Exam Name: HCIP-AI-EI Developer V2.0
Certification Provider: Huawei
Corresponding Certification: HCIP AI EI
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Instant Download Huawei : H13-321_V2.0-ENU Questions & Answers as PDF & Test Engine
Updated: Aug 07, 2026
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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Theoretical Knowledge and Applications of Image Processing | 26% | - Convolutional neural networks - Image preprocessing and feature extraction - Computer vision overview - Image recognition, detection and segmentation |
| Topic 2: Overview of ModelArts | 4% | - Development and deployment workflow - ModelArts platform architecture |
| Topic 3: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Common NLP tasks and applications - NLP core concepts - Text processing and representation |
| Topic 4: Image Processing Lab Guide | 12% | - ModelArts-based image development |
| Topic 5: Theoretical Knowledge and Applications of Speech Processing | 10% | - Acoustic and language models - Speech processing basics - Speech recognition and synthesis |
| Topic 6: Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | |
| Topic 7: ModelArts Platform Development Experiment | 10% | |
| Topic 8: Speech Processing Lab Guide | 12% | - Speech application development on ModelArts |
| Topic 9: Neural Network Basics | 4% | - Artificial neural network principles - Network training and optimization - Deep learning fundamentals |
| Topic 10: Natural Language Processing Lab Guide | 10% | - NLP project practice on ModelArts |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. The more parameters a deep neural network has, the better it is at capturing the characteristic information of the data.
A) True
B) False
2. Digital images can perform basic mathematical operations, that is, addition, subtraction, multiplication and division.
A) True
B) False
3. What are the characteristics of ModelArts preset models compared with custom model development? (Multiple choice)
A) A large amount of data is required
B) Able to converge faster
C) No need to develop your own code
D) Need to label data
4. Which of the following descriptions of the "boundedness" factors that need to be considered when designing activation functions are correct? (Multiple choice)
A) Bounded functions have been shown to generalize better in some cases
B) If the activation function is unbounded, training is usually more efficient, but the training is prone to divergence. In this case, the learning rate can be appropriately reduced.
C) If the activation function is bounded, the loss function associated with a single-layer model is convex
D) If the activation function is bounded, gradient-based training methods tend to be more stable
5. What are the common feature selection methods for text classification? (Multiple choice)
A) Information Gain
B) Chi-Square Test
C) Mutual Information
D) None of the above
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: C,D | Question # 4 Answer: B,D | Question # 5 Answer: A,B,C |
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