently and effectively?

A large language model (LLM) fine-tuned solely on text data.

A Vision Transformer (ViT) trained only on image classification tasks.

A diffusion model conditioned on both image and text embeddings, such as Stable Diffusion or DALL-E 2.

A simple convolutional neural network (CNN) for image classification.

A recurrent neural network (RNN) for sequence modeling.

Q174. Which of the following evaluation metrics is MOST appropriate for assessing the performance of a multimodal generative A1 model that generates image captions based on images and audio descriptions?

Perplexity

BLEU (Bilingual Evaluation Understudy)

Inception Score

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Q175. You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model’s performance with audio data?

Increase the learning rate for the audio modality during training.

Reduce the dimensionality of the audio features to simplify the learning task.

Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.

Decrease the weight of the audio modality in the loss function.

Use transfer learning by pre-training the audio component of the model on a large audio dataset.