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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Neural Network Basics | 4% | - Basic concepts of neural networks - Common neural network structures - Training and optimization methods |
| Overview of ModelArts | 4% | - ModelArts positioning and architecture - Core functions and service modules - Basic operation process |
| Speech Processing Lab Guide | 12% | - Speech model building and tuning - Application deployment and verification - Speech data processing practice |
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Speech feature extraction - Application cases - Speech signal processing foundation |
| Natural Language Processing Lab Guide | 10% | - End-to-end application development - NLP model training and evaluation - Text preprocessing and feature engineering |
| Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Practical application - Machine translation, text generation and other technologies - Text processing and representation - Language model and semantic understanding |
| Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack AI technology system - Huawei AI development layout - All-scenario AI solutions |
| Image Processing Lab Guide | 12% | - Development environment setup - Image processing model development and deployment - Performance optimization and testing |
| Theoretical Knowledge and Applications of Image Processing | 26% | - Feature extraction and representation - Image classification, detection and segmentation - Image preprocessing technology - Typical application scenarios |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?
A) Grayscale stretching
B) Gamma correction
C) Grayscale compression
D) Inversion
2. Which of the following are object detection algorithms?
A) SSD
B) R-CNN
C) Faster-R-CNN
D) YOLO
3. In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
A) Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
B) Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
C) The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
D) Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
4. Which of the following has never been used as a method in the history of NLP?
A) Statistics-based method
B) Rule-based method
C) Deep learning-based method
D) Recursion-based method
5. The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
A) FALSE
B) TRUE
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A,B,C,D | Question # 3 Answer: B,C,D | Question # 4 Answer: D | Question # 5 Answer: B |






