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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

2. What does 'modality alignment' refer to?

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

3. What are some methods to overcome limited throughput between CPU and GPU?

A) Increase the clock speed of the CPU.
B) Using techniques like memory pooling.
C) Upgrade the GPU to a higher-end model.
D) Increase the number of CPU cores.

4. During the process of data cleansing, which of the following steps is NOT typically performed?

A) Collecting additional data
B) Removing duplicates
C) Transforming data into a different format
D) Identifying and handling missing values

5. 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.

Solutions: