[Oct-2026] NVIDIA NCA-GENM Dumps – Secret To Pass in First Attempt [Q21-Q37]

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[Oct-2026] NVIDIA NCA-GENM Dumps – Secret To Pass in First Attempt

NVIDIA NCA-GENM Exam Dumps [2026] Practice Valid Exam Dumps Question

NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Software Development & Engineering	15%	– Python libraries for multimodal AI – Integration and deployment of multimodal AI systems
Data Analysis & Visualization	10%	– Visualization techniques for multimodal data – Data preprocessing and feature engineering
Performance Optimization	10%	– Techniques for optimizing AI performance – Monitoring and improving system efficiency
Multimodal Data	15%	– Handling and integrating text, image, and audio data – Applications and use cases
Experimentation	25%	– Hypothesis testing – Experimental design – Model evaluation and comparison – A/B testing
Trustworthy AI	5%	– Ensuring fairness and transparency – Ethical considerations in AI development
Core ML & AI Knowledge	20%	– Basic concepts and terminology – Key algorithms and techniques

Q21. You are analyzing a dataset of customer reviews for a new product using Natural Language Processing (NLP). The dataset contains both positive and negative reviews, but a significant portion of the negative reviews uses sarcasm. Which of the following NLP techniques would be MOST effective in accurately identifying the sentiment expressed in sarcastic reviews?

Bag-of-words model with TF-IDF weighting.

Sentiment lexicon-based approach.

Fine-tuning a pre-trained transformer model (e.g., BERT, RoBERTa) on a dataset of sarcastic and non-sarcastic reviews.

Using a rule-based system that identifies keywords associated with positive or negative sentiment.

Calculating the average word embedding for each review.

Q22. You’re developing a text-to-image generation system using a pre-trained CLIP model and a diffusion model. You notice that while the generated images match the overall theme of the text prompt, they often fail to accurately represent specific objects mentioned in the prompt. What are the two MOST effective strategies to improve object fidelity in this scenario?

Fine-tune the diffusion model using a dataset of images specifically depicting the objects that are frequently misrepresented.

Increase the guidance scale during diffusion sampling, forcing the generated images to align more closely with the CLIP embeddings.

Replace the CLIP model with a larger, more powerful text encoder that has been trained on a more diverse dataset.

Implement a technique called ‘Classifier-Free Diffusion Guidance’, which allows for more flexible control over the generated image content.

All of the Above

Q23. What does ‘kernel fusion’ refer to in the context of AI model optimization?

Optimizing model inference by reducing the number of computations by pruning.

Combining multiple kernels into a single kernel for faster computation.

Applying multiple layers of kernels to improve model accuracy.

Using kernel functions to optimize model hyperparameters.

Q24. Consider this Python code snippet using PyTorch:

torch.Size ([32, 5121). The issue is a dimension mismatch.

torch.Size([256, 512]). This implementation is correct and efficient for cross-modal attention.

torch.Size ([32, 32]). This is correct and computes attention weights for each text-image pair in the batch independently

Error. The transpose operation is incorrect for achieving cross-modal attention.

Q25. 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?

Increase the size of the training dataset.

Use a simpler model architecture.

Implement a contrastive learning approach, training the model to distinguish between samples with similar and dissimilar sentiments.

Reduce the learning rate.

Randomly flip the images during training

Q26. Which of the following is a component of the Content Authenticity Initiative?

Content validity

Ethical AI development

Data encryption

Content credential

Q27. You’re building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot’s performance on complex visual scenes?

Use a larger language model for the chatbot.

Integrate an object detection model to identify and localize objects in the image before feeding the information to the chatbot.

Reduce the resolution of the input images.

Train the chatbot on a dataset with only simple images containing a single object.

Remove the image processing component entirely.

Q28. 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.

Q29. You are fine-tuning a large pre-trained language model for a specific downstream task using a limited amount of training dat a. Which of the following techniques is MOST likely to prevent overfitting and improve the model’s generalization performance?

Training the entire model from scratch using the limited training data.

Using a very large learning rate during fine-tuning.

Applying aggressive weight decay and dropout regularization.

Increasing the batch size as much as possible to maximize GPU utilization.

Removing all regularization techniques to allow the model to perfectly fit the training data.

Q30. You are tasked with building a multimodal generative AI model to create marketing content from product images and descriptions. The image encoder uses a pre-trained ResNet50 model, and the text encoder uses a pre-trained BERT model. After initial training, the generated content frequently misinterprets the image. Which of the following strategies is MOST effective in improving the model’s ability to correctly interpret the image within the multimodal context?

Freeze the weights of both the ResNet50 and BERT models to prevent overfitting.

Fine-tune the ResNet50 model with a dataset of images specifically related to the product domain, using a contrastive loss function that encourages representations of images and corresponding text to be close in the embedding space.

Increase the learning rate for the BERT model to prioritize text-based information.

Replace ResNet50 with a simpler image encoder like a shallow CNN to reduce computational complexity.

Decrease the batch size during training.

Q31. You are working on a multimodal emotion recognition system that analyzes video (visual and audio) and transcript (text) dat a. You want to fuse these modalities effectively. Which fusion technique is MOST likely to capture complex inter-modal relationships and improve performance, especially when the modalities have varying degrees of reliability?

Early fusion (concatenating features before feeding into a single model).

Late fusion (averaging the probabilities from separate modality-specific models).

Attention-based fusion (using attention mechanisms to weigh the contributions of each modality dynamically).

Simple concatenation of modality-specific embeddings at a single point in the model.

Feature-level averaging.

Q32. You’re training a multimodal model to generate images from text prompts. The model architecture consists of a text encoder (Transformer) and an image decoder (GAN). After training, you observe that the generated images are highly realistic but often don’t accurately reflect the details specified in the text prompt. What strategy would be MOST effective in improving the alignment between the text prompts and the generated images?

Increase the capacity of the GAN’s generator network.

Use a larger dataset of images for training the GAN.

Introduce a contrastive loss that encourages the image embedding to be close to the text embedding of its corresponding prompt and far from the embeddings of other prompts.

Reduce the learning rate of the text encoder.

Use a simpler text encoder.

Q33. You are building a system to generate captions for images. You want to evaluate how well the generated captions describe the content of the images. Which of the following metrics are most suitable for evaluating the quality of image captions?

Pixel Accuracy.

BLEU (Bilingual Evaluation Understudy).

Inception Score.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation).

F 1-Score

Q34. 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.

Q35. You are building a real-time multimodal system that processes live video and audio streams to detect potentially dangerous situations. Latency is a critical constraint. Which of the following strategies is MOST important to minimize latency in this system?

Using a large batch size during inference to maximize GPU utilization.

Optimizing the model architecture for efficient computation, using techniques like model quantization, knowledge distillation, and reducing the number of layers.

Employing extensive data augmentation during training.

Using a very deep neural network to achieve the highest possible accuracy, regardless of latency.

Converting the video stream to text transcripts before processing.

Q36. Which of the following regularization techniques is MOST effective for preventing overfitting in a multimodal deep learning model with a large number of parameters and complex interactions between different modalities?

L1 regularization

L2 regularization

Dropout

Batch Normalization

Early Stopping

Q37. You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a ‘cat’ as a ‘dog’. What is the MOST critical step to address this issue?

Increase the training data size with more diverse images.

Implement a mechanism to verify the generated descriptions against an external knowledge base or object recognition system.

Fine-tune the model using a smaller learning rate.

Use a more complex model architecture.

Apply image sharpening filters to the input images.

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[Oct-2026] NVIDIA NCA-GENM Dumps – Secret To Pass in First Attempt [Q21-Q37]

NVIDIA NCA-GENM Exam Syllabus Topics:

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