Try and practice the latest Huawei : H13-321_V2.0 real questions & answers
Updated: Aug 25, 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 Format: | Scenario-based, True/False, Multiple-choice, Single-choice |
| Exam Price: | 300 USD |
| Exam Duration: | 90 minutes |
| Available Languages: | Chinese, English |
| Real Exam Qty: | 60 |
| Related Certifications: | HCIA-AI HCIE-AI |
| Certificate Validity Period: | 3 years |
| 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: Neural Network Basics | 4% | - Multilayer Perceptron (MLP) - Basic concepts of neural networks - Activation functions and regularization - Gradient descent and backpropagation |
| Topic 2: Natural Language Processing Theory and Applications | 10% | - RNN, LSTM, GRU, Transformer architecture - BERT, GPT and pre-trained models - Word representation and embedding - Text classification, NER, machine translation |
| 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: Image Processing Theory and Applications | 26% | - Image processing fundamentals - OCR and visual application development - Image classification, object detection, segmentation - Convolutional Neural Networks (CNN) |
| Topic 5: Overview of ModelArts | 4% | - ModelArts platform positioning and architecture - Data processing, training, deployment capabilities - Development environment and tool usage |
| Topic 6: Image Processing Lab Guide | 12% | - Object detection and segmentation practice - Ascend-based deployment - ModelArts-based image classification |
| Topic 7: Speech Processing Theory and Applications | 10% | - Automatic Speech Recognition (ASR) - Acoustic and language modeling - Speech signal characteristics and processing - Text-to-Speech (TTS) technology |
| Topic 8: Natural Language Processing Lab Guide | 10% | - Application integration and deployment - Text classification and NER implementation - ModelArts NLP model training and tuning |
| Topic 9: Speech Processing Lab Guide | 12% | - ModelArts speech application deployment - ASR and TTS service development - Huawei Cloud Speech Interaction Service |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Question 1
What effect can mean filtering have? (Multiple choice)
A. Extraction target
B. Fuzzification
C. Extracting edges
D. Noise Removal
Question 2
Which of the following descriptions of the "boundedness" factors that need to be considered when designing activation functions are correct? (Multiple choice)
A. If the activation function is bounded, the loss function associated with a single-layer model is convex
B. Bounded functions have been shown to generalize better in some cases
C. If the activation function is bounded, gradient-based training methods tend to be more stable
D. If the activation function is unbounded, training is usually more efficient, but the training is prone to divergence. In this case, the learning rate can be appropriately reduced.
Question 3
Which of the following is the correct solution process for maximum likelihood estimation?
1. Log-likelihood function
2. Probability density function
3. Likelihood function
4. Derivative and set the equation to 0
5. Solve equations
A. 1-3-4-2-5
B. 2-3-1-4-5
C. 4-1-3-2-5
D. 2-1-3-4-5
Question 4
The larger the template size, the better the denoising effect of the mean filter. When performing denoising, a large-sized template should be selected for mean filtering.
A. False
B. True
Question 5
How to label image classification tasks?
A. Use a rectangle to frame the target
B. Use a circle to frame the target
C. Use an ellipse to frame the target
D. Enter a category label for the image
Solutions:
| Question 1 Answer: B,D | Question 2 Answer: C,D | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: D |
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