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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

Question 1

What characteristic of autoencoders makes them suitable for anomaly detection?

A. Their capability to predict future outcomes based on past data.
B. Their function in enhancing the quality of images.
C. Their capacity to learn a compressed representation of the data.
D. Their ability to classify images with high accuracy.

Question 2

In LLM evaluation, what does "zero-shot learning" refer to?

A. The model's performance after extensive training
B. A technique to reduce training time to zero
C. The model's ability to perform tasks it has not been explicitly trained on
D. The model's ability to learn from zero examples

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 increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.
C. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
D. 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.
E. 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.

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

What is a main application of Triton Inference Server?

A. Triton Server can be used to generate images from pure noise.
B. Triton Server can be used to deploy neural networks from various frameworks.
C. Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.
D. Triton Server can be used to deploy AI models on the GPU only.

Solutions: