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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a retrieval-augmented generation (RAG) system that utilizes a knowledge graph to enhance the responses generated by a large language model. The knowledge graph contains information about entities and their relationships extracted from both text documents and image metadat a. However, you observe that the system often retrieves irrelevant or outdated information from the knowledge graph, leading to inaccurate or misleading responses. Which of the following strategies would be MOST effective in addressing this issue?

A) None of the above.
B) Reduce the number of entities in the knowledge graph.
C) Implement a mechanism to filter and rank the retrieved information based on relevance and recency, using both semantic similarity and temporal information.
D) Increase the size of the knowledge graph.
E) Use a simpler language model for the generative component of the RAG pipeline.

2. You are building a multimodal emotion recognition system that combines facial expressions (images) and spoken language (audio). The image data is preprocessed using a CNN, and the audio data is processed using an LSTM. Which of the following fusion strategies would be MOST effective for combining these two modalities to predict the emotion?

A) Using an attention mechanism to weigh the contributions of the CNN and LSTM features based on their relevance to the predicted emotion.
B) Intermediate fusion by concatenating the CNN and LSTM hidden state representations before feeding them into a shared classification layer.
C) Early fusion by concatenating the raw pixel values of the images with the raw audio waveform.
D) Training the CNN and LSTM models independently without any fusion.
E) Late fusion by training separate classifiers on the CNN and LSTM outputs and then averaging their predicted probabilities.

3. You are working on a multimodal AI model that generates images from text descriptions. You notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques is LEAST likely to improve the visual quality of the generated images?

A) Employing a convolutional neural network (CNN) with strided convolutions for upsampling.
B) Increasing the latent space dimensionality of the generative model.
C) Training with a larger dataset of higher-resolution images.
D) Using a perceptual loss function that penalizes differences in high-level features.
E) Reducing the batch size during training.

4. Which NVIDIA SDK would be most appropriate for building a real-time, interactive avatar that can respond to voice commands and generate realistic facial expressions?

A) Avatar Cloud Engine (ACE)
B) Triton Inference Server
C) RAPIDS
D) Riva
E) NeMo

5. You are tasked with analyzing a large dataset of images used for training a generative A1 model. The dataset contains noisy labels and varying image quality. Which of the following preprocessing steps are MOST crucial for improving the performance of your model?

A) Implementing a label smoothing technique to mitigate the impact of noisy labels.
B) Using a pre-trained image quality assessment model to filter out low-quality images.
C) Converting all images to grayscale to reduce computational complexity.
D) Resizing all images to a fixed resolution (e.g., 256x256).
E) Applying aggressive data augmentation techniques like random rotations and flips.

Solutions: