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NVIDIA Generative AI Multimodal Sample Questions:

1. You have a large dataset of images and text descriptions. You want to train a model that can perform both image captioning (generating text from images) and text-to-image generation (generating images from text). What architectural approach is best suited for this multimodal bi-directional task?

A) Use a shared encoder for both images and text, and separate decoders for generating text and images.
B) Use a generative adversarial network (GAN) for generating the outputs.
C) Use separate encoders for images and text, a shared attention mechanism, and separate decoders for generating text and images.
D) Use a single transformer model with a shared vocabulary and treat both image and text as sequences of tokens.
E) Train two separate models: one for image captioning and one for text-to-image generation.

2. You are tasked with optimizing a large multimodal AI model for deployment on edge devices with limited computational resources. Which combination of techniques would provide the BEST trade-off between model accuracy and inference speed? (Select TWO)

A) Model quantization (e.g., INT8) to reduce model size and improve inference speed.
B) Adding more layers to the model to increase its representational capacity.
C) Using larger batch sizes during inference to maximize GPIJ utilization.
D) Increasing the number of attention heads in the transformer architecture.
E) Pruning to remove less important connections in the model.

3. Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

A)

B)

C)

D)

E)

4. You are tasked with deploying a generative A1 model trained with NeMo using Triton Inference Server. You want to leverage TensorRT for optimized inference. Which of the following steps is crucial to ensure compatibility and optimal performance?

A) Export the NeMo model to ONNX format before deploying it to Triton+.
B) Bake the Triton server into a Docker container that includes all NeMo dependencies.
C) Directly deploy the NeMo model as a Python backend within Triton without any conversion.
D) Ensure that the Triton server is running on a CPU-only instance for maximum compatibility.
E) Convert the NeMo model to a TorchScript representation for TensorRT optimization.

5. You are working on a project that involves generating realistic images from text descriptions using a diffusion model. You want to reduce the inference time of the model, which currently takes several minutes to generate a single image. Which of the following techniques would be MOST effective for accelerating inference without significantly compromising image quality?

A) Increasing the number of diffusion steps.
B) Switching to a CPU-based inference engine.
C) Training the diffusion model with a larger dataset.
D) Using a smaller batch size during inference.
E) Employing techniques like DDIM (Denoising Diffusion Implicit Models) or progressive distillation to reduce the number of sampling steps required.

Solutions: