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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ModelArts Pro Development | 20% | - Hyperparameter Optimization - Inference Service Configuration - Model Deployment and Management - AutoML and Automatic Model Training |
| Topic 2: Image Recognition Application Development | 15% | - Object Detection Implementation - Image Segmentation - Transfer Learning with Pre-trained Models - Image Classification Models |
| Topic 3: Natural Language Processing Application | 15% | - Named Entity Recognition - Language Model Fine-tuning - Text Preprocessing and Embedding - Text Classification Models |
| Topic 4: HiLens Platform Development | 20% | - Skill Development Framework - Real-time Inference Optimization - Multi-modal Data Processing - Edge Deployment Strategy |
| Topic 5: EI Model Development Fundamentals | 15% | - EI Service and Architecture - HiLens Framework and Skills - Model Development Process - Development Environment Setup |
| Topic 6: Deep Learning Fundamentals | 15% | - Training and Fine-tuning - Neural Network Basics - Optimization Algorithms - CNN and RNN Architectures |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Question #1
Which of the following is not an algorithm for training word vectors?
A. BERT
B. TextCNN
C. Word2Vec
D. FastText
Question #2
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A. The multi-head attention mechanism captures information about different subspaces within a sequence.
B. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
C. Each header's query, key, and value undergo a shared linear transformation to obtain them.
D. The concatenated output is fed directly into the multi-headed attention mechanism.
Question #3
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. 10
B. 9
C. 55
D. 28
Question #4
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
A. CNNs are suitable for image tasks, but they perform poorly in text tasks.
B. Color image input is multi-channel, whereas text input is single-channel.
C. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
D. For CNN, there is no difference in handling text or image tasks.
Question #5
Which of the following has never been used as a method in the history of NLP?
A. Statistics-based method
B. Rule-based method
C. Deep learning-based method
D. Recursion-based method
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: A,B | Question #3 Correct Answer: D | Question #4 Correct Answer: B,C | Question #5 Correct Answer: D |
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