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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: EI Model Development Fundamentals | 15% | - HiLens Framework and Skills - Model Development Process - Development Environment Setup - EI Service and Architecture |
| Topic 2: Image Recognition Application Development | 15% | - Image Classification Models - Image Segmentation - Transfer Learning with Pre-trained Models - Object Detection Implementation |
| Topic 3: Deep Learning Fundamentals | 15% | - CNN and RNN Architectures - Optimization Algorithms - Training and Fine-tuning - Neural Network Basics |
| Topic 4: ModelArts Pro Development | 20% | - Inference Service Configuration - AutoML and Automatic Model Training - Model Deployment and Management - Hyperparameter Optimization |
| Topic 5: Natural Language Processing Application | 15% | - Named Entity Recognition - Language Model Fine-tuning - Text Preprocessing and Embedding - Text Classification Models |
| Topic 6: HiLens Platform Development | 20% | - Multi-modal Data Processing - Skill Development Framework - Edge Deployment Strategy - Real-time Inference Optimization |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Question 1
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A. The concatenated output is fed directly into the multi-headed attention mechanism.
B. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
C. The multi-head attention mechanism captures information about different subspaces within a sequence.
D. Each header's query, key, and value undergo a shared linear transformation to obtain them.
Question 2
-------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)
Question 3
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
A. Residual connections primarily add depth to the model but do not aid in gradient propagation.
B. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
C. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
D. Layer normalization accelerates model convergence and does not affect model stability.
Question 4
What are the adjacency relationships between two pixels whose coordinates are (21,13) and (22,12)?
A. Diagonal adjacency
B. No adjacency relationship
C. 4-adjacency
D. 8-adjacency
Question 5
The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.
A. TRUE
B. FALSE
Solutions:
| Question 1 Answer: B,C | Question 2 Answer: Only visible for members | Question 3 Answer: B | Question 4 Answer: A,D | Question 5 Answer: B |
