Passing exam with H13-321_V2.0-ENU latest training materials
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Updated: Sep 08, 2026
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Huawei H13-321_V2.0-ENU Exam Overview:
| Certification Vendor: | Huawei |
|---|---|
| Exam Name: | HCIP-AI-EI Developer V2.0 |
| Exam Number: | H13-321_V2.0-ENU |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Related Certifications: | HCIE-AI HCIA-AI |
| Real Exam Qty: | 60 |
| Passing Score: | 600/1000 |
| Exam Format: | Scenario-based, Single-choice, True/False, Multiple-choice |
| Exam Price: | 300 USD |
| Available Languages: | Chinese, English |
| Recommended Training: | HCIP-AI-EI Developer V2.0 Official Training |
| Exam Registration: | Pearson VUE Registration Huawei Certification Official |
| Sample Questions: | Huawei H13-321_V2.0-ENU Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE / Huawei authorized test centers |
| Pre Condition: | Recommended: HCIA-AI certification or equivalent knowledge; 6+ months AI development experience |
| 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 |
|---|---|---|
| Overview of ModelArts | 4% | - ModelArts platform positioning and architecture - Development environment and tool usage - Data processing, training, deployment capabilities |
| Speech Processing Lab Guide | 12% | - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service - ASR and TTS service development |
| Image Processing Theory and Applications | 26% | - Image classification, object detection, segmentation - Image processing fundamentals - Convolutional Neural Networks (CNN) - OCR and visual application development |
| Neural Network Basics | 4% | - Gradient descent and backpropagation - Activation functions and regularization - Multilayer Perceptron (MLP) - Basic concepts of neural networks |
| Natural Language Processing Theory and Applications | 10% | - Word representation and embedding - RNN, LSTM, GRU, Transformer architecture - BERT, GPT and pre-trained models - Text classification, NER, machine translation |
| Natural Language Processing Lab Guide | 10% | - Text classification and NER implementation - ModelArts NLP model training and tuning - Application integration and deployment |
| Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
| Speech Processing Theory and Applications | 10% | - Acoustic and language modeling - Automatic Speech Recognition (ASR) - Speech signal characteristics and processing - Text-to-Speech (TTS) technology |
| Image Processing Lab Guide | 12% | - Ascend-based deployment - Object detection and segmentation practice - ModelArts-based image classification |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Question 1
Which of the following can be done using Tensorboard? (Multiple choice)
A. Visualize the changes in loss and accuracy during deep learning training
B. Data Annotation (Tensorboard is not an annotation tool)
C. Visualizing high-dimensional distributions
D. Automatic parameter tuning (what the algorithm needs to do)
Question 2
The back propagation algorithm (BP algorithm) mainly has two stages: forward propagation stage and back propagation stage, which completes the parameter update work in the neural network.
A. False
B. True
Question 3
The bag-of - words model is the earliest text vectorization method that uses words as the basic processing unit.
Which of the following options is not a defect of the bag-of-words model?
A. Based on the distribution hypothesis
B. There is a semantic gap
C. The curse of dimensionality
D. Unable to preserve order information
Question 4
Speech synthesis is also known as speech conversion.
A. False
B. True
Question 5
What is the unique gate structure of GRU?
A. Forget Gate
B. Update Gate
C. Output Gate
D. Input Gate
Solutions:
| Question 1 Answer: A,C | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: B |
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