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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Image Recognition Application Development | 15% | - Transfer Learning with Pre-trained Models - Image Classification Models - Image Segmentation - Object Detection Implementation |
| Topic 2: Natural Language Processing Application | 15% | - Text Preprocessing and Embedding - Named Entity Recognition - Text Classification Models - Language Model Fine-tuning |
| Topic 3: HiLens Platform Development | 20% | - Multi-modal Data Processing - Skill Development Framework - Edge Deployment Strategy - Real-time Inference Optimization |
| Topic 4: ModelArts Pro Development | 20% | - Model Deployment and Management - Inference Service Configuration - Hyperparameter Optimization - AutoML and Automatic Model Training |
| Topic 5: EI Model Development Fundamentals | 15% | - EI Service and Architecture - Development Environment Setup - HiLens Framework and Skills - Model Development Process |
| Topic 6: Deep Learning Fundamentals | 15% | - CNN and RNN Architectures - Optimization Algorithms - Training and Fine-tuning - Neural Network Basics |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Question 1
The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
A. FALSE
B. TRUE
Question 2
John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?
A. Model security
B. Demand for computing power
C. Model development framework
D. Output delay
Question 3
The mAP evaluation metric in object detection combines accuracy and recall.
A. FALSE
B. TRUE
Question 4
Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?
A. Object detection
B. Video analytics
C. Speech recognition
D. Image classification
Question 5
Overfitting is a condition where a model is overly simple and excessive generalization errors occur.
A. FALSE
B. TRUE
Solutions:
| Question 1 Answer: B | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: A,B,C,D | Question 5 Answer: A |
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