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Last Updated: Jul 31, 2026
No. of Questions: 62 Questions & Answers with Testing Engine
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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Natural Language Processing Application | 15% | - Text Preprocessing and Embedding - Language Model Fine-tuning - Named Entity Recognition - Text Classification Models |
| HiLens Platform Development | 20% | - Skill Development Framework - Real-time Inference Optimization - Edge Deployment Strategy - Multi-modal Data Processing |
| ModelArts Pro Development | 20% | - AutoML and Automatic Model Training - Hyperparameter Optimization - Model Deployment and Management - Inference Service Configuration |
| Image Recognition Application Development | 15% | - Transfer Learning with Pre-trained Models - Image Classification Models - Image Segmentation - Object Detection Implementation |
| Deep Learning Fundamentals | 15% | - Training and Fine-tuning - Optimization Algorithms - Neural Network Basics - CNN and RNN Architectures |
| EI Model Development Fundamentals | 15% | - Model Development Process - HiLens Framework and Skills - EI Service and Architecture - Development Environment Setup |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?
A) Grayscale stretching
B) Grayscale compression
C) Inversion
D) Gamma correction
2. Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
A) It can handle small datasets with minimal labeling required.
B) It can process high-resolution images to enhance classification accuracy.
C) It achieves fast convergence without using pre-trained models.
D) The self-attention mechanism is used to capture global features of images, improving classification accuracy.
3. -------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)
4. Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
A) CNNs are suitable for image tasks, but they perform poorly in text tasks.
B) Color image input is multi-channel, whereas text input is single-channel.
C) When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
D) For CNN, there is no difference in handling text or image tasks.
5. The jieba ------() method can be used for word segmentation.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: Only visible for members | Question # 4 Answer: B,C | Question # 5 Answer: Only visible for members |
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