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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?

A) Calculate the cosine similarity between the image and text embeddings.
B) Encode the text prompt using CLIP's text encoder.
C) Encode the generated image using CLIP's image encoder.
D) Fine-tune the CLIP model on your specific image generation task.
E) Resize the generated image to a very high resolution.

2. You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model's performance?

A) Translating the clinical notes into multiple languages and then back-translating to the original language.
B) Utilizing named entity recognition (NER) to identify medical entities (diseases, medications, etc.), and employing a medical-specific language model (e.g., BioBERT) for text embeddings.
C) Applying basic text cleaning (removing punctuation, converting to lowercase) and using a standard word embedding (e.g., Word2Vec).
D) Performing sentiment analysis on the clinical notes.
E) Directly feeding the raw clinical notes into the model without any preprocessing.

3. You are working on a sequence-to-sequence model for neural machine translation. You've implemented an attention mechanism, but the model is still struggling with long sentences, often losing context in the later parts of the translation. Which type of attention mechanism is most likely to alleviate this issue effectively?

A) Self-Attention
B) Local (Hard) Attention
C) Global (Soft) Attention
D) Multi-Head Attention
E) Bahdanau Attention (Additive Attention)

4. You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?

A) Riva ASR, Riva TTS, Audi02Emotion, and a 3D avatar model.
B) Riva ASR, Riva TTS, Audi02Emotion, a 3D avatar model, and an animation engine.
C) Riva ASR, Riva TTS, and Audi02Emotion.
D) Only a 3D avatar model.
E) only Riva ASR and TTS.

5. You are tasked with evaluating a multimodal A1 model that combines image and text inputs to generate product descriptions. You observe that the model performs well on common product categories (e.g., clothing, electronics) but struggles with niche categories (e.g., antique furniture, scientific instruments). Which of the following strategies would be MOST effective in improving the model's performance on niche categories?

A) Fine-tune the model on a dataset specifically curated for niche product categories.
B) Decrease the learning rate during training.
C) Implement data augmentation techniques to create synthetic data for niche categories.
D) Replace the image encoder with a more powerful architecture.
E) Increase the overall size of the training dataset.

Solutions: