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NVIDIA Generative AI Multimodal Sample Questions:

1. You're developing an Avatar Cloud Engine (ACE) application to create a real-time, interactive virtual assistant. The assistant needs to respond to user speech, understand their intent, and generate appropriate responses. Which sequence of NVIDIA SDKs would provide the MOST complete solution for this task?

A) Riva (for speech recognition and synthesis) -> Triton Inference Server (for serving a pre-trained chatbot model) -> ACE (for avatar rendering and animation).
B) CUDA (For running deep learning workloads)-> Riva (for speech recognition and synthesis) -> ACE (for avatar rendering and animation).
C) Riva (for speech recognition and synthesis) -> NeMo (for natural language understanding and response generation) -> Triton Inference Server (for model deployment) ACE (for avatar rendering and animation).
D) Triton Inference Server (for serving all models) -> Riva (for speech recognition and synthesis) ACE (for avatar rendering and animation).
E) NeMo (for training a custom language model) -> Triton Inference Server (for serving the trained language model) -> ACE (for avatar rendering and animation).

2. When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

A) Train the GAN for a longer duration.
B) Use a larger UNet architecture within the Diffusion model.
C) Use a smaller batch size.
D) Increase the number of diffusion steps.
E) Employ distillation techniques to train a faster, smaller model.

3. Which of the following Python code snippets correctly demonstrates how to load pre-trained word embeddings (e.g., GloVe or Word2Vec) using spaCy and then calculate the cosine similarity between two words?

A)

B)

C)

D)

E)

4. You are building a real-time image captioning system using a Transformer model. You observe significant latency issues when generating captions for high-resolution images. Which optimization strategies would be most effective in reducing the latency without significantly sacrificing caption quality? (Select all that apply)

A) Use mixed precision training and inference, leveraging Tensor Cores on NVIDIA GPUs.
B) Employ model quantization techniques (e.g., INT8 quantization) to reduce model size and memory bandwidth requirements.
C) Implement knowledge distillation, training a smaller, faster student model to mimic the behavior of the larger Transformer model.
D) Decrease the batch size during inference.
E) Increase the number of Transformer layers to improve caption accuracy, even if it increases latency.

5. Which of the following are potential solutions to mitigate the impact of missing or incomplete data in a multimodal dataset used for training a generative A1 model? (Select all that apply)

A) Using a masking strategy during training, where the model learns to predict the missing data based on the available data.
B) Data imputation techniques, such as mean imputation or k-nearest neighbors imputation.
C) Using a specialized multimodal model designed to handle missing data.
D) Training the model only on complete data samples, discarding any samples with missing data.
E) All of the above.

Solutions: