Huawei HCIP-AI-EI Developer V2.0 : H13-321_V2.0-ENU

  • Exam Code: H13-321_V2.0-ENU
  • Exam Name: HCIP-AI-EI Developer V2.0
  • Updated: Aug 26, 2026
  • Q & A: 574 Questions and Answers

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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Speech Processing Lab Guide12%- Speech application development on ModelArts
Topic 2: Neural Network Basics4%- Artificial neural network principles
- Deep learning fundamentals
- Network training and optimization
Topic 3: Theoretical Knowledge and Applications of Speech Processing10%- Speech recognition and synthesis
- Speech processing basics
- Acoustic and language models
Topic 4: Image Processing Lab Guide12%- ModelArts-based image development
Topic 5: Overview of ModelArts4%- Development and deployment workflow
- ModelArts platform architecture
Topic 6: Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio2%
Topic 7: Theoretical Knowledge and Applications of Image Processing26%- Image recognition, detection and segmentation
- Convolutional neural networks
- Image preprocessing and feature extraction
- Computer vision overview
Topic 8: Natural Language Processing Lab Guide10%- NLP project practice on ModelArts
Topic 9: Theoretical Knowledge and Applications of Natural Language Processing10%- Common NLP tasks and applications
- NLP core concepts
- Text processing and representation
Topic 10: ModelArts Platform Development Experiment10%

Huawei HCIP-AI-EI Developer V2.0 Sample Questions:

1. What are the types of pooling? (Multiple choice)

A) Max Pooling
B) Average Pooling
C) Convolution Pooling
D) Minimum Pooling


2. Token can be understood as a bridge between voice and text. Please sort the following tokens in ascending order of granularity. Phoneme Phoneme State State Word Word Syllable Syllable


3. Which of the following is the core idea of convolutional neural network? (Multiple choice)

A) Parameter Sharing
B) Fully connected
C) Local Perception
D) Zero padding


4. In Huawei's artificial intelligence product stack, it can be compared to NVIDIA 's CUDA and CUDNN ()


5. In image preprocessing, the function of () is to remove noise, etc.


Solutions:

Question # 1
Answer: A,B
Question # 2
Answer: Only visible for members
Question # 3
Answer: A,C
Question # 4
Answer: Only visible for members
Question # 5
Answer: Only visible for members

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