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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Theoretical Knowledge and Applications of Image Processing | 26% | - Image preprocessing technology - Feature extraction and representation - Image classification, detection and segmentation - Typical application scenarios |
| Speech Processing Lab Guide | 12% | - Speech model building and tuning - Speech data processing practice - Application deployment and verification |
| Natural Language Processing Lab Guide | 10% | - End-to-end application development - NLP model training and evaluation - Text preprocessing and feature engineering |
| Overview of ModelArts | 4% | - ModelArts positioning and architecture - Core functions and service modules - Basic operation process |
| 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 |
| Image Processing Lab Guide | 12% | - Performance optimization and testing - Development environment setup - Image processing model development and deployment |
| Neural Network Basics | 4% | - Basic concepts of neural networks - Common neural network structures - Training and optimization methods |
| Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development layout - All-scenario AI solutions - Full-stack AI technology system |
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Application cases - Speech feature extraction - Speech signal processing foundation |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Question 1
The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
Question 2
Overfitting is a condition where a model is overly simple and excessive generalization errors occur.
A. FALSE
B. TRUE
Question 3
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.)
Question 4
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
B. The multi-head attention mechanism captures information about different subspaces within a sequence.
C. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
D. The concatenated output is fed directly into the multi-headed attention mechanism.
Question 5
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
A. FALSE
B. TRUE
Solutions:
| Question 1 Answer: Only visible for members | Question 2 Answer: A | Question 3 Answer: Only visible for members | Question 4 Answer: B,C | Question 5 Answer: B |


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