NVIDIA Generative AI Multimodal : NCA-GENM Exam Questions

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

Question 2

You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?

A. Bar chart
B. Scatter plot
C. Pie chart
D. Line chart

Question 3

In multimodal machine learning, what does 'early fusion' refer to?

A. Ignoring certain modalities and only using one modality for analysis and prediction.
B. Implementing the model in the early stages of development of the ML solution.
C. Training separate models for each modality and then combining their predictions.
D. Integrating different modalities at the beginning of the model pipeline.

Question 4

You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?

A. Linear Regression
B. K-Means Clustering
C. Convolutional Neural Networks (CNN)
D. Decision Trees

Question 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. Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
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. More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.

Solutions: