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問題 #15
You are tasked with visualizing the performance of a Generative A1 model across different categories of input dat a. You need to show both the accuracy and the number of data points in each category. Which visualization technique would be MOST effective for this purpose?
答案：A
解題說明：
A combination chart effectively displays two different types of data (accuracy and sample size) for each category. It allows for easy comparison and identification of trends. A pie chart is not suitable for showing multiple data points. Error bars don't effectively represent sample size in a standard bar chart. A scatter plot is more appropriate for showing the relationship between two continuous variables. A table lacks the visual impact of a chart.
問題 #16
You have developed a multimodal generative A1 model that generates images based on textual descriptions. You want to set up an automated system to monitor the model's performance and identify potential issues like degradation in image quality or introduction of biases over time. Which of the following components are essential for such a monitoring system? (Select THREE)
答案：A,B,E
解題說明：
Automated metrics calculation (B) and alerting mechanisms (C) are crucial for continuously monitoring performance and detecting issues. Storing and analyzing text prompts (E) can help identify patterns related to performance degradation or bias. While storing training data (A) can be useful, it's not essential for monitoring. A manual evaluation interface (D) can be helpful for occasional spot-checks, but it's not a core component of an automated monitoring system.
問題 #17
Consider the following Python code snippet used for evaluating a generative model. What potential issue exists with this code, and how would you rectify it to ensure a robust evaluation?
答案：A
解題說明：
Generative models produce different outputs each time due to their inherent stochasticity. Evaluating only once can result in a score that isn't representative. The code needs to be run multiple times with different random seeds, and the results averaged to obtain a more reliable Inception Score. While FID is a good metric (A), the core issue here is the lack of accounting for stochasticity. While a larger sample size can help improve reliability, addressing the randomness is most important. Inception score is a valid metric (D) although it has limitations. The given sample code is assumed to be incomplete, hence it will not have any issue on its own.
問題 #18
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?
答案：B
解題說明：
A complete ACE setup for realistic avatar interaction requires: Automatic Speech Recognition (ASR) to understand the user's query, Text-to-Speech (TTS) to generate the avatar's response, Audi02Emotion to infer emotional expressions from the text/audio, a 3D avatar model to represent the avatar visually, and an animation engine to drive facial expressions and lip synchronization. This combination ensures a lifelike and engaging user experience.
問題 #19
You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?
答案：E
解題說明：
TensorRT is specifically designed for optimizing deep learning models for inference on NVIDIA GPUs. It performs graph optimization, quantization, and kernel fusion to reduce latency and increase throughput. Increasing batch size can sometimes help, but may also lead to memory issues or increased latency for small batch sizes. A larger model will generally increase latency. Simpler loss functions and removing dropout affect training and generalization, not necessarily inference speed.
問題 #20
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