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Updated: Aug 02, 2026
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| Certification Vendor: | Huawei |
| Exam Name: | HCIP-AI-EI Developer V2.5 |
| Exam Number: | H13-321_V2.5 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 60-70 |
| Exam Format: | Multiple Choice, True or False, Drag and Drop, Simulation |
| Available Languages: | English, Chinese |
| Related Certifications: | HCIA-AI-EI Developer HCIE-AI-EI Developer |
| Exam Price: | USD 300 |
| Passing Score: | 600/1000 |
| Certificate Validity Period: | 3 years |
| Sample Questions: | Huawei H13-321_V2.5 Sample Questions |
| Exam Way: | Online/Offline Testing Center |
| Pre Condition: | Recommended: HCIA-AI-EI Developer certification or equivalent knowledge of deep learning and Huawei cloud services |
| Official Syllabus URL: | https://support.huawei.com/hedex/hdx.do?docid=DOC1000203314 |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Image Recognition Application Development | 15% | - Image Segmentation - Transfer Learning with Pre-trained Models - Object Detection Implementation - Image Classification Models |
| Topic 2: Natural Language Processing Application | 15% | - Named Entity Recognition - Language Model Fine-tuning - Text Preprocessing and Embedding - Text Classification Models |
| Topic 3: HiLens Platform Development | 20% | - Edge Deployment Strategy - Multi-modal Data Processing - Real-time Inference Optimization - Skill Development Framework |
| Topic 4: Deep Learning Fundamentals | 15% | - Training and Fine-tuning - Optimization Algorithms - Neural Network Basics - CNN and RNN Architectures |
| Topic 5: EI Model Development Fundamentals | 15% | - HiLens Framework and Skills - EI Service and Architecture - Model Development Process - Development Environment Setup |
| Topic 6: ModelArts Pro Development | 20% | - AutoML and Automatic Model Training - Inference Service Configuration - Model Deployment and Management - Hyperparameter Optimization |
1. The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
2. Which of the following is not an acoustic feature of speech?
A) Amplitude
B) Duration
C) Semantics
D) Frequency
3. Which of the following methods are useful when tackling overfitting?
A) Data augmentation
B) Using more complex models
C) Using dropout during model training
D) Using parameter norm penalties
4. Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A) The multi-head attention mechanism captures information about different subspaces within a sequence.
B) The concatenated output is fed directly into the multi-headed attention mechanism.
C) Each header's query, key, and value undergo a shared linear transformation to obtain them.
D) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
5. What are the advantages of deep learning-based speech recognition algorithms?
A) No data training
B) Automated feature extraction
C) End-to-end task processing
D) Forced alignment of annotated data
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: C | Question # 3 Answer: A,C,D | Question # 4 Answer: A,D | Question # 5 Answer: B,C |
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