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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?

A) Calculating the loss function of the model on the training set.
B) Interviewing the developers of the AI model to assess its performance.
C) Calculating the model's accuracy from randomly selected data points from the dataset not used during the model's training.
D) Randomly selecting data points from the training set and calculating the accuracy of the model on these data points.

2. 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) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
D) 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.

3. Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?

A) F1 Score
B) Mean Opinion Score (MOS)
C) Word Error Rate (WER)
D) CTC Loss

4. Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?

A) To classify and categorize data based on patterns and features.
B) To produce new data that is similar to the training data.
C) To optimize search algorithms for faster data retrieval.
D) To optimize decision-making processes based on historical data.

5. Which of the following tasks can be performed using the transformer LLM encoder model?

A) Speech recognition
B) Semantic analysis
C) Generating code
D) Image generation

Solutions: