[May 12, 2026] Valid NCA-GENM Test Answers & NVIDIA NCA-GENM Exam PDF [Q102-Q124]

Q102. Consider a multimodal dataset consisting of product reviews (text), product images, and customer demographics. You want to build a model that can predict customer satisfaction based on all three modalities. However, you suspect that there might be complex interactions between these modalities that are not easily captured by simple concatenation or averaging. What approach would be most effective for modeling these interactions?

Training separate models for each modality and then averaging their predictions.

Concatenating the feature vectors from each modality and feeding them into a single fully connected layer.

Using a tensor fusion network that explicitly models higher-order interactions between modalities.

Using a gated recurrent unit (GRU) to process the combined data.

Employing transfer learning, using pre-trained models for image and text processing then using the final combined layer for downstream task.

Q103. Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

Using a beam search decoding strategy with a small beam width.

Employing a nucleus sampling (top-p sampling) decoding strategy.

Increasing the model’s training data size.

Utilizing a temperature scaling parameter during decoding and increasing its value.

Using a greedy decoding strategy.

Q104. 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.

Q105. 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.

Q106. You are working with a large multimodal dataset that contains images and corresponding text descriptions. The text descriptions are highly variable in length and content. Which of the following techniques is MOST effective for handling this variability when training a multimodal model?

Pad all text descriptions to the same maximum length using a special padding token.

Truncate all text descriptions to a fixed length.

Use dynamic padding and masking to handle variable-length sequences efficiently during batch processing.

Ignore text descriptions that are longer than a certain threshold.

Create a fixed-size vocabulary and discard any words not in the vocabulary.

Q107. Consider the following scenario: You’re training a GAN for generating high-resolution images (e.g., 1024×1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

Replacing standard convolutional layers with transposed convolutional layers in the generator.

Using Wasserstein GAN (WGAN) with gradient penalty (GP).

Applying batch normalization in both the generator and discriminator.

Increasing the learning rate of both the generator and discriminator.

Using ReLU activation functions in the discriminator.

Q108. You are tasked with optimizing a multimodal model that combines audio and text data for speech recognition. The model currently struggles with noisy audio environments. Which data augmentation technique would be MOST effective in improving the model’s robustness to noise?

Randomly masking parts of the text input.

Adding Gaussian noise to the audio data.

Rotating the images used for visual context.

Translating the text into different languages and back.

Normalizing the text data to lowercase.

Q109. Consider a scenario where you want to use a Transformer model for generating music. Which of the following modifications to the standard Transformer architecture would be most beneficial for capturing the long-range dependencies and musical structure inherent in music?

Reducing the number of attention heads in the multi-head attention mechanism-

Using a smaller embedding dimension for the musical notes-

Implementing relative positional embeddings to encode the relationships between notes based on their relative positions in the sequence.

Removing the feed forward layers after the attention mechanism.

Using a smaller vocabulary of musical notes to reduce computational complexity

Q110. 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

Q111. 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.

Q112. 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.

Q113. You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

Variations in background noise affecting audio quality.

Differences in lighting conditions influencing facial expression recognition.

Subjectivity in emotional expression across cultures and individuals.

Synchronization issues between audio and video streams.

All of the above.

Q114. Which NVIDIA SDK would be most appropriate for building a real-time, interactive avatar that can respond to voice commands and generate realistic facial expressions?

Riva

NeMo

Triton Inference Server

Avatar Cloud Engine (ACE)

RAPIDS

Q115. 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.

Q116. Consider the following Python code snippet used for evaluating a generative model. What potential issue exists with this code, and how would you rectify it to ensure a robust evaluation?

The ‘inception_score’ function is deprecated and should be replaced with a newer metric like FID.

The code does not account for the stochastic nature of generative models and needs to be run multiple times with different random seeds to get a stable estimate of the Inception Score.

The number of generated images Cn_generated_imageS) is too small for a reliable Inception Score calculation and should be increased to at least 50,000.

The Inception Score is not an appropriate metric for evaluating generative models, and other methods should be used instead.

The code is fundamentally correct and does not have any issues.

Q117. During data analysis for a multimodal A1 project involving image and text data, you discover that the image dataset contains a large number of blurry or low-resolution images. The text data, however, is relatively clean and well-structured. What is the BEST approach to mitigate the impact of the noisy image data on the overall model performance?

Discard the blurry and low-resolution images from the dataset to ensure data quality.

Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.

Increase the weight of the text data during model training to compensate for the noisy image data.

Train the model on the noisy image data without any preprocessing or data augmentation.

Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.

Q118. You are tasked with evaluating the performance of a generative model that produces synthetic tabular dat a. This data will be used for downstream tasks such as training a fraud detection model. Which of the following evaluation metrics and strategies are MOST appropriate for assessing the quality and utility of the generated data in this scenario? Select all that apply.

Calculating the Jensen-Shannon Divergence (JSD) between the distributions of individual features in the real and synthetic datasets.

Training a fraud detection model on the synthetic data and evaluating its performance on a held-out set of real data.

Using Principal Component Analysis (PCA) to visualize the real and synthetic datasets in a lower-dimensional space and comparing their distributions.

Measuring the Frechet Inception Distance (FID) between the real and synthetic datasets.

Calculating the Structural Similarity Index (SSIM) between the real and synthetic datasets.

Q119. Which of the following statements are TRUE regarding the challenges of training multimodal machine learning models? (Select TWO)

Multimodal models are generally easier to train than unimodal models due to the increased information available.

Handling missing modality data (e.g., missing image for a text input) requires specialized techniques.

Aligning data from different modalities with varying temporal resolutions (e.g., high-frame-rate video and low-frequency audio) is a significant challenge.

Multimodal models are immune to the problem of overfitting due to the diverse nature of the input data.

All available open-source tools readily support multimodal architectures and loss functions, so there are no software-related challenges.

Q120. You’re building a multimodal model that takes images and text as input. You notice that your model is heavily biased towards the text modality, essentially ignoring the visual input. Which of the following strategies could you employ to address this modality imbalance? (Select TWO)

Use a modality-specific loss function, weighting the loss from the visual modality more heavily.

Remove the text modality entirely.

Implement a gating mechanism that dynamically adjusts the contribution of each modality based on the input.

Increase the learning rate for the text encoder.

Reduce the size of the visual encoder.

Q121. You’re building a multimodal sentiment analysis model using text and audio dat a. You observe that the model’s performance is significantly worse on audio samples from noisy environments. Which of the following data augmentation techniques would be MOST effective for improving the model’s robustness to noisy audio?

Adding random noise to the text data.

Applying time stretching to the audio data.

Mixing different types of noise (e.g., background noise, speech babble) at varying signal-to-noise ratios (SNRs) to the audio data.

Applying pitch shifting to the audio data.

Random Cropping

Q122. You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model’s performance?

Directly feeding the raw clinical notes into the model without any preprocessing.

Applying basic text cleaning (removing punctuation, converting to lowercase) and using a standard word embedding (e.g., Word2Vec).

Utilizing named entity recognition (NER) to identify medical entities (diseases, medications, etc.), and employing a medical-specific language model (e.g., BioBERT) for text embeddings.

Translating the clinical notes into multiple languages and then back-translating to the original language.

Performing sentiment analysis on the clinical notes.

Q123. When deploying a Generative A1 model to a resource-constrained edge device (e.g., a mobile phone), what are the key considerations for model optimization and which techniques are most effective?

Model quantization (e.g., INT8) to reduce model size and memory bandwidth, and model pruning to remove unimportant connections.

Knowledge distillation to transfer knowledge from a large, accurate model to a smaller, faster model suitable for the edge.

Increasing the model’s complexity to improve accuracy on the limited hardware.

Focusing solely on reducing the model’s parameter count without considering its computational complexity.

A and B.

Q124. You are working with a dataset containing text descriptions of products and corresponding product images. You want to train a model that can retrieve the most relevant image for a given text description. Which of the following loss functions is MOST appropriate for this task?

Cross-entropy loss

Mean Squared Error (MSE) loss

Triplet loss

Binary Cross-entropy loss

Huber Loss

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