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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
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. Supervised and unsupervised learning 2. Neural networks fundamentals
Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations
Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models
Multimodal AI Systems	- Multimodal model design - Cross-modal learning 1. Text-image integration 2. Audio-visual understanding

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NCA-GENM関連資料、NCA-GENM資格関連題

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NVIDIA Generative AI Multimodal 認定 NCA-GENM 試験問題 (Q13-Q18):

質問 # 13
You are training a conditional GAN (cGAN) to generate images of animals based on text descriptions. Which of the following is the most crucial difference in the training process of a cGAN compared to a regular GAN?

A. The cGAN uses a different loss function than a regular GAN.
B. The discriminator in a cGAN does not receive generated images.
C. The generator in a cGAN receives both random noise and the condition (text description) as input.
D. The discriminator in a cGAN only receives real images as input.
E. The cGAN does not require a discriminator.

正解：C

解説：
The key difference in a cGAN is that the generator takes an additional input, the condition (in this case, the text description), along with random noise. This allows the generator to learn a mapping from conditions to images, enabling the generation of images that match the given descriptions. The discriminator also receives the condition to evaluate if the generated image matches the corresponding condition. Other options are incorrect as Discriminator need both generated and real images with the conditioning variable to train correctly.

質問 # 14
Which of the following tasks can be performed using the transformer LLM encoder model?

A. Speech recognition
B. Semantic analysis
C. Image generation
D. Generating code

正解：B

解説：
Encoder-only transformer architectures (BERT and its derivatives being the canonical examples) process an entire input sequence bidirectionally, attending to both left and right context simultaneously, and output contextualized representations for each token - well suited to understanding tasks that require a rich representation of input meaning without generating new sequential output. Semantic analysis - including sentiment classification, named entity recognition, semantic similarity, and text classification more broadly - is a natural fit, typically implemented by taking the encoder's output representation (often a special [CLS] token embedding) and feeding it to a lightweight classification head.
The other options require architectures the encoder-only design is not built for: generating code (B) and open- ended text generation generally require autoregressive, causal (decoder-only or encoder-decoder) architectures that predict tokens sequentially, since encoder-only models produce a fixed set of contextualized representations rather than generating novel token sequences left-to-right. Image generation (C) requires an entirely different model family (diffusion models, GANs, or autoregressive image models), unrelated to text- encoder architectures. Speech recognition (D) requires an audio-processing pipeline - typically a CTC-based or encoder-decoder acoustic model operating on spectrogram or waveform input - not a text-only transformer encoder.
Reference: Core Machine Learning and AI Knowledge domain - transformer encoder vs. decoder vs.
encoder-decoder architectures and their respective task fits.

質問 # 15
You are tasked with evaluating a multimodal A1 model that combines image and text inputs to generate product descriptions. You observe that the model performs well on common product categories (e.g., clothing, electronics) but struggles with niche categories (e.g., antique furniture, scientific instruments). Which of the following strategies would be MOST effective in improving the model's performance on niche categories?

A. Implement data augmentation techniques to create synthetic data for niche categories.
B. Fine-tune the model on a dataset specifically curated for niche product categories.
C. Increase the overall size of the training dataset.
D. Replace the image encoder with a more powerful architecture.
E. Decrease the learning rate during training.

正解：B

解説：
Fine-tuning on a niche dataset addresses the specific lack of knowledge about those categories. While other options might offer marginal improvements, targeted fine-tuning is the most direct and effective approach. Data augmentation (E) could help, but is secondary to using real-world data for fine-tuning.

質問 # 16
You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?

A. Combining A, B, and C.
B. Synthesizing new text descriptions using paraphrasing and back-translation techniques.
C. Randomly cropping and rotating the existing furniture images.
D. Focusing solely on increasing the size of the text dataset and ignoring image augmentation.
E. Using generative adversarial networks (GANs) to generate new furniture images from the existing dataset.

正解：A

解説：
Combining all techniques provides the best results. Image augmentations like cropping and rotation increase the variance of the image data. GANs create entirely new images, and text augmentation enhances the diversity of the input descriptions. Focusing only on one modality will likely limit the model's performance.

質問 # 17
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

A. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.
B. Train a new classification head from scratch on top of the frozen pre-trained model.
C. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.
D. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.
E. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.

正解：A

解説：
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.

質問 # 18
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