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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Theoretical Knowledge and Applications of Image Processing | 26% | - Image preprocessing technology - Typical application scenarios - Image classification, detection and segmentation - Feature extraction and representation |
| Topic 2: Natural Language Processing Lab Guide | 10% | - Text preprocessing and feature engineering - End-to-end application development - NLP model training and evaluation |
| Topic 3: Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - All-scenario AI solutions - Huawei AI development layout - Full-stack AI technology system |
| Topic 4: Speech Processing Lab Guide | 12% | - Application deployment and verification - Speech data processing practice - Speech model building and tuning |
| Topic 5: Neural Network Basics | 4% | - Basic concepts of neural networks - Common neural network structures - Training and optimization methods |
| Topic 6: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech feature extraction - Application cases - Speech signal processing foundation - Speech recognition and synthesis |
| Topic 7: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Practical application - Language model and semantic understanding - Text processing and representation - Machine translation, text generation and other technologies |
| Topic 8: Image Processing Lab Guide | 12% | - Development environment setup - Performance optimization and testing - Image processing model development and deployment |
| Topic 9: Overview of ModelArts | 4% | - ModelArts positioning and architecture - Basic operation process - Core functions and service modules |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?
- A. 55
- B. 10
- C. 9
- D. 28
Correct Answer: D 🗳️
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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. FALSE
- B. TRUE
Correct Answer: B 🗳️
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Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
- A. It can handle small datasets with minimal labeling required.
- B. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
- C. It achieves fast convergence without using pre-trained models.
- D. It can process high-resolution images to enhance classification accuracy.
Correct Answer: B 🗳️
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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 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.
- 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 vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
Correct Answer: B,C,D 🗳️
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When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using:
Correct Answer:
python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods
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