NVIDIA Generative AI Multimodal : NCA-GENM

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. 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) 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.
C) 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.
D) Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
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.

2. Which framework is used for conversational AI models development?

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

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

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

4. Which of the following best describes the role of machine learning in handling multimodal data?

A) To eliminate the need for human intervention in data analysis.
B) To focus on textual data analysis.
C) To enable models to learn from and interpret diverse data types.
D) To reduce the amount of data needed for accurate predictions.

5. Which of the following tasks can be performed using the transformer LLM encoder model?

A) Speech recognition
B) Generating code
C) Image generation
D) Semantic analysis

Solutions: