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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

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

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

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

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

4. Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?

A) To optimize search algorithms for faster data retrieval.
B) To produce new data that is similar to the training data.
C) To classify and categorize data based on patterns and features.
D) To optimize decision-making processes based on historical data.

5. Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?

A) CTC Loss
B) Word Error Rate (WER)
C) F1 Score
D) Mean Opinion Score (MOS)

Solutions: