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NVIDIA Generative AI Multimodal Sample Questions:

1. You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.

A) Increase the size of the vocabulary used by the model.
B) Apply techniques like beam search during decoding to generate more diverse code snippets.
C) Increase the number of Transformer layers.
D) Use a reinforcement learning approach where the reward is based on whether the generated code passes the unit tests associated with the descriptiom
E) Fine-tune the model on a subset of the data where each code snippet is accompanied by unit tests, and train the model to also generate these tests.

2. Consider the following Python code snippet used for evaluating a generative model. What potential issue exists with this code, and how would you rectify it to ensure a robust evaluation?

A) The number of generated images Cn_generated_imageS) is too small for a reliable Inception Score calculation and should be increased to at least 50,000.
B) The code is fundamentally correct and does not have any issues.
C) The Inception Score is not an appropriate metric for evaluating generative models, and other methods should be used instead.
D) The 'inception_score' function is deprecated and should be replaced with a newer metric like FID.
E) The code does not account for the stochastic nature of generative models and needs to be run multiple times with different random seeds to get a stable estimate of the Inception Score.

3. You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

A) Deploying Riva on CPU using multiprocessing.
B) Deploying Riva on a single CPU core with a small batch size.
C) Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.
D) Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.
E) Deploying Riva on a single GPU with a large batch size.

4. You are working with a large multimodal dataset containing images and text. You want to efficiently load and preprocess this data for training a generative A1 model on an NVIDIA GPU. Which of the following approaches would be most effective for maximizing data loading speed and GPU utilization?

A) Compressing the dataset into a single large archive file and decompressing it on the fly during training.
B) Using a Python-based data loader that reads images and text directly from disk during training.
C) Loading the entire dataset into CPU memory before starting training.
D) Storing the images and text in a relational database and querying the database during training.
E) Employing NVIDIA's DALI (Data Loading Library) to perform data loading and preprocessing on the GPU.

5. You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A) Using random prompts to explore the model's creative capabilities.
B) Using overly verbose and descriptive prompts to maximize detail.
C) Using prompts consisting only of keywords related to the desired image.
D) Using negative prompts to explicitly exclude unwanted features or styles.
E) Using short, concise prompts to minimize ambiguity.

Solutions: