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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

A) It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
B) It ensures that AI systems are transparent in their decision-making processes.
C) It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
D) It requires AI systems to be developed with an ethical consideration for societal impacts.

2. What does 'modality alignment' refer to?

A) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
B) The integration of pretrained models to perform custom tasks involving different types of data.
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. 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 produce new data that is similar to the training data.
C) To optimize search algorithms for faster data retrieval.
D) To classify and categorize data based on patterns and features.

4. What is a main application of Triton Inference Server?

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

5. Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?

A) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
B) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
C) Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.
D) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.

Solutions: