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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Language model and semantic understanding - Text processing and representation - Practical application - Machine translation, text generation and other technologies |
| Topic 2: Overview of ModelArts | 4% | - Basic operation process - ModelArts positioning and architecture - Core functions and service modules |
| Topic 3: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech signal processing foundation - Speech feature extraction - Application cases - Speech recognition and synthesis |
| Topic 4: Speech Processing Lab Guide | 12% | - Application deployment and verification - Speech model building and tuning - Speech data processing practice |
| Topic 5: Theoretical Knowledge and Applications of Image Processing | 26% | - Typical application scenarios - Feature extraction and representation - Image preprocessing technology - Image classification, detection and segmentation |
| Topic 6: Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - All-scenario AI solutions - Full-stack AI technology system - Huawei AI development layout |
| Topic 7: Natural Language Processing Lab Guide | 10% | - NLP model training and evaluation - Text preprocessing and feature engineering - End-to-end application development |
| Topic 8: Neural Network Basics | 4% | - Training and optimization methods - Common neural network structures - Basic concepts of neural networks |
| Topic 9: Image Processing Lab Guide | 12% | - Performance optimization and testing - Image processing model development and deployment - Development environment setup |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)
2. Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)
3. Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
A) When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
B) Color image input is multi-channel, whereas text input is single-channel.
C) CNNs are suitable for image tasks, but they perform poorly in text tasks.
D) For CNN, there is no difference in handling text or image tasks.
4. The mAP evaluation metric in object detection combines accuracy and recall.
A) TRUE
B) FALSE
5. In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
A) TRUE
B) FALSE
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: Only visible for members | Question # 3 Answer: A,B | Question # 4 Answer: B | Question # 5 Answer: A |
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