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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Multimodal Data	15%	- Handling and integrating text, image, and audio data - Applications and use cases
Software Development & Engineering	15%	- Python libraries for multimodal AI - Integration and deployment of multimodal AI systems
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
Performance Optimization	10%	- Monitoring and improving system efficiency - Techniques for optimizing AI performance
Data Analysis & Visualization	10%	- Visualization techniques for multimodal data - Data preprocessing and feature engineering
Experimentation	25%	- Hypothesis testing - Experimental design - A/B testing - Model evaluation and comparison

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NVIDIA Generative AI Multimodal Sample Questions (Q25-Q30):

NEW QUESTION # 25
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?

A. Training seperate models for each avalible modality.
B. Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
C. Imputing missing values using simple methods like mean imputation or filling with a constant value.
D. Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.
E. Removing all patients with missing data to create a clean dataset.

Answer: B,D

Explanation:
Removing patients with missing data can lead to a significant loss of information and bias the model. Simple imputation methods can introduce inaccuracies and fail to capture the relationships between modalities. Multimodal variational autoencoders (MVAEs) are specifically designed to handle missing data in multimodal datasets by learning a joint latent representation and imputing values based on the observed modalities. This approach is more robust and accurate than simple imputation methods. GAN can also be used to impute missing values.

NEW QUESTION # 26
Which of the following techniques can be used to improve the factual accuracy of text generated by a large language model?

A. Using retrieval-augmented generation (RAG) to ground the model's knowledge in external sources.
B. Fine-tuning the model on a dataset of factually correct information.
C. Increasing the model size and training it on more data.
D. Always using the same prompt, regardless of the desired output.
E. Applying a temperature of 0 during text generation.

Answer: A,B,C

Explanation:
Increasing model size and training data can improve factual accuracy, but it's not a guaranteed solution. Fine-tuning on factually correct data directly teaches the model to generate accurate information. RAG allows the model to access external knowledge sources and incorporate them into the generated text, which significantly improves factual accuracy. A temperature of 0 makes the model more deterministic but doesn't guarantee accuracy. Varying prompts is important for exploring the model's capabilities, but it doesn't directly address factual accuracy.

NEW QUESTION # 27
Hyperparameter tuning is used for what purpose in machine learning experimentation?

A. Adjusting the weights and biases of a neural network to optimize its performance.
B. Selecting the best ML algorithm for a given task.
C. Collecting and preprocessing data to improve the accuracy of the model.
D. Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.

Answer: D

Explanation:
Hyperparameters are configuration values set *before* training begins and are not updated by the optimization process itself - learning rate, batch size, number of layers, regularization strength, and number of training epochs are canonical examples. Hyperparameter tuning is the systematic search for the combination of these values that yields the best model performance on a validation set, using strategies such as grid search, random search, or more sample-efficient approaches like Bayesian optimization and population-based training.
This is explicitly distinct from option A, which describes the *training* process itself - weights and biases are trainable parameters, updated automatically via backpropagation and gradient descent, not selected through hyperparameter search. Option B describes algorithm selection, a higher-level modeling decision that may precede hyperparameter tuning but is not what tuning itself accomplishes (you tune hyperparameters
*within* a chosen algorithm/architecture). Option C describes data engineering work that happens upstream of model training entirely, unrelated to parameter search.
In practice, hyperparameter tuning requires careful experimental design to avoid overfitting to the validation set - techniques like k-fold cross-validation, held-out test sets, and tracking tools (e.g., experiment trackers logging each trial's configuration and resulting metric) are standard practice, connecting this topic directly to the Experimentation domain's broader emphasis on rigorous, reproducible model evaluation.
Reference: Experimentation domain - hyperparameter tuning, search strategies, validation methodology.

NEW QUESTION # 28
You are fine-tuning a pre-trained multimodal model for a specific task that involves generating short video clips from text prompts. The pre-trained model was trained on a large dataset of diverse videos and text descriptions. However, you observe that the fine-tuned model tends to generate video clips that are visually appealing but often deviate significantly from the meaning of the text prompts. Which of the following techniques is LEAST likely to improve the semantic consistency between the generated video clips and the text prompts?

A. Freeze the weights of the video encoder during fine-tuning.
B. Adding a contrastive loss to the fine-tuning process.
C. Use a lower learning rate during fine-tuning.
D. Augment the fine-tuning dataset with synthesized video-text pairs generated by another model.
E. Implement a reinforcement learning objective that rewards the model for generating videos that are semantically similar to the text prompts.

Answer: A

Explanation:
Freezing the weights of the video encoder will prevent it from adapting to the specific nuances of the fine-tuning task, potentially hindering the model's ability to generate videos that accurately reflect the meaning of the text prompts. A lower learning rate, reinforcement learning, data augmentation, or contrastive learning are all techniques that can help improve semantic consistency.

NEW QUESTION # 29
A self-driving car uses multimodal data (camera images, LiDAR point clouds, radar data, and GPS information) to navigate. The LiDAR sensor occasionally fails, resulting in missing point cloud dat a. How should the system be designed to handle this sensor failure gracefully and maintain safe navigation?

A. Immediately stop the car until the LiDAR sensor is fixed.
B. Use a sensor fusion technique that prioritizes the available modalities (camera, radar, GPS) and estimates the missing LiDAR data based on these modalities.
C. Employ a Kalman filter to predict the LiDAR point cloud based on the previous sensor readings and the car's motion model.
D. Switch to a pre-programmed route that does not require LiDAR data.
E. Rely solely on the camera images and ignore the missing LiDAR data.

Answer: B,C

Explanation:
Stopping the car or relying solely on a single modality is not a robust solution. Using sensor fusion to prioritize available modalities and estimate missing data allows the system to continue navigating safely. A Kalman filter is a specific technique for estimating the state of a system (in this case, the LiDAR point cloud) based on noisy sensor readings and a motion model.

NEW QUESTION # 30
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