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NVIDIA Generative AI Multimodal Sample Questions (Q154-Q159):

NEW QUESTION # 154
You're tasked with building a system that generates personalized exercise recommendations based on user's text descriptions of their fitness goals and images of their current physical condition. Due to privacy concerns, you cannot directly access the user's raw images or text after initial processing. What technique can allow you to continue to train the model while respecting these privacy constraints?.

A. Federated Learning
B. Transfer Learning
C. Data Augmentation
D. Generative Adversarial Networks (GANs)
E. Reinforcement Learning

Answer: A

Explanation:
Federated learning allows training a model across multiple decentralized devices or servers holding local data samples, without exchanging them. This is perfect for privacy-sensitive scenarios as the raw data remains on the user's device. Transfer learning relies on pre- trained models, data augmentation modifies existing data, GANs generate new data (but still require initial data access), and reinforcement learning optimizes actions through interaction with an environment.

NEW QUESTION # 155
You are tasked with evaluating the trustworthiness of a multimodal A1 model that predicts diagnoses based on medical images and patient history text. Which of the following evaluation metrics or techniques are MOST relevant for assessing the model's trustworthiness in this critical application?

A. Robustness testing by introducing adversarial perturbations to the input data.
B. Attribution methods (e.g., Grad-CAM) to visualize which parts of the image and text the model focuses on.
C. Accuracy and F1-score on a held-out test set.
D. Measuring inference throughput (samples per second).
E. Calibration error, measuring the alignment between predicted probabilities and actual outcomes.

Answer: A,B,E

Explanation:
Trustworthiness goes beyond simple accuracy. Calibration error assesses how well the model's predicted probabilities reflect the true likelihood of the diagnosis. Attribution methods provide insights into the model's reasoning process, helping to identify potential biases or reliance on irrelevant features. Robustness testing assesses the model's sensitivity to noise and adversarial attacks, which can indicate vulnerability to manipulation. Accuracy and Fl-score are important but insufficient for trustworthiness. Throughput is a performance metric, not a trustworthiness metric.

NEW QUESTION # 156
You are deploying a multimodal model that uses both video and audio data for real-time emotion recognition. The model is deployed on an edge device with limited computational resources. Which optimization techniques would be MOST effective for reducing latency and improving the model's inference speed on the edge device?

A. Transmitting the video and audio data to a cloud server for inference.
B. Using full precision (FP32) for all model operations.
C. Increasing the model's complexity to improve accuracy.
D. Quantizing the model to a lower precision (e.g., INT8) and pruning less important connections.
E. Increasing the resolution of the video input.

Answer: D

Explanation:
Quantization to a lower precision (e.g., INT8) significantly reduces the model size and computational requirements, leading to faster inference speeds on edge devices. Pruning further reduces the model's complexity. Increasing model complexity (A) or using FP32 (B) would increase latency. Offloading to the cloud (D) introduces network latency. Increasing video resolution (E) increases the computational load.

NEW QUESTION # 157
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)

A. It subtracts the 'skip' tensor from the 'up' tensor.
B. It performs an element-wise addition of the 'up' and 'skip' tensors.
C. It concatenates the 'up' and 'skip' tensors along the channel dimension.
D. It performs a matrix multiplication between the 'up' and 'skip' tensors.
E. It multiplies the 'up' and 'skip' tensors element-wise.

Answer: C

Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.

NEW QUESTION # 158
You're designing a multimodal A1 system for autonomous driving that integrates data from cameras (images), LiDAR (point clouds), radar (time-series), and GPS (geospatial). The system needs to make real-time decisions in complex urban environments. Which hardware and software components are crucial for achieving low latency and high accuracy in data processing and fusion?

A. Real-time operating system (RTOS) for deterministic execution and minimal jitter.
B. All of the above.
C. NVIDIA GPUs with CUDA for accelerated processing of image and point cloud data.
D. Sensor fusion algorithms optimized for GPU acceleration.
E. High-bandwidth, low-latency communication interfaces (e.g., PCle Gen4/5) for data transfer between sensors and processing units.

Answer: B

Explanation:
All the options are critical for real-time performance. GPUs accelerate processing, high-bandwidth interfaces enable fast data transfer, RTOS ensures deterministic execution, and optimized sensor fusion algorithms maximize accuracy.

NEW QUESTION # 159
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