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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: EI Model Development Fundamentals | 15% | - Model Development Process - EI Service and Architecture - Development Environment Setup - HiLens Framework and Skills |
| Topic 2: Natural Language Processing Application | 15% | - Text Preprocessing and Embedding - Text Classification Models - Language Model Fine-tuning - Named Entity Recognition |
| Topic 3: Image Recognition Application Development | 15% | - Image Segmentation - Transfer Learning with Pre-trained Models - Object Detection Implementation - Image Classification Models |
| Topic 4: HiLens Platform Development | 20% | - Real-time Inference Optimization - Edge Deployment Strategy - Skill Development Framework - Multi-modal Data Processing |
| Topic 5: ModelArts Pro Development | 20% | - AutoML and Automatic Model Training - Model Deployment and Management - Hyperparameter Optimization - Inference Service Configuration |
| Topic 6: Deep Learning Fundamentals | 15% | - Optimization Algorithms - Neural Network Basics - CNN and RNN Architectures - Training and Fine-tuning |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. 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
2. What are the advantages of deep learning-based speech recognition algorithms?
A) Forced alignment of annotated data
B) No data training
C) End-to-end task processing
D) Automated feature extraction
3. Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
B) Each header's query, key, and value undergo a shared linear transformation to obtain them.
C) The multi-head attention mechanism captures information about different subspaces within a sequence.
D) The concatenated output is fed directly into the multi-headed attention mechanism.
4. A text classification task has only one final output, while a sequence labeling task has an output in each input position.
A) FALSE
B) TRUE
5. 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
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C,D | Question # 3 Answer: A,C | Question # 4 Answer: B | Question # 5 Answer: B |
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