Prepare with Huawei H13-321_V2.0 exam torrent, pass for sure
Last Updated: Oct 10, 2026
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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Natural Language Processing (NLP) | 20% | - NLP Theory and Applications - NLP Lab Guide |
| Topic 2: Computer Vision (Image Processing) | 26% | - Image Processing Lab Guide - Image Processing Theory and Applications
|
| Topic 3: Speech Processing | 22% | - Speech Processing Lab Guide - Speech Processing Theory and Applications |
| Topic 4: Huawei AI Ecosystem and ModelArts | 16% | - ModelArts Platform Overview - ModelArts Lab Guide - Huawei AI Strategy and Ecosystem Overview |
| Topic 5: AI Fundamentals and Neural Networks | 4% | - Neural Network Basics |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
() in CANN combines the operator fusion of the internal storage architecture of the Ascend chip to reduce operator calling overhead and memory movement, and improve performance.
Reveal Solution Discussion 0Correct Answer:
Fusion engine
When taking a selfie, what transformation does the image displayed by the front camera of the mobile phone need to undergo to become a real scene image?
- A. Horizontal mirror
- B. Translation
- C. Vertical Mirror
- D. Rotation
Correct Answer: A 🗳️
From which channels can image classification data for automatic learning be obtained? (Multiple choice)
- A. Import from ModelArts Marketplace
- B. Upload from OBS
- C. Load from other cloud service providers' storage services
- D. Upload from the web page
Correct Answer: A,B,C 🗳️
Which of the following methods can improve the fitting ability of neural networks? (Multiple choice)
- A. Add a certain number of hidden layers
- B. Adding residual blocks to neural networks
- C. Use non-linear activation functions
- D. Reduce the number of neurons
Correct Answer: A,B,C 🗳️
Please associate the following description with the corresponding tag
Reveal Solution Discussion 0Correct Answer:
Use explicit programming to solve problems (rule-based methods) For a large amount of unknown labeled data, divide the data into multiple categories according to the intrinsic similarity of the data (clustering) In a task, try to let the learner automatically help a small amount of labeled data to train the model directly on a large amount of unlabeled data (semi-supervised learning) The rules are automatically learned by the machine (machine learning)
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