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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NVIDIA NCA-GENM덤프데모문제 - NCA-GENM응시자료

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최신 NVIDIA-Certified Associate NCA-GENM 무료샘플문제 (Q26-Q31):

질문 # 26
What is the purpose of a kernel in a Convolutional Neural Network (CNN)?

A. To normalize the input data.
B. To calculate the loss function.
C. To classify the data into different categories.
D. To perform convolution operations on input data.

정답：D

설명：
A kernel (or filter) in a CNN is a small matrix of learnable weights that slides across the input (an image, feature map, or intermediate activation) computing a dot product at each spatial position - the convolution operation. Each kernel is trained to detect a specific local pattern: early-layer kernels typically learn to detect low-level features like edges and color gradients, while kernels in deeper layers combine these into detectors for more complex, higher-level patterns (textures, object parts, and eventually whole-object representations as receptive fields grow with depth). A convolutional layer typically applies many kernels in parallel, each producing its own output channel, collectively forming the layer's feature map.
The other options describe separate CNN components with distinct responsibilities: the loss function (B) is computed at the network's output based on the difference between predictions and ground truth, entirely separate from the kernel's role in feature extraction. Classification (C) is typically performed by fully connected (dense) layers - often with a softmax activation - placed after the convolutional feature- extraction stack, not by the kernels themselves. Normalization (D) is handled by dedicated layers such as batch normalization or layer normalization, inserted between convolutional layers to stabilize activations, again a separate mechanism from the convolution operation itself.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture, kernels/filters, feature extraction.

질문 # 27
You are building a multimodal emotion recognition system that combines facial expressions (images) and spoken language (audio). The image data is preprocessed using a CNN, and the audio data is processed using an LSTM. Which of the following fusion strategies would be MOST effective for combining these two modalities to predict the emotion?

A. Late fusion by training separate classifiers on the CNN and LSTM outputs and then averaging their predicted probabilities.
B. Early fusion by concatenating the raw pixel values of the images with the raw audio waveform.
C. Intermediate fusion by concatenating the CNN and LSTM hidden state representations before feeding them into a shared classification layer.
D. Training the CNN and LSTM models independently without any fusion.
E. Using an attention mechanism to weigh the contributions of the CNN and LSTM features based on their relevance to the predicted emotion.

정답：C,E

설명：
Early fusion is generally ineffective due to the high dimensionality and lack of meaningful relationships between raw pixel values and audio waveforms. Late fusion ignores the potential for interactions between modalities. Intermediate fusion allows the model to learn these interactions by combining representations at a higher level. Attention mechanism is even better because the model could weigh the contributions of the CNN and LSTM features.

질문 # 28
Consider the following Python code snippet, which attempts to implement a basic form of cross-validation. What is the primary issue with this code and how would you fix it to prevent data leakage?

A. Data leakage occurs because feature scaling is applied to the entire dataset before splitting it into training and testing sets. Fix: Apply scaling separately to the training and testing sets within each fold.
B. Data leakage occurs because the model is not being evaluated on a hold-out set Fix: Create a separate validation set and evaluate the model on it after each fold.
C. The code is correct and doesn't have any data leakage issues.
D. Data leakage occurs because the model is being reinitialized in each fold. Fix: Move the model initialization outside the loop.
E. Data leakage occurs because the 'KFold" split is not shuffled. Fix: Set in the "KFold' constructor.

정답：A

설명：
Data leakage happens because the StandardScaler is fit on the entire dataset before splitting into training and testing sets. This means that the scaling parameters (mean and standard deviation) are influenced by the test data, which then leaks information into the training process, leading to overoptimistic performance estimates. The correct fix is to fit the scaler only on the training data within each fold and then use the fitted scaler to transform both the training and testing data.

질문 # 29
Consider you are working on a project that aims at generating photorealistic images from segmentation maps, using a conditional GAN architecture. The training process is unstable, frequently exhibiting mode collapse and artifacts. Describe a series of techniques, ranked by their likely impact, to mitigate these issues.

A. 1. Increase batch size. 2. Decrease learning rate. 3. Add more convolutional layers.
B. 1. None of the above
C. 1. Implement Spectral Normalization. 2. Use PatchGAN discriminator. 3. Apply data augmentation (e.g., random flips, jitter).
D. 1. Reduce the number of layers in the discriminator. 2. Increase the learning rate of the generator. 3. Disable batch normalization.
E. 1. Switch to a Transformer-based architecture. 2. Use a larger dataset. 3. Decrease the number of channels in the generator.

정답：C

설명：
Spectral Normalization stabilizes training by limiting the Lipschitz constant of the discriminator. PatchGAN discriminator focuses on local image patches, improving detail and reducing artifacts. Data augmentation increases the diversity of training data and improves generalization. Thus, Option B presents the most impactful techniques, ranked appropriately. Other options either suggest less impactful techniques or recommend steps that could worsen the issues. Mode Collapse can be avoided here with data augmentation.

질문 # 30
Which of the following is a disadvantage of the ReLU activation function?

A. It is computationally expensive.
B. It can cause dead neurons.
C. It is not suitable for deep neural networks.
D. It is prone to vanishing gradient problem.

정답：B

설명：
Reviewer note: Marked answer (C) is factually incorrect - ReLU is well suited to deep networks and specifically helps mitigate vanishing gradients. The genuine, well-established disadvantage is the 'dying ReLU' problem (D).
I need to flag this one as well: the marked answer (C) does not hold up, and stating otherwise would misrepresent a fairly foundational deep learning fact. ReLU (Rectified Linear Unit, f(x) = max(0, x)) is, if anything, particularly well suited to deep neural networks - it was widely adopted specifically *because* it mitigates the vanishing gradient problem that plagued earlier activation functions like sigmoid and tanh in deep architectures: ReLU's gradient is a constant 1 for all positive inputs, rather than the saturating, near-zero gradients that sigmoid/tanh produce for large-magnitude inputs, which allows gradients to propagate more effectively through many layers.
The genuine, well-documented disadvantage of ReLU is option D: the "dying ReLU" problem. Because ReLU's gradient is exactly zero for any negative input, a neuron whose weighted input becomes consistently negative - often due to a large negative gradient update or an unfavorable initialization - will always output zero and will never receive a gradient large enough to recover, effectively "dying" and no longer contributing to learning. This is a real, practically significant issue that motivated variants like Leaky ReLU, Parametric ReLU (PReLU), and ELU, which allow a small non-zero gradient for negative inputs specifically to prevent neurons from dying.
Options A and B are also factually incorrect characterizations of ReLU - it is computationally cheap (a simple thresholding operation, part of its original appeal over sigmoid/tanh) and it specifically helps *avoid* vanishing gradients rather than causing them.

질문 # 31
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