View All NCA-GENM Actual Free Exam Questions Apr 16, 2026 Updated [Q187-Q202]

4/5 - (1 vote)

View All NCA-GENM Actual Free Exam Questions Apr 16, 2026 Updated

Pass Authentic NVIDIA NCA-GENM with Free Practice Tests and Exam Dumps

NO.187 You’re working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?

Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.

Increasing the size of the model’s hidden layers.

Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.

Adding more layers to the model.

Training the model on a larger dataset of music.

NO.188 In the context of multimodal data analysis, which of the following statements accurately describe the challenges associated with data alignment?

Data alignment ensures that data from different modalities refers to the same event or entity.

Data alignment is only necessary when dealing with time-series data.

Misalignment can lead to spurious correlations and reduced model performance.

Perfect data alignment is always achievable with proper preprocessing techniques.

Data alignment is not relevant when using deep learning models.

NO.189 When deploying a large multimodal model to a resource-constrained environment (e.g., an edge device), which optimization techniques are MOST crucial to consider? (Select all that apply)

Model quantization to reduce the model’s memory footprint and computational requirements.

Knowledge distillation to transfer knowledge from a larger, more accurate model to a smaller, faster model.

Increasing the batch size to improve throughput.

Pruning to remove less important connections from the model.

Adding more layers to the model to improve accuracy.

NO.190 You’ve trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model’s factual accuracy and coherence?

Increasing the model size by adding more layers.

Implementing a retrieval-augmented generation (RAG) approach.

Reducing the temperature parameter during generation.

Training the model on a smaller dataset.

Removing dropout layers.

NO.191 Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

To create separate data loaders for images and text.

To concatenate image and text data into a single tensor.

To ensure images and text are processed in the same order during training.

To create a custom dataset class for handling paired image and text data.

To resize all images to the same dimension.

NO.192 Which of the following are potential solutions to mitigate the impact of missing or incomplete data in a multimodal dataset used for training a generative A1 model? (Select all that apply)

Data imputation techniques, such as mean imputation or k-nearest neighbors imputation.

Training the model only on complete data samples, discarding any samples with missing data.

Using a masking strategy during training, where the model learns to predict the missing data based on the available data.

Using a specialized multimodal model designed to handle missing data.

All of the above.

NO.193 Consider a multimodal emotion recognition system that uses both facial expressions and speech audio as input. You want to fuse the information from these two modalities. Which of the following fusion techniques would be most suitable if the modalities have significantly different temporal resolutions (e.g., facial expressions change more rapidly than overall vocal tone)?

Early Fusion (concatenating raw features)

Late Fusion (averaging probabilities from individual classifiers)

Intermediate Fusion (using attention mechanisms to align features)

Decision Fusion (majority voting based on modality predictions)

Feature Extraction (extracting features)

NO.194 Consider a scenario where you are building a multimodal model to generate realistic indoor scenes. You have access to text descriptions of the scene, 3D models of furniture, and ambient sound recordings. Which of the following loss functions would be most appropriate to ensure coherence and realism in the generated scenes?

Mean Squared Error (MSE) between the generated image and a reference image.

Cross-entropy loss for classifying different object categories in the scene.

A combination of adversarial loss (GAN) to ensure realism, a perceptual loss to match high-level features, and a semantic consistency loss to align the generated image with the input text description.

Cosine similarity loss between the generated image and the input 3D models.

KL Divergence loss between the generated sound and the input text.

NO.195 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.

NO.196 You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)

Increase the batch size significantly.

Implement weight decay and dropout regularization.

Use a smaller pre-trained model.

Employ data augmentation techniques specifically designed for video data (e.g., temporal jittering, random cropping).

Reduce the number of transformer layers in the model.

NO.197 You’re working on a multimodal AI system that combines text and image dat a. You’re using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?

Increasing the embedding dimension-

Using a larger batch size during training.

Implementing hard negative mining.

Decreasing the temperature parameter in the contrastive loss.

Using a simpler model architecture-

NO.198 You are developing a text-to-image generative model and want to evaluate the quality and diversity of the generated images. Which metric is MOST appropriate for assessing the diversity of generated images, considering computational efficiency is also important?

Inception Score (IS)

Frechet Inception Distance (FID)

Learned Perceptual Image Patch Similarity (LPIPS)

Multi-Scale Structural Similarity Index (MS-SSIM)

Number of unique images in the generated set.

NO.199 Which of the following techniques are commonly used to address the ‘hallucination’ problem in generative A1 models, where the model generates content that is factually incorrect or nonsensical? (Select all that apply)

Increasing the model size and training dataset.

Using Reinforcement Learning from Human Feedback (RLHF) to align the model’s output with human preferences and factual correctness.

Implementing retrieval-augmented generation (RAG), where the model consults an external knowledge source before generating content.

Employing adversarial training techniques to improve the model’s robustness to noisy input data.

Lowering the temperature parameter during decoding to encourage more conservative and predictable outputs.

NO.200 You’re developing a system that analyzes video footage and generates textual summaries of the events occurring in the video. Which of the following architectures would be the MOST appropriate starting point for this task?

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

A long short-term memory (LSTM) network for time series forecasting.

A transformer-based encoder-decoder architecture, with the encoder processing video frames (e.g., using a 3D CNN) and the decoder generating text.

A generative adversarial network (GAN) for image generation.

A support vector machine (SVM) for classification.

NO.201 Which prompt engineering technique is most likely to improve the coherence and visual quality of images generated by a text-to-image model when generating complex scenes with multiple objects and intricate details?

Using short, concise prompts with only a few keywords.

Employing a negative prompt to specify elements to avoid.

Relying solely on the model’s default style settings.

Using only abstract and ambiguous language.

Exclusively describing the background and neglecting foreground elements.

NO.202 You’re training a Generative Adversarial Network (GAN) to generate realistic images of faces. After several epochs, you notice that the generator is producing very similar faces, lacking diversity. Which of the following techniques could BEST address this mode collapse issue?

Decrease the learning rate of the discriminator.

Increase the batch size used for training the generator

Implement Minibatch Discrimination in the discriminator.

Use a simpler generator architecture.

Reduce the noise injected into the generator’s input.