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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Software Development & Engineering	15%	- Python libraries for multimodal AI - Integration and deployment of multimodal AI systems
Topic 2: Multimodal Data	15%	- Applications and use cases - Handling and integrating text, image, and audio data
Topic 3: Data Analysis & Visualization	10%	- Data preprocessing and feature engineering - Visualization techniques for multimodal data
Topic 4: Performance Optimization	10%	- Monitoring and improving system efficiency - Techniques for optimizing AI performance
Topic 5: Core ML & AI Knowledge	20%	- Basic concepts and terminology - Key algorithms and techniques
Topic 6: Trustworthy AI	5%	- Ensuring fairness and transparency - Ethical considerations in AI development
Topic 7: Experimentation	25%	- A/B testing - Model evaluation and comparison - Hypothesis testing - Experimental design

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DumpsValid NVIDIA NCA-GENM Exam Questions in PDF Format

The third and last format is the NCA-GENM desktop practice exam software form that can be used without an active internet connection. This software works offline on the Windows operating system. The practice exams benefit your preparation because you can attempt them multiple times to improve yourself for the NVIDIA Generative AI Multimodal Professional-Cloud-Developercertification test. Our NCA-GENM Exam Dumps are customizable, so you can set the time and questions according to your needs.

NVIDIA Generative AI Multimodal Sample Questions (Q29-Q34):

NEW QUESTION # 29
You're working with a Text-to-Image generation model and want to improve its explainability by visualizing which words in the text prompt are most influential in generating specific regions of the output image. What technique would be most suitable for achieving this?

A. Analyzing the frequency of words in the generated image captions.
B. SHAP values applied to the text encoder.
C. Gradient-weighted Class Activation Mapping (Grad-CAM) on the image encoder.
D. Attention maps from the cross-attention layers between the text encoder and the image decoder.
E. Visualizing the latent space embeddings of the generated images.

Answer: D

Explanation:
Attention maps from the cross-attention layers directly indicate which words in the text prompt the image decoder attends to when generating different regions of the image. This provides a visual explanation of the relationship between text and image generation. Grad-CAM is more suited for explaining image classification models. SHAP values can explain the importance of words in the text, but not their spatial influence in the generated image.

NEW QUESTION # 30
You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?

A. Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.
B. Using word embeddings (e.g., Word2Vec, GloVe) for text, pre-trained CNN features (e.g., from ResNet, Inception) for images, and embedding user profiles using a graph embedding technique.
C. Using TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.
D. Using bag-of-words for text, histogram of oriented gradients (HOG) for images, and simple numerical features (e.g., number of followers) for user profiles.
E. Using a combination of TF-IDF for text, pixel values for images, and numerical features for user profile information. Then apply PCA for dimensionality reduction.

Answer: B

Explanation:
Using word embeddings captures semantic meaning in text. Pre-trained CNN features provide high-level image representations. Graph embedding for user profiles captures relationships between users. These advanced techniques provide better representations than simple methods like TF-IDF, pixel values, or bag-of-words.

NEW QUESTION # 31
Which of the following techniques are MOST effective for improving the energy efficiency of a large-scale Generative A1 model during inference, while minimizing performance degradation?

A. Knowledge distillation to a smaller model
B. Gradient accumulation
C. Pruning (removing less important weights)
D. Model quantization (e.g., INT8)
E. Increasing the batch size significantly

Answer: A,C,D

Explanation:
Model quantization reduces the memory footprint and computational cost by representing weights with fewer bits. Knowledge distillation trains a smaller, faster model to mimic the behavior of a larger model. Pruning removes redundant connections, reducing the number of computations. Gradient accumulation is for training, not inference. Increasing batch size may improve throughput but not necessarily energy efficiency per sample and might even decrease it due to increased memory usage.

NEW QUESTION # 32
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)

A. SPICE (Semantic Propositional Image Caption Evaluation)
B. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
C. BLEU (Bilingual Evaluation Understudy)
D. Self-BLEU
E. CIDEr (Consensus-based Image Description Evaluation)

Answer: D

Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.

NEW QUESTION # 33
Consider the following Python code snippet using PyTorch, designed to combine text and image embeddings before feeding them into a transformer. Assume 'text_embedding' has shape '(batch_size, seq_len, hidden_dim)' and 'image_embedding' has shape '(batch_size, image_features)'. Which of the following code snippets MOST correctly combines these embeddings for a multimodal transformer input?

Answer: A

Explanation:
Option B correctly expands the image embedding to match the sequence length of the text embedding and then concatenates them along the hidden dimension (dim=2), creating a combined multimodal input. Option A is incorrect as the shapes are incompatible, and Option C performs element-wise addition, requiring compatible shapes. Option D performs matrix multiplication, which is not a suitable way to combine embeddings. Option E is not a proper way of combining the tensors

NEW QUESTION # 34
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