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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deep Learning Fundamentals | 15% | - Neural Network Basics - Optimization Algorithms - Training and Fine-tuning - CNN and RNN Architectures |
| HiLens Platform Development | 20% | - Edge Deployment Strategy - Skill Development Framework - Multi-modal Data Processing - Real-time Inference Optimization |
| ModelArts Pro Development | 20% | - AutoML and Automatic Model Training - Inference Service Configuration - Model Deployment and Management - Hyperparameter Optimization |
| EI Model Development Fundamentals | 15% | - Development Environment Setup - Model Development Process - EI Service and Architecture - HiLens Framework and Skills |
| Natural Language Processing Application | 15% | - Language Model Fine-tuning - Text Preprocessing and Embedding - Named Entity Recognition - Text Classification Models |
| Image Recognition Application Development | 15% | - Image Segmentation - Transfer Learning with Pre-trained Models - Object Detection Implementation - Image Classification Models |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Which of the following has never been used as a method in the history of NLP?
A) Rule-based method
B) Deep learning-based method
C) Statistics-based method
D) Recursion-based method
2. Among image preprocessing techniques, gamma correction is a common non-linear brightness adjustment method. Which of the following statements are true about the application and features of gamma correction?
A) When # > 1, the input low grayscale range is compressed, and the high grayscale range is stretched, enhancing the bright areas while compressing the dark areas.
B) Gamma correction applies only to grayscale images and does not apply to color images.
C) Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.
D) When # < 1, the input high grayscale range is compressed, and the low grayscale range is stretched, enhancing the dark areas while compressing the bright areas.
3. The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
A) FALSE
B) TRUE
4. Which of the following is a learning algorithm used for Markov chains?
A) Exhaustive search
B) Forward-backward algorithm
C) Baum-Welch algorithm
D) Viterbi algorithm
5. Which of the following methods are useful when tackling overfitting?
A) Data augmentation
B) Using more complex models
C) Using dropout during model training
D) Using parameter norm penalties
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A,C,D | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: A,C,D |
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