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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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

NEW QUESTION # 27
When training a Variational Autoencoder (VAE) for generating new data points, which of the following objectives does the VAE optimize?

A. All of the above.
B. Maximizing the similarity between the input data and the reconstructed data.
C. Minimizing the Kullback-Leibler (KL) divergence between the learned latent distribution and a prior distribution (e.g., a Gaussian distribution).
D. Maximizing the likelihood of the input data given the latent representation.
E. Only A and B.

Answer: A

Explanation:
A VAE optimizes all three objectives. It aims to maximize the likelihood of the input data given the latent representation (reconstruction accuracy), minimize the KL divergence to ensure the latent space is well-structured and smooth, and maximize the similarity between the input and reconstructed data to ensure effective encoding and decoding.

NEW QUESTION # 28
You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?

A. Implementing a retrieval-augmented generation (RAG) approach.
B. Training the model on a smaller dataset.
C. Reducing the temperature parameter during generation.
D. Increasing the model size by adding more layers.
E. Removing dropout layers.

Answer: A

Explanation:
Retrieval-augmented generation (RAG) involves retrieving relevant information from an external knowledge source (e.g., a database or a collection of documents) and incorporating it into the model's generation process. This can help the model generate more factually accurate and coherent outputs by grounding its responses in external knowledge. Increasing the model size might improve performance, but doesn't guarantee factual accuracy. Reducing temperature makes the output more predictable but doesn't address factual correctness. Smaller datasets and removing dropout would likely worsen performance.

NEW QUESTION # 29
You are working on a project that involves generating music from video. The approach uses a pre-trained video encoder and a pre- trained music decoder. You find that the generated music often lacks a clear connection to the visual content of the video. To improve the coherence between the video and the generated music, which of the following steps would be the MOST effective? (Select TWO)

A. Introduce a cross-modal attention mechanism to allow the music decoder to attend to relevant visual features during music generation.
B. Remove the video encoder and generate music directly from random noise.
C. Fine-tune the entire system end-to-end with a loss function that encourages temporal alignment between video and music features.
D. Train the video encoder and music decoder separately on larger datasets.
E. Only use videos that are shorter than 5 seconds.

Answer: A,C

Explanation:
Cross-modal attention (B) allows the model to explicitly learn relationships between visual and musical features. End-to-end fine- tuning with a suitable loss function (D) encourages the model to learn a coherent mapping between the two modalities. Training the encoders and decoders separately won't necessarily improve coherence. Generating from random noise defeats the purpose. Limiting video length may reduce complexity but doesn't fundamentally address the alignment problem.

NEW QUESTION # 30
You're training a VQA (Visual Question Answering) model. During evaluation, you notice the model performs well on common object recognition tasks but struggles with questions requiring reasoning about object relationships or scene understanding. What are the MOST effective strategies to improve the model's performance on these complex reasoning tasks? (Choose two)

A. Train the model on a larger dataset with more diverse and complex questions/answers.
B. Replace the LSTM with a simpler RNN in the question encoder.
C. Decrease the learning rate.
D. Increase the size of the image embedding.
E. Use a more sophisticated attention mechanism that attends to relevant image regions based on the question.

Answer: A,E

Explanation:
More sophisticated attention mechanisms help the model focus on relevant image regions. A larger, more diverse dataset provides the model with more examples of complex reasoning scenarios. Increasing the image embedding size may help but is not as targeted. Decreasing the learning rate is a general optimization technique, and using a simpler RNN would likely degrade performance.

NEW QUESTION # 31
In convolutional neural networks, we may use padding in both convolution and transposed convolution.
Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.

A. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.
B. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
C. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.
D. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
E. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.

Answer: B,D

Explanation:
Padding behaves in a genuinely counter-intuitive, and often confused, way between standard convolution and transposed convolution, which is exactly why this pairing is tested together. In standard convolution, adding padding to the input before the kernel slides across it effectively increases the input's spatial extent, which - for a fixed kernel size and stride - increases (or, in "same" padding, preserves) the resulting output feature map's spatial dimensions relative to the unpadded case; padding this way also allows the kernel to be centered properly over boundary/edge pixels, which would otherwise be under-sampled compared to interior pixels (option C's first half).
In transposed convolution (sometimes called "deconvolution," used for upsampling in decoder/generator architectures), padding operates on the *output* side after the input has already been expanded by inserting stride-related spacing between elements: the padding parameter specifies how many rows/columns to
*remove* from the perimeter of that expanded, computed output - meaning padding in transposed convolution shrinks rather than grows the resulting output dimensions, the reverse of its effect in standard convolution. This gives option A's directional claim and option C's second half.
Option B reverses which operation padding applies to (input vs. output) for each case. Option D is incorrect
- padding's purpose is spatial-dimension and boundary handling, not memory/compute reduction (padding, if anything, typically adds slightly more computation). Option E states an artificial, non-standard usage rule that doesn't reflect how padding is actually applied in practice.
Reference: Core Machine Learning and AI Knowledge domain - convolution vs. transposed convolution, padding mechanics and output-dimension effects.

NEW QUESTION # 32
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