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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Neural Network Basics | 4% | - Training and optimization methods - Basic concepts of neural networks - Common neural network structures |
| Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Language model and semantic understanding - Practical application - Machine translation, text generation and other technologies - Text processing and representation |
| Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development layout - Full-stack AI technology system - All-scenario AI solutions |
| Theoretical Knowledge and Applications of Image Processing | 26% | - Feature extraction and representation - Image preprocessing technology - Image classification, detection and segmentation - Typical application scenarios |
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech feature extraction - Speech recognition and synthesis - Speech signal processing foundation - Application cases |
| Overview of ModelArts | 4% | - Core functions and service modules - ModelArts positioning and architecture - Basic operation process |
| Speech Processing Lab Guide | 12% | - Application deployment and verification - Speech model building and tuning - Speech data processing practice |
| Image Processing Lab Guide | 12% | - Development environment setup - Image processing model development and deployment - Performance optimization and testing |
| Natural Language Processing Lab Guide | 10% | - NLP model training and evaluation - End-to-end application development - Text preprocessing and feature engineering |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. 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) Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
B) 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.
C) 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.
D) 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".
2. The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
A) TRUE
B) FALSE
3. 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
4. Which of the following applications are supported by ModelArts ExeML?
A) Predictive maintenance of manufacturing equipment
B) Dress code conformance monitoring in campuses
C) Automatic offering classification
D) Anomalous sound detection in production or security scenarios
5. The U-Net uses an upsampling mechanism and has a fully-connected layer.
A) TRUE
B) FALSE
Solutions:
| Question # 1 Answer: A,C,D | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A,B,C,D | Question # 5 Answer: B |
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