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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a multimodal model that takes both images and text as input. You want to fuse these modalities at an early stage.
Which of the following techniques is MOST appropriate for early fusion?

A) Concatenating the feature vectors extracted from the image and text encoders before passing them to a shared decoder.
B) Training separate models for images and text and then averaging their predictions.
C) Concatenating the raw image pixels and text tokens as a single input sequence.
D) Using a cross-attention mechanism between the image and text feature maps in the early layers of the model.
E) Passing the image and text through separate encoders and then using a late fusion technique such as a weighted sum of their outputs.

2. When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?

A) Decrease the learning rate during training.
B) Use beam search during inference with a large beam size.
C) Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.
D) Decrese the size of the vocabulary.
E) Increase the size of the image encoder.

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

4. You are tasked with evaluating the scalability of a multimodal generative model deployed on an NVIDIAAI 00 GPU. The model processes text, images, and audio. Which of the following metrics and tools would be MOST relevant to monitor and analyze?

A) CPU utilization and memory usage.
B) GPU utilization, GPU memory usage, and throughput (samples per second).
C) Network latency and bandwidth.
D) Disk 1/0 and storage capacity.
E) CUDA core utilization and Tensor Core utilization.

5. You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)

A) Inception Score of the input image.
B) Precision and recall of the ingredients mentioned in the generated recipe compared to a ground truth ingredient list.
C) Calculating the cosine similarity between the word embeddings of the generated and reference recipes.
D) BLEU score between the generated recipe and a reference recipe.
E) Human evaluation of the generated recipe's clarity, coherence, and accuracy.

Solutions: