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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

Question 1

What is the purpose of the cuDNN library?

A. To optimize deep neural network computations on NVIDIA GPUs.
B. To implement GPU-accelerated data preparation and feature extraction.
C. To generate images from English text-prompts using CLIP.
D. To measure GPU usage and other metrics with Prometheus.

Question 2

In the transformer architecture, what is the purpose of positional encoding?

A. To remove redundant information from the input sequence.
B. To add information about the order of each token in the input sequence.
C. To encode the semantic meaning of each token in the input sequence.
D. To encode the importance of each token in the input sequence.

Question 3

Which of the following best describes the role of the Hugging Face model repository in ML software development?

A. A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
B. A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
C. A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
D. A platform for sharing and accessing pre-trained models and transformers for natural language processing.

Question 4

Which framework is used for conversational AI models development?

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

Question 5

What does 'modality alignment' refer to?

A. Addressing challenges related to missing or incomplete information across different modalities.
B. The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
C. The integration of pretrained models to perform custom tasks involving different types of data.
D. Aligning different modalities within multimodal data to ensure meaningful connections and associations.

Solutions: