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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ModelArts Lab Guide | 10% | |
| Topic 2: Neural Network Basics | 4% | |
| Topic 3: Overview of ModelArts | 4% | |
| Topic 4: Natural Language Processing Theory and Applications | 10% | |
| Topic 5: Natural Language Processing Lab Guide | 10% | |
| Topic 6: Image Processing Lab Guide | 12% | |
| Topic 7: Speech Processing Lab Guide | 12% | |
| Topic 8: Overview of Huawei's AI Development Strategy and Full-Stack All-Scenario AI Portfolio | 2% | |
| Topic 9: Image Processing Theory and Applications | 26% | |
| Topic 10: Speech Processing Theory and Applications | 10% |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Question 1
Which of the following is the general format of a URL?
A. https://Endpoint/uri
B. https://www.XXXX
C. https://uri
D. https://Endpoint
Question 2
The v component of the HSV color space and the Y component of the YUV color space both represent the brightness of the image and can be separated as the result of grayscale conversion of the color image.
A. False
B. True
Question 3
Which of the following descriptions of gradient disappearance and gradient explosion are correct? (Multiple choice)
A. Gradient explosion usually occurs in deep networks and when the weight initialization value is too large
B. One of the reasons for the disappearance of gradients is the use of inappropriate activation functions in deep networks, such as sigmoid
C. The gradient disappearance problem can be effectively solved by simple gradient clipping
D. The gradient explosion problem can be effectively solved by simple gradient clipping
Question 4
Which of the following is the highlights of Huawei's deep learning framework MindSpore?
A. Through MindSpore 's own technological innovation and the collaborative optimization of MindSpore and Ascend processors, efficient running state is achieved and computing performance is greatly improved.
B. All of the above
C. By implementing the Al algorithm as code, the development environment becomes more friendly, significantly reducing model development time and lowering the model development threshold.
D. Natively adapt to every scenario including terminal, edge and cloud, and can collaborate on demand, with flexible deployment.
Question 5
What problems of mean filtering can Gaussian filtering overcome?
A. Slow calculation (the calculation principle is the same, so the calculation speed depends on the data size)
B. Large amount of calculation (same as C)
C. Denoising (the main function of Gaussian filtering is noise reduction)
D. Fuzzification
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: A,B,D | Question 4 Answer: B | Question 5 Answer: D |




