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Updated: Aug 15, 2026
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Huawei H13-321_V2.0 Exam Overview:
| Certification Vendor: | Huawei |
| Exam Name: | HCIP-AI-EI Developer V2.0 |
| Exam Number: | H13-321_V2.0-ENU |
| Exam Price: | 300 USD |
| Available Languages: | English, Chinese |
| Real Exam Qty: | 60 |
| Exam Format: | Single-choice, Multiple-choice, True/False, Scenario-based |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Related Certifications: | HCIA-AI HCIE-AI |
| Passing Score: | 600/1000 |
| Recommended Training: | HCIP-AI-EI Developer V2.0 Official Training |
| Exam Registration: | Huawei Certification Official Pearson VUE Registration |
| Sample Questions: | Huawei H13-321_V2.0 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 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Image Processing Theory and Applications | 26% | - OCR and visual application development - Image processing fundamentals - Image classification, object detection, segmentation - Convolutional Neural Networks (CNN) |
| Topic 2: Natural Language Processing Theory and Applications | 10% | - Word representation and embedding - BERT, GPT and pre-trained models - Text classification, NER, machine translation - RNN, LSTM, GRU, Transformer architecture |
| Topic 3: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development strategy - Full-stack and all-scenario AI technology layout |
| Topic 4: Neural Network Basics | 4% | - Multilayer Perceptron (MLP) - Activation functions and regularization - Basic concepts of neural networks - Gradient descent and backpropagation |
| Topic 5: Natural Language Processing Lab Guide | 10% | - Text classification and NER implementation - ModelArts NLP model training and tuning - Application integration and deployment |
| Topic 6: Speech Processing Theory and Applications | 10% | - Automatic Speech Recognition (ASR) - Acoustic and language modeling - Text-to-Speech (TTS) technology - Speech signal characteristics and processing |
| Topic 7: Overview of ModelArts | 4% | - Development environment and tool usage - ModelArts platform positioning and architecture - Data processing, training, deployment capabilities |
| Topic 8: Image Processing Lab Guide | 12% | - Ascend-based deployment - Object detection and segmentation practice - ModelArts-based image classification |
| Topic 9: Speech Processing Lab Guide | 12% | - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service - ASR and TTS service development |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. Which of the following algorithms can be applied to text classification? (Multiple choice)
A) Linear regression
B) Naive Bayes
C) TextCNN
D) SVM
2. Which of the following is not a common method for text vectorization?
A) DM
B) DBOW
C) EM
D) CBOW
3. In the process of model training and evaluation, overfitting and underfitting often occur. Which of the following descriptions of overfitting and underfitting is correct?
A) Underfitting refers to a situation where the model performs well both during training and prediction.
B) Overfitting is reflected in the evaluation indicators, that is, the model performs well on the training set, but performs poorly on the test set and new data.
C) Overfitting refers to a situation where the model does not fit the training data sufficiently.
D) Underfitting refers to the situation where the model performs well during training but performs poorly during prediction.
4. Which of the following descriptions of the depth and width of deep learning neural networks are correct? (Multiple choice)
A) The more layers of the neural network, the better
B) When there are only two hidden layers, no matter how many neurons are added, the performance improvement of the network is very limited. However, if one more hidden layer is added, the performance of the model will be significantly improved.
C) Experiments have shown that increasing the depth of the network is much more efficient than increasing the width of the network.
D) Experiments have shown that increasing network width is much more efficient than increasing network depth.
5. Cifar10 is a built-in dataset of keras.
A) True
B) False
Solutions:
| Question # 1 Answer: B,C,D | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: B,C | Question # 5 Answer: A |
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