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NVIDIA Generative AI Multimodal Sample Questions:

1. During data analysis for a multimodal A1 project involving image and text data, you discover that the image dataset contains a large number of blurry or low-resolution images. The text data, however, is relatively clean and well-structured. What is the BEST approach to mitigate the impact of the noisy image data on the overall model performance?

A) Train the model on the noisy image data without any preprocessing or data augmentation.
B) Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.
C) Increase the weight of the text data during model training to compensate for the noisy image data.
D) Discard the blurry and low-resolution images from the dataset to ensure data quality.
E) Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.

2. When training a Variational Autoencoder (VAE) for generating new data points, which of the following objectives does the VAE optimize?

A) Maximizing the similarity between the input data and the reconstructed data.
B) Only A and B.
C) All of the above.
D) Minimizing the Kullback-Leibler (KL) divergence between the learned latent distribution and a prior distribution (e.g., a Gaussian distribution).
E) Maximizing the likelihood of the input data given the latent representation.

3. Which of the following techniques can be used to improve the factual accuracy of text generated by a large language model?

A) Applying a temperature of 0 during text generation.
B) Always using the same prompt, regardless of the desired output.
C) Fine-tuning the model on a dataset of factually correct information.
D) Using retrieval-augmented generation (RAG) to ground the model's knowledge in external sources.
E) Increasing the model size and training it on more data.

4. You're developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?

A) Reduce the dimensionality of the image features before fusion.
B) Increase the batch size for each epoch.
C) Apply modality-specific dropout to the image pathway.
D) Oversample the audio data during training.
E) Increase the learning rate for the audio modality pathway during training.

5. You're developing a system that translates spoken language into sign language animations. Which of the following losses would be MOST suitable for training the model to generate realistic and accurate sign language sequences from speech input?

A) Mean Squared Error (MSE) loss between the predicted joint positions of the sign language character and the ground truth joint positions.
B) Cross-entropy loss between the predicted sign language sequence and the ground truth sequence.
C) Cosine Similarity loss between audio embeddings and sign language animation embeddings.
D) Binary Cross entropy to classify the output sign animation-
E) A combination of MSE loss for joint positions and a temporal smoothness loss to encourage smooth transitions between sign language poses.

Solutions: