- Exam Code: H13-321_V2.0-ENU
- Exam Name: HCIP-AI-EI Developer V2.0
- Updated: Aug 01, 2026
- Q & A: 574 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Image Processing Lab Guide | 12% | - Ascend-based deployment - Object detection and segmentation practice - ModelArts-based image classification |
| Topic 2: Overview of ModelArts | 4% | - Data processing, training, deployment capabilities - ModelArts platform positioning and architecture - Development environment and tool usage |
| Topic 3: Natural Language Processing Lab Guide | 10% | - Text classification and NER implementation - ModelArts NLP model training and tuning - Application integration and deployment |
| Topic 4: Natural Language Processing Theory and Applications | 10% | - BERT, GPT and pre-trained models - RNN, LSTM, GRU, Transformer architecture - Text classification, NER, machine translation - Word representation and embedding |
| Topic 5: Speech Processing Lab Guide | 12% | - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service - ASR and TTS service development |
| Topic 6: Image Processing Theory and Applications | 26% | - Convolutional Neural Networks (CNN) - OCR and visual application development - Image classification, object detection, segmentation - Image processing fundamentals |
| Topic 7: Neural Network Basics | 4% | - Basic concepts of neural networks - Activation functions and regularization - Multilayer Perceptron (MLP) - Gradient descent and backpropagation |
| Topic 8: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development strategy - Full-stack and all-scenario AI technology layout |
| Topic 9: Speech Processing Theory and Applications | 10% | - Acoustic and language modeling - Automatic Speech Recognition (ASR) - Speech signal characteristics and processing - Text-to-Speech (TTS) technology |
1. After affine transformation, the square in the image may not still be a square.
A) False
B) True
2. The XOR problem refers to the prediction of an output obtained through an XOR logic gate for two given binary inputs. When the inputs are not equal, the output is 1, otherwise the output is 0. ()
A) False
B) True
3. Which of the following descriptions of the depth and width of deep learning neural networks are correct? (Multiple choice)
A) Experiments have shown that increasing network width is much more efficient than increasing network depth.
B) The more layers of the neural network, the better
C) Experiments have shown that increasing the depth of the network is much more efficient than increasing the width of the network.
D) 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.
4. What are the organs of pronunciation? (Multiple choice)
A) Throat
B) Below the throat
C) Above the throat
D) Larynx
5. What types of image annotations does ModelArts support?
A) All of the above are true
B) Image Classification
C) Image Segmentation
D) Object Detection
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: C,D | Question # 4 Answer: B,C,D | Question # 5 Answer: A |
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