Get real H13-321_V2.0-ENU exam questions for better preparation
Real Huawei H13-321_V2.0-ENU practice exam questions for easy pass!
Updated: Oct 18, 2026
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Huawei H13-321_V2.0-ENU Exam Overview:
| Certification Vendor: | Huawei |
|---|---|
| Exam Name: | Huawei Certified ICT Professional - AI-EI Developer V2.0 |
| Exam Number: | H13-321_V2.0 |
| Passing Score: | 600/1000 |
| Available Languages: | Chinese, English |
| Exam Format: | Drag and drop, True or false, Fill-in-the-blank, Multiple-answer questions, Single-answer questions |
| Related Certifications: | HCIA-AI HCIE-AI |
| Real Exam Qty: | 60 |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD 300 |
| Exam Duration: | 90 minutes |
| Recommended Training: | Huawei Cloud Developer Learning |
| Exam Registration: | Pearson VUE |
| Sample Questions: | Huawei H13-321_V2.0-ENU Sample Questions |
| Exam Way: | Computer-based test, online or at authorized test centers |
| Pre Condition: | HCIA-AI certification or equivalent knowledge is strongly recommended, not mandatory |
| Official Syllabus URL: | https://edu.huaweicloud.com/intl/en-us/certificationindex/career/aisd.html |
Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ModelArts Platform Development Experiment | 10% | |
| Topic 2: Theoretical Knowledge and Applications of Image Processing | 26% | - Image recognition, detection and segmentation - Image preprocessing and feature extraction - Convolutional neural networks - Computer vision overview |
| Topic 3: Natural Language Processing Lab Guide | 10% | - NLP project practice on ModelArts |
| Topic 4: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Text processing and representation - Common NLP tasks and applications - NLP core concepts |
| Topic 5: Speech Processing Lab Guide | 12% | - Speech application development on ModelArts |
| Topic 6: Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | |
| Topic 7: Image Processing Lab Guide | 12% | - ModelArts-based image development |
| Topic 8: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Acoustic and language models - Speech processing basics |
| Topic 9: Overview of ModelArts | 4% | - Development and deployment workflow - ModelArts platform architecture |
| Topic 10: Neural Network Basics | 4% | - Network training and optimization - Deep learning fundamentals - Artificial neural network principles |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Regarding ResNet_v1_50, which of the following statement is correct? (Multiple choice)
- A. Can be used for image classification
- B. Can be used for object detection
- C. The neural network structure is greater than or equal to 50 layers
- D. It is a CNN neural network model
Correct Answer: A,C,D 🗳️
N-gram and neural network language models differ in the way they calculate probability and train models.
- A. True
- B. False
Correct Answer: A 🗳️
In Huawei Cloud Face Recognition Service, users can create multiple face libraries without restrictions.
- A. True
- B. False
Correct Answer: A 🗳️
() cancels the two independent assumptions of HMM, treats label transfer and context input as global features, performs probability normalization globally, and solves the label bias and context feature missing problems of HMM.
Reveal Solution Discussion 0Correct Answer:
Conditional Random Field, CRF
Natural language processing can be defined as a discipline that studies language problems in human-to-human communication and human-to-computer communication. Natural language processing requires the development of models that represent language ability and language application, the establishment of a computational framework to implement such language models, the proposal of corresponding methods to continuously improve such language models, the design of various practical systems based on such language models, and the discussion of the evaluation techniques of these practical systems.
- A. True
- B. False
Correct Answer: A 🗳️
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