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NVIDIA Generative AI Multimodal Sample Questions:

1. Explainable A1 (XAI) is crucial when deploying multimodal models, especially in high-stakes scenarios. Which technique is MOST appropriate for understanding the relative importance of different modalities (e.g., image vs. text) in a multimodal classification task?

A) Calculating the gradient of the output with respect to the input text embeddings.
B) Ablation studies, where each modality is individually removed during inference and the change in model performance is measured.
C) Randomly shuffling the pixels in the input images and observing the change in model performance.
D) Performing a principal component analysis (PCA) on the combined feature vectors.
E) Visualizing the attention weights in the image processing component.

2. You have developed a multimodal model that predicts stock prices using news articles (text), historical stock data (time-series), and company financial reports (tabular data). You want to deploy this model using NVIDIA Triton Inference Server. Assume you have preprocessed the data and have individual models for each modality. What is the recommended approach to configure Triton for efficient and scalable multimodal inference?

A) Convert all models to TensorRT for maximum inference speed, even if it compromises accuracy due to quantization.
B) Create a single Triton model that encapsulates the entire multimodal pipeline, including preprocessing, individual modality models, and fusion logic, using the Ensemble Modeling feature.
C) Deploy the text model using ONNX Runtime, the time-series model using TensorFlow, and the tabular data model using PyTorch, and handle fusion manually.
D) Deploy each modality-specific model as a separate Triton model and use a load balancer to distribute requests across the models.
E) Deploy each modality-specific model as a separate Triton model and handle the fusion logic in the client application.

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

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

4. You are working on a project involving generating photorealistic images of human faces using a generative model. Ethical considerations are paramount. Which of the following practices are MOST important to incorporate into your development workflow to mitigate potential biases and misuse?

A) Focusing solely on improving the technical performance of the model, ignoring potential ethical concerns, and releasing the model as open-source to promote innovation.
B) Implementing strict controls over the types of images the model can generate, limiting its use to specific applications, and restricting access to the model to a small group of trusted individuals.
C) Training the model on a diverse and representative dataset, implementing mechanisms to detect and mitigate biases in the generated images, and providing transparency about the limitations and potential risks of the technology.
D) Prioritizing speed and efficiency in the development process, neglecting to address potential biases, and deploying the model without conducting thorough testing or evaluation.
E) Using synthetic data for training to avoid any potential privacy concerns related to real-world data, ignoring potential biases in the synthetic data, and claiming that the model is completely unbiased.

5. You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

A) Only utilize pre-trained open-source models
B) Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
C) Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.
D) Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.
E) Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.

Solutions: