NVIDIA Generative AI Multimodal : NCA-GENM

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

Question #1
Which framework is used for conversational AI models development?

A. NVIDIA Metropolis
B. NVIDIA DeepStream
C. NVIDIA Clara
D. NVIDIA NeMo

Question #2
For building a zero-shot image classification pipeline, what could be a crucial step in the process?

A. Focusing on enhancing the resolution and quality of images before classification.
B. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.
C. Manually labeling each image in the dataset for precise classification.
D. Designing an algorithm to replace the need for textual descriptions in the classification process.

Question #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 increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.
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 different objects and decreases the similarity of representations across modalities for same 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.

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

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

Question #5
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?

A. The model's accuracy in recognizing individuals of different races.
B. The model's ability to recognize various facial expressions.
C. The model's compatibility with different operating systems.
D. The model's processing speed in recognizing faces of different races.

Solutions: