Updated: Jul 31, 2026
No. of Questions: 62 Questions & Answers with Testing Engine
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| Certification Vendor: | Huawei |
| Exam Name: | Huawei HCIP-AI-EI Developer V2.5 Certification Exam |
| Exam Number: | H13-321_V2.5 |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Related Certifications: | HCIA-AI HCIP-AI-EI Developer |
| Available Languages: | English, Simplified Chinese |
| Exam Format: | Single choice, Multiple choice, True/False, Drag and drop |
| Passing Score: | 600/1000 |
| Real Exam Qty: | 60-70 |
| Exam Price: | USD 200 (varies by region) |
| Recommended Training: | Huawei HCIP-AI-EI Developer Training Huawei Cloud Academy AI Courses |
| Exam Registration: | Huawei Certification Portal Huawei Talent |
| Sample Questions: | Huawei H13-321_V2.5 Sample Questions |
| Exam Way: | Online proctored exam or authorized test center |
| Pre Condition: | Basic knowledge of AI, machine learning, and programming (Python recommended). HCIA-AI or equivalent knowledge is recommended. |
| Official Syllabus URL: | https://certification.huawei.com |
| Section | Objectives |
|---|---|
| Deep Learning | - Model Training and Optimization - Neural Network Fundamentals - CNN and RNN Architectures |
| Data Processing | - Data Labeling and Preparation - Feature Engineering - Data Collection and Cleaning |
| Model Development with Huawei ModelArts | - ModelArts Platform Overview - Training Models on ModelArts - AutoML Capabilities |
| Model Deployment and Operations | - Monitoring and Maintenance - Inference Services - Model Deployment Strategies |
| Huawei AI Ecosystem Tools | - Huawei Cloud AI Services - AI Development Toolchain - MindSpore Framework Basics |
| AI Fundamentals | - Introduction to Artificial Intelligence - Common AI Use Cases in Industry - AI Development Lifecycle |
| Machine Learning | - Unsupervised Learning
|
| AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts - Building AI Applications - AI Service Integration |
1. The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
A) FALSE
B) TRUE
2. The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
A) FALSE
B) TRUE
3. What type of task is viewed when using the Seq2Seq model in speech recognition?
A) Clustering task
B) Regression task
C) Dimensionality reduction task
D) Classification task
4. Which of the following is not an algorithm for training word vectors?
A) Word2Vec
B) BERT
C) TextCNN
D) FastText
5. Which of the following methods are useful when tackling overfitting?
A) Using parameter norm penalties
B) Data augmentation
C) Using more complex models
D) Using dropout during model training
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: A,B,D |
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