Purchase Huawei : H13-321_V2.5 Exam Materials and then pass exam easily
Updated: Jul 18, 2026
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Huawei H13-321_V2.5 Exam Overview:
| Certification Vendor: | Huawei |
| Exam Name: | HCIP-AI-EI Developer V2.5 |
| Exam Number: | H13-321_V2.5 |
| Certificate Validity Period: | 3 years |
| Available Languages: | Chinese, English |
| Real Exam Qty: | 60 |
| Exam Duration: | 90 minutes |
| Exam Price: | $200 USD |
| Passing Score: | 600/1000 |
| Related Certifications: | HCIA-AI HCIE-AI |
| Exam Format: | Single-choice, Multiple-choice, Judgment, Scenario-based |
| Recommended Training: | HCIP-AI-EI Developer V2.5 Official Training Course ModelArts Official Learning Path |
| Exam Registration: | Huawei Certification Official Website Pearson VUE Registration |
| Sample Questions: | Huawei H13-321_V2.5 Sample Questions |
| Exam Way: | Online proctored or onsite at authorized test centers |
| Pre Condition: | Recommended to have HCIA-AI certification or equivalent knowledge; 6–12 months of AI development experience |
| Official Syllabus URL: | https://edu.huaweicloud.com/intl/en-us/certificationindex/career/aisd.html |
Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Image Processing Lab Guide | 12% | - Performance optimization and testing - Image processing model development and deployment - Development environment setup |
| Topic 2: Overview of ModelArts | 4% | - Basic operation process - Core functions and service modules - ModelArts positioning and architecture |
| Topic 3: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech feature extraction - Speech recognition and synthesis - Application cases - Speech signal processing foundation |
| Topic 4: Theoretical Knowledge and Applications of Image Processing | 26% | - Feature extraction and representation - Image classification, detection and segmentation - Typical application scenarios - Image preprocessing technology |
| Topic 5: Speech Processing Lab Guide | 12% | - Application deployment and verification - Speech data processing practice - Speech model building and tuning |
| Topic 6: Neural Network Basics | 4% | - Common neural network structures - Training and optimization methods - Basic concepts of neural networks |
| Topic 7: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Practical application - Language model and semantic understanding - Text processing and representation - Machine translation, text generation and other technologies |
| Topic 8: Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development layout - All-scenario AI solutions - Full-stack AI technology system |
| Topic 9: Natural Language Processing Lab Guide | 10% | - End-to-end application development - Text preprocessing and feature engineering - NLP model training and evaluation |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?
A) 10
B) 9
C) 28
D) 55
2. Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
A) FALSE
B) TRUE
3. Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?
A) Video analytics
B) Object detection
C) Speech recognition
D) Image classification
4. Which of the following ModelArts training parameters is used to customize hyperparameters?
A) Hyperparameter
B) Compute Nodes
C) Resource Pool
D) Algorithm Type
5. In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
A) Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
B) Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
C) Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
D) The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: A,B,C,D | Question # 4 Answer: A | Question # 5 Answer: A,B,D |
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