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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?

A) To classify input images as noisy or clean.
B) To detect noisy objects in input images.
C) To generate new images from pure noise.
D) To segment noisy patches in input images.

2. In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

A) It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
B) It ensures that AI systems are transparent in their decision-making processes.
C) It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
D) It requires AI systems to be developed with an ethical consideration for societal impacts.

3. In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?

A) Support vector machine (SVM)
B) Decision tree
C) Generative adversarial network (GAN)
D) K-means clustering

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

A) Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
B) 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.
C) 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.
D) 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.
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.

5. Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?

A) Heatmap
B) Box plot
C) Pie chart
D) Histogram

Solutions: