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

  • Exam Code: H13-321_V2.0-ENU
  • Exam Name: HCIP-AI-EI Developer V2.0
  • Updated: Sep 11, 2026
  • Q & A: 574 Questions and Answers

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

SectionWeightObjectives
AI Fundamentals and Neural Networks4%- Neural Network Basics
Speech Processing22%- Speech Processing Theory and Applications
- Speech Processing Lab Guide
Computer Vision (Image Processing)26%- Image Processing Lab Guide
- Image Processing Theory and Applications
  • 1. Image classification concepts
    • 2. Object detection basics
      Huawei AI Ecosystem and ModelArts16%- Huawei AI Strategy and Ecosystem Overview
      - ModelArts Lab Guide
      - ModelArts Platform Overview
      Natural Language Processing (NLP)20%- NLP Theory and Applications
      - NLP Lab Guide

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

      Question #1

      Image recognition tasks can be divided into three levels, from low to high according to the abstractness of the processing content?

      • A. Image analysis, image processing, image understanding
      • B. Image analysis, image understanding, image processing
      • C. Image understanding, image analysis, image processing
      • D. Image processing, image analysis, image understanding
      Answer: D
      Question #2

      Which of the following is not an application of hidden Markov model in speech recognition?

      • A. Form words and sentences based on labels
      • B. Phonemes and words combined according to the best sequence
      • C. Use the BW algorithm to find the optimal solution
      • D. Calculate the output sequence
      Answer: A
      Question #3

      Syntactic analysis generally uses a syntax tree to represent the analysis results.

      • A. True
      • B. False
      Answer: A
      Question #4

      The perceptron is doing a simple binary classification task.

      • A. True
      • B. False
      Answer: A
      Question #5

      Which of the following OpenCV functions can achieve image size scaling?

      • A. cv2.resize(img, (200, 300))
      • B. print (type(im ))
      • C. cv2.warpAffine(img , M, (w, h)
      • D. cv2.imread(\1ena.jpg\)
      Answer: A

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