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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Overview of ModelArts | 4% | - ModelArts platform positioning and architecture - Development environment and tool usage - Data processing, training, deployment capabilities |
| Topic 2: Neural Network Basics | 4% | - Basic concepts of neural networks - Gradient descent and backpropagation - Activation functions and regularization - Multilayer Perceptron (MLP) |
| Topic 3: Natural Language Processing Theory and Applications | 10% | - BERT, GPT and pre-trained models - Text classification, NER, machine translation - Word representation and embedding - RNN, LSTM, GRU, Transformer architecture |
| Topic 4: Image Processing Lab Guide | 12% | - ModelArts-based image classification - Ascend-based deployment - Object detection and segmentation practice |
| Topic 5: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
| Topic 6: Natural Language Processing Lab Guide | 10% | - Application integration and deployment - ModelArts NLP model training and tuning - Text classification and NER implementation |
| Topic 7: Speech Processing Theory and Applications | 10% | - Text-to-Speech (TTS) technology - Acoustic and language modeling - Automatic Speech Recognition (ASR) - Speech signal characteristics and processing |
| Topic 8: Speech Processing Lab Guide | 12% | - ASR and TTS service development - Huawei Cloud Speech Interaction Service - ModelArts speech application deployment |
| Topic 9: Image Processing Theory and Applications | 26% | - Image classification, object detection, segmentation - OCR and visual application development - Image processing fundamentals - Convolutional Neural Networks (CNN) |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Question 1
AI application development process
Question 2
In image preprocessing, the function of () is to remove noise, etc.
Question 3
In the N-gram model, N refers to ignoring the influence of the previous words whose distance is greater than n. The larger N is, the richer the order information contained in the model, and the amount of calculation increases accordingly.
A. False
B. True
Question 4
For a 32-level grayscale image, how many levels can be distinguished from the darkest to the brightest at each sampling point?
A. 128
B. 256
C. 32
D. 64
Question 5
What text annotation tasks does ModelArts support?
A. Text Classification
B. Name recognition
C. All of the above are true
D. Text triples
Solutions:
| Question 1 Answer: Only visible for members | Question 2 Answer: Only visible for members | Question 3 Answer: B | Question 4 Answer: C | Question 5 Answer: C |
