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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deep Learning Fundamentals | 15% | - Training and Fine-tuning - Optimization Algorithms - CNN and RNN Architectures - Neural Network Basics |
| HiLens Platform Development | 20% | - Multi-modal Data Processing - Real-time Inference Optimization - Skill Development Framework - Edge Deployment Strategy |
| ModelArts Pro Development | 20% | - Hyperparameter Optimization - AutoML and Automatic Model Training - Inference Service Configuration - Model Deployment and Management |
| Image Recognition Application Development | 15% | - Transfer Learning with Pre-trained Models - Object Detection Implementation - Image Classification Models - Image Segmentation |
| EI Model Development Fundamentals | 15% | - EI Service and Architecture - Model Development Process - HiLens Framework and Skills - Development Environment Setup |
| Natural Language Processing Application | 15% | - Named Entity Recognition - Language Model Fine-tuning - Text Preprocessing and Embedding - Text Classification Models |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Which of the following methods are useful when tackling overfitting?
A) Using parameter norm penalties
B) Using more complex models
C) Using dropout during model training
D) Data augmentation
2. Which of the following statements about the functions of the encoder and decoder is true?
A) The encoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
B) The decoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
C) The output lengths of the encoder and decoder are the same.
D) The encoder converts context vectors into variable-length output sequences.
3. 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
4. In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)
5. 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.)
Solutions:
| Question # 1 Answer: A,C,D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: Only visible for members | Question # 5 Answer: Only visible for members |
Ruth 
Maria 
Dick 
Helen 
Ina 
Horace 
Avery 
Xanthe 
Ophelia 
Bing 
Michelle 
Webb 
Dennis 
Meroy 
