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Last Updated: Aug 27, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Speech Processing Lab Guide | 12% | |
| Topic 2: Natural Language Processing Lab Guide | 10% | |
| Topic 3: Overview of Huawei's AI Development Strategy and Full-Stack All-Scenario AI Portfolio | 2% | |
| Topic 4: Natural Language Processing Theory and Applications | 10% | |
| Topic 5: Overview of ModelArts | 4% | |
| Topic 6: ModelArts Lab Guide | 10% | |
| Topic 7: Speech Processing Theory and Applications | 10% | |
| Topic 8: Image Processing Lab Guide | 12% | |
| Topic 9: Image Processing Theory and Applications | 26% | |
| Topic 10: Neural Network Basics | 4% |
1. The accuracy of the target detection position can be evaluated by the IoU value. The calculation method of IoU is to find the ratio of the union and intersection of the detection result and the true annotation.
A) True
B) False
2. The LDA algorithm assumes that the prior distribution of topics in documents and the prior distribution of words in topics both follow the Dirichlet distribution.
A) True
B) False
3. Which of the following options belong to the training servers provided by Shengteng Computing to customers and partners? (Multiple choice)
A) Atlas 300 model
B) Atlas 800 model
C) Atlas 800 model
D) Atlas 800 model
4. In the process of model training and evaluation, overfitting and underfitting often occur. Which of the following descriptions of overfitting and underfitting is correct?
A) Underfitting refers to a situation where the model performs well both during training and prediction.
B) Overfitting is reflected in the evaluation indicators, that is, the model performs well on the training set, but performs poorly on the test set and new data.
C) Underfitting refers to the situation where the model performs well during training but performs poorly during prediction.
D) Overfitting refers to a situation where the model does not fit the training data sufficiently.
5. What are the DNN model acceleration methods? (Multiple choice)
A) Multi-GPU Backpropagation
B) Asynchronous Stochastic Gradient Descent
C) Scaling up the model
D) Reduce the size of the model
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: A,B,C | Question # 4 Answer: B | Question # 5 Answer: A,B,D |
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