NVIDIA NCA-GENL Exam Dumps
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NVIDIA NCA-GENL Sample Questions
Question # 1[Experimentation]You have access to training data but no access to test data. What evaluation method can you use to assess the performance of your AI model?
A. Cross-validation
B. Randomized controlled trial
C. Average entropy approximation
D. Greedy decoding
Question # 2
[Data Preprocessing and Feature Engineering]What is a Tokenizer in Large Language Models (LLM)?
A. A method to remove stop words and punctuation marks from text data.
B. A machine learning algorithm that predicts the next word/token in a sequence of text.
C. A tool used to split text into smaller units called tokens for analysis and processing.
D. A technique used to convert text data into numerical representations called tokens for machine
learning.
Question # 3
[Fundamentals of Machine Learning and Neural Networks]What is the main difference between forward diffusion and reverse diffusion in diffusion models ofGenerative AI?
A. Forward diffusion focuses on generating a sample from a given noise vector, while reversediffusion reverses the process by estimating the latent space representation of a given sample.
B. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
C. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing togenerate samples from noise vectors
D. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusionfocuses on generating new samples from the given noise vectors.
Question # 4
[Software Development]Which of the following is a key characteristic of Rapid Application Development (RAD)?
A. Iterative prototyping with active user involvement.
B. Extensive upfront planning before any development.
C. Linear progression through predefined project phases.
D. Minimal user feedback during the development process.
Question # 5
[Experimentation]You have developed a deep learning model for a recommendation system. You want to evaluate theperformance of the model using A/B testing. What is the rationale for using A/B testing with deeplearning model performance?
A. A/B testing allows for a controlled comparison between two versions of the model, helping toidentify the version that performs better.
B. A/B testing methodologies integrate rationale and technical commentary from the designers ofthe deep learning model.
C. A/B testing ensures that the deep learning model is robust and can handle different variations ofinput data.
D. A/B testing helps in collecting comparative latency data to evaluate the performance of the deeplearning model.
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