NCA-GENM Exam Questions Vce & NCA-GENM Study Center

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Supervised and unsupervised learning 2. Neural networks fundamentals
Topic 2: Multimodal AI Systems	- Cross-modal learning 1. Text-image integration 2. Audio-visual understanding - Multimodal model design
Topic 3: NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. GPU-accelerated AI workflows 2. NeMo framework usage
Topic 4: Generative AI Concepts	- Generative models 1. Diffusion models 2. Transformers and LLM basics
Topic 5: Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations

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2026 Pass-Sure NCA-GENM: NVIDIA Generative AI Multimodal Exam Questions Vce

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NVIDIA Generative AI Multimodal Sample Questions (Q32-Q37):

NEW QUESTION # 32
Explain the role of Tensor Cores and mixed-precision training (e.g., using FP16 or bfloat16) in accelerating the training of large generative AI models.

A. A and B.
B. Tensor Cores are only useful for inference, not training.
C. Mixed-precision training guarantees the same convergence behavior as full-precision training.
D. Mixed-precision training allows using lower precision for forward and backward passes but keeps weights and gradients in higher precision to maintain stability.
E. Tensor Cores perform specialized matrix multiplications optimized for lower-precision data types, enabling faster computation and reduced memory footprint.

Answer: A

Explanation:
Tensor Cores are designed to accelerate matrix multiplication, the core operation in deep learning, using lower precision data types. Mixed-precision training leverages this by using lower precision for the bulk of the computation, while maintaining higher precision for critical variables to avoid instability. Tensor Cores are used both for training and inference.

NEW QUESTION # 33
Consider a scenario where you are developing a multimodal model for medical diagnosis using patient medical history (text), X-ray images, and ECG data (time-series). A significant portion of the ECG data is missing due to sensor malfunction. Which of the following approaches would be MOST effective in handling the missing data and ensuring accurate diagnosis?

A. Impute the missing ECG values using time-series imputation techniques (e.g., Kalman filtering or interpolation).
B. Employ a multimodal fusion technique that is robust to missing modalities, such as attention mechanisms that dynamically weight the available data sources.
C. Train a separate model using only the available medical history and X-ray images, ignoring the ECG data altogether.
D. Replace the missing ECG data with the average values from the entire dataset.
E. Combine imputation of missing ECG data with a robust multimodal fusion technique.

Answer: E

Explanation:
Combining imputation with robust fusion is optimal. Imputation recovers some information from the missing data, while robust fusion ensures the model can still make accurate predictions even if the imputed data is not perfect. Ignoring the ECG data or simply replacing it with average values would likely lead to inaccurate diagnoses.

NEW QUESTION # 34
You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?

A. Friedman Test
B. Mann-Whitney U test
C. Kruskal-Wallis test
D. ANOVA
E. T-test

Answer: B

Explanation:
Since you're interested in comparing the medians of the ratings and not assuming a normal distribution (which is often the case with subjective human ratings), a non-parametric test is more appropriate than a t-test or ANOVA. The Mann-Whitney U test (also known as the Wilcoxon rank-sum test) is used to compare the medians of two independent groups. The Kruskal-Wallis test is used when you have more than two groups.

NEW QUESTION # 35
Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?

A. Using a learning rate scheduler to adjust the learning rate during training-
B. Applying standard image augmentation techniques to the image data.
C. Padding or truncating text sequences to a fixed length.
D. Using the exact same length of text and same images.
E. Applying Batch Normalization to the image features.

Answer: C

Explanation:
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.

NEW QUESTION # 36
You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

A. Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.
B. Treat all modalitites with equal weights during model training, ignoring potential skewness issues.
C. Balance the dataset by oversampling under-represented data points within each modality independently.
D. Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.
E. Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.

Answer: C,D,E

Explanation:
Addressing skewness is crucial for preventing the model from being biased towards dominant modalities. Oversampling, modality- specific weighting, and a biased-aware loss function are all effective strategies for mitigating this problem.

NEW QUESTION # 37
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