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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Network Basics | 4% | - Multilayer Perceptron (MLP) - Activation functions and regularization - Gradient descent and backpropagation - Basic concepts of neural networks |
| Topic 2: Natural Language Processing Theory and Applications | 10% | - BERT, GPT and pre-trained models - RNN, LSTM, GRU, Transformer architecture - Word representation and embedding - Text classification, NER, machine translation |
| Topic 3: Overview of ModelArts | 4% | - Development environment and tool usage - Data processing, training, deployment capabilities - ModelArts platform positioning and architecture |
| Topic 4: Image Processing Lab Guide | 12% | - Ascend-based deployment - ModelArts-based image classification - Object detection and segmentation practice |
| Topic 5: Natural Language Processing Lab Guide | 10% | - Application integration and deployment - ModelArts NLP model training and tuning - Text classification and NER implementation |
| Topic 6: Image Processing Theory and Applications | 26% | - Image processing fundamentals - Image classification, object detection, segmentation - Convolutional Neural Networks (CNN) - OCR and visual application development |
| Topic 7: Speech Processing Lab Guide | 12% | - Huawei Cloud Speech Interaction Service - ModelArts speech application deployment - ASR and TTS service development |
| Topic 8: Speech Processing Theory and Applications | 10% | - Automatic Speech Recognition (ASR) - Speech signal characteristics and processing - Acoustic and language modeling - Text-to-Speech (TTS) technology |
| Topic 9: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. Please match the following artificial intelligence algorithms and their corresponding fields
2. Through image algorithms, the foreground and background of a person in a photo taken with an ordinary mobile phone are segmented, and then () operations are performed on () parts to achieve a blur effect similar to that of a SLR camera.
A) Background, mean filter
B) Foreground, median filter
C) Foreground, mean filter
D) Background, median filter
3. LSTM does not belong to the RNN network.
A) False
B) True
4. () in CANN combines the operator fusion of the internal storage architecture of the Ascend chip to reduce operator calling overhead and memory movement, and improve performance.
5. __________ is a deeply optimized general operator library provided by Huawei, which can meet the needs of most mainstream vision and NLP neural networks.
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: Only visible for members | Question # 5 Answer: Only visible for members |
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