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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q27-Q32):

NEW QUESTION # 27
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. Mixed-precision training guarantees the same convergence behavior as full-precision training.
B. Mixed-precision training allows using lower precision for forward and backward passes but keeps weights and gradients in higher precision to maintain stability.
C. A and B.
D. Tensor Cores are only useful for inference, not training.
E. Tensor Cores perform specialized matrix multiplications optimized for lower-precision data types, enabling faster computation and reduced memory footprint.

Answer: C

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 # 28
Consider a multimodal emotion recognition system that uses both facial expressions (images) and speech (audio). You want to fuse the information from these two modalities at the decision level. Which of the following techniques would be MOST suitable for decision-level fusion?

A. Concatenate the feature vectors extracted from the images and audio, then train a single classifier.
B. Train separate classifiers for images and audio, then use the output of the image classifier as input to the audio classifier-
C. Train separate classifiers for images and audio, then use a weighted average of their output probabilities based on the confidence scores of each classifier.
D. Train a single transformer to process both images and audio in sequence.
E. Train separate classifiers for images and audio, then average their output probabilities for each emotion class.

Answer: C

Explanation:
Weighted averaging allows you to give more weight to the modality that is more reliable or confident in its prediction for a given input. Simply averaging treats all modalities equally. Concatenation is feature-level fusion. The image classifier as input to audio classifier is a specific cascade approach. Using a single transformer is possible, but less common for decision fusion specifically. It is feature level fusion.

NEW QUESTION # 29
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)

A. Data imputation techniques, such as mean imputation or k-nearest neighbors imputation.
B. Using a specialized multimodal model designed to handle missing data.
C. All of the above.
D. Training the model only on complete data samples, discarding any samples with missing data.
E. Using a masking strategy during training, where the model learns to predict the missing data based on the available data.

Answer: A,B,E

Explanation:
Data imputation, masking strategies, and specialized multimodal models are all valid techniques for handling missing data. Discarding samples with missing data can lead to a significant loss of information and potentially bias the model. Option E is incorrect as B is incorrect.

NEW QUESTION # 30
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?

A. Replace the CLIP model with a larger, more powerful text encoder that has been trained on a more diverse dataset.
B. Implement a technique called 'Classifier-Free Diffusion Guidance', which allows for more flexible control over the generated image content.
C. Fine-tune the diffusion model using a dataset of images specifically depicting the objects that are frequently misrepresented.
D. Increase the guidance scale during diffusion sampling, forcing the generated images to align more closely with the CLIP embeddings.
E. All of the Above

Answer: B,D

Explanation:
Increasing the guidance scale (B) forces stronger alignment with the CLIP embeddings, improving object fidelity. Classifier-Free Diffusion Guidance (D) provides finer-grained control over image content, allowing the model to better represent specific objects. Fine-tuning the diffusion model (A) can be helpful but requires a significant amount of data. Using a larger text encoder (C) may improve overall performance but may not directly address object fidelity. Classifier-Free Diffusion Guidance and increasing guidance scale are the most targeted strategies to increase object fidelity for text-to-image models, as guidance scale can also have some artifacts.

NEW QUESTION # 31
For building a zero-shot image classification pipeline, what could be a crucial step in the process?

A. Focusing on enhancing the resolution and quality of images before classification.
B. Designing an algorithm to replace the need for textual descriptions in the classification process.
C. Manually labeling each image in the dataset for precise classification.
D. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.

Answer: D

Explanation:
Zero-shot image classification, by definition, requires classifying images into categories the model was never explicitly trained to recognize, with no task-specific labeled examples. CLIP-style models enable this by encoding both images and candidate text labels (e.g., "a photo of a {class}") into a shared embedding space; classification then reduces to a similarity comparison - computing cosine similarity between the image embedding and each candidate text embedding and selecting the closest match. This is the crucial architectural step: without a shared embedding space linking visual and textual semantics, there is no mechanism to generalize to unseen classes using only their names or descriptions.
Option B directly contradicts the "zero-shot" premise - manual labeling of the target dataset is precisely what zero-shot classification is designed to avoid; if labels were being collected for the target classes, the task would be standard supervised classification, not zero-shot. Option A (image enhancement) may marginally help downstream accuracy but is not the crucial, defining step. Option D is incoherent with how CLIP-style zero-shot classification actually works - the textual description of each candidate class is the essential input that makes zero-shot generalization possible; eliminating it would remove the mechanism entirely, not improve it.
Reference: Multimodal Data domain - zero-shot classification via shared embedding spaces (CLIP).

NEW QUESTION # 32
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