Free NVIDIA NCA-GENL Exam Dumps Questions & Answers

You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is the rationale for using A/B testing with deep learning model performance?

• A.  A/B testing ensures that the deep learning model is robust and can handle different variations of input data.
• B.  A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.
• C.  A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.
• D.  A/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.

What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

• A.  Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
• B.  Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.
• C.  Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.
• D.  Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.

When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?

• A.  Chi-squared test
• B.  Mann-Whitney U test
• C.  ANOVA test
• D.  Paired t-test

Which of the following is a key characteristic of Rapid Application Development (RAD)?

• A.  Minimal user feedback during the development process.
• B.  Extensive upfront planning before any development.
• C.  Iterative prototyping with active user involvement.
• D.  Linear progression through predefined project phases.

Imagine you are training an LLM consisting of billions of parameters and your training dataset is significantly larger than the available RAM in your system. Which of the following would be an alternative?

• A.  Using the GPU memory to extend the RAM capacity for storing the dataset and move the dataset in and out of the GPU, using the PCI bandwidth possibly.
• B.  Eliminating sentences that are syntactically different by semantically equivalent, possibly reducing the risk of the model hallucinating as it is trained to get to the point.
• C.  Using a memory-mapped file that allows the library to access and operate on elements of the dataset without needing to fully load it into memory.
• D.  Discarding the excess of data and pruning the dataset to the capacity of the RAM, resulting in reduced latency during inference.