NCA-GENM Actual Lab Questions: NVIDIA Generative AI Multimodal & NCA-GENM Exam Preparatory

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA Generative AI Multimodal Sample Questions (Q11-Q16):

NEW QUESTION # 11
You are building a multimodal Generative A1 system to generate image captions based on both the visual content of an image and a short audio description of the scene. Which architectural approach would be MOST effective for fusing these two modalities into a coherent representation for caption generation?

A. Intermediate Fusion: Train separate image and audio encoders, then use cross-attention mechanisms to allow the image features to attend to the audio features (and vice-versa) at multiple layers of the model.
B. Ignore the audio entirely, as images are sufficient for generating captions.
C. Early Fusion: Concatenate the raw image pixel data with the raw audio waveform data before feeding it into a single model.
D. Late Fusion: Train separate image and audio encoders, then concatenate their high-level feature vectors before feeding into a caption generation model.
E. Concatenate the image file name with the audio file name before feeding into the LLM.

Answer: A

Explanation:
Intermediate Fusion, particularly using cross-attention, allows for nuanced interaction between the modalities at multiple levels of abstraction. Early fusion is generally ineffective due to the vast differences in data type. Late fusion may miss important correlations. Ignoring a modality is obviously suboptimal when aiming for multimodal understanding.

NEW QUESTION # 12
In LLM evaluation, what does "zero-shot learning" refer to?

A. The model's ability to learn from zero examples
B. The model's ability to perform tasks it has not been explicitly trained on
C. A technique to reduce training time to zero
D. The model's performance after extensive training

Answer: B

Explanation:
Zero-shot learning describes a model's capacity to correctly perform a task it was never explicitly trained or fine-tuned on, relying instead on knowledge and generalization ability acquired during broader pretraining.
For LLMs, this typically means the model is given only a natural-language instruction or prompt describing the task - with no task-specific labeled examples provided in the prompt at all - and is expected to produce a reasonable response by generalizing from its pretraining. This is directly analogous to CLIP's zero-shot image classification (covered elsewhere in this set): a model trained broadly can be applied to a new, specific task purely through how the task is described to it, without additional task-specific training.
Option A is a subtly incorrect paraphrase: zero-shot learning is not about the model "learning from zero examples" during a training process - it's about applying a model that was never trained for the specific task at all, at inference time. The model isn't learning in the zero-shot moment; it's generalizing from prior training. Option B misapplies "zero" to training time rather than to task-specific examples - an unrelated concept. Option C directly contradicts the definition; zero-shot specifically refers to performance *without* task-specific training, not performance *after* extensive training on that task.
Zero-shot is typically contrasted with few-shot learning, where a small number of task-specific examples are included in the prompt to guide the model's response without updating its weights.
Reference: Core Machine Learning and AI Knowledge domain - zero-shot vs. few-shot learning, generalization in LLMs.

NEW QUESTION # 13
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A. Increase the size of the training dataset to dilute the effect of any biased examples.
B. Randomly shuffle the training dataset to minimize bias.
C. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
D. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
E. Filter out all examples containing demographic information from the training dataset.

Answer: C,D

Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.

NEW QUESTION # 14
You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?

A. Mean Squared Error (MSE)
B. Inception Score
C. BLEU (Bilingual Evaluation Understudy)
D. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
E. Perplexity

Answer: D

Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.

NEW QUESTION # 15
Which of the following are key benefits of using multimodal learning compared to unimodal learning? (Select TWO correct answers)

A. Reduced computational complexity.
B. Improved robustness to noise and missing data in one modality.
C. Simpler model architectures.
D. Guaranteed perfect accuracy.
E. Enhanced ability to capture complex relationships between different data types.

Answer: B,E

Explanation:
Multimodal learning leverages information from multiple modalities, which can lead to improved robustness because the model can rely on other modalities when one is noisy or incomplete. It also allows the model to learn more complex relationships that might not be apparent from a single modality.

NEW QUESTION # 16
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