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NVIDIA NCA-GENM 試験シラバストピック:

NVIDIA Generative AI Multimodal 認定 NCA-GENM 試験問題：

問題 #1

In experimentation, how does data augmentation contribute to improving model accuracy?

A. It improves the interpretability of the model by providing additional insights into the data.
B. It helps in increasing the size of the dataset, leading to better generalization of the model.
C. It reduces the complexity of the model, making it easier to train and evaluate.
D. It has no impact on model accuracy and is primarily used for data visualization purposes.

問題 #2

How does the batch size influence VRAM consumption during inference with ML models on GPUs?

A. Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.
B. Increasing or decreasing the batch size has the same impact on VRAM consumption.
C. Decreasing the batch size reduces VRAM consumption.
D. The batch size has no impact on VRAM consumption during inference.

問題 #3

What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.

A. In a multimodal context, usually, contrastive learning decreases the similarity of representations across modalities for the same objects and increases the similarity of representations across modalities for different objects.
B. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
C. Contrastive learning is a technique used to train deep learning models by comparing similar and dissimilar inputs and optimizing the model to maximize the similarity between representations of similar inputs and minimize the similarity between representations of dissimilar inputs.
D. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.
E. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.

問題 #4

What is the correct order of steps in an ML project?

A. Model evaluation, Data preprocessing, Model training, Data collection
B. Data collection, Data preprocessing, Model training, Model evaluation
C. Model evaluation, Data collection, Data preprocessing, Model training
D. Data preprocessing, Data collection, Model training, Model evaluation

問題 #5

You are conducting an experiment to evaluate the performance of different AI models. What is the purpose of AI model evaluation?

A. To study the impact of AI models on human behavior.
B. To determine the ethical implications of AI model usage.
C. To analyze the cost-effectiveness of AI model development.
D. To determine the best AI model architecture.

解説：