NVIDIA NCA-GENM real exam prep : NVIDIA Generative AI Multimodal

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

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

2. What advantage does multimodal learning have over unimodal learning?

A) It is easier to collect multimodal data than unimodal data.
B) It can capture more complex patterns and relationships in data.
C) It is more reliable than unimodal learning.
D) It requires fewer data samples for learning.

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

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

4. Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?

A) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
B) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
C) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
D) Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.

5. How does CLIP understand the content of both text and images?

A) Through a database of predefined images with their descriptions.
B) Using contrastive learning to match images with text descriptions.
C) By converting text and images into a frequency domain for comparison.
D) By translating images into text and comparing them with the prompt.

Solutions: