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Huawei H13-321_V2.0-ENU Exam Overview:
| Certification Vendor: | Huawei |
|---|---|
| Exam Name: | HCIP-AI-EI Developer V2.0 |
| Exam Number: | H13-321_V2.0-ENU |
| Exam Price: | 300 USD |
| Related Certifications: | HCIE-AI HCIA-AI |
| Certificate Validity Period: | 3 years |
| Available Languages: | English, Chinese |
| Real Exam Qty: | 60 |
| Passing Score: | 600/1000 |
| Exam Format: | True/False, Single-choice, Scenario-based, Multiple-choice |
| Exam Duration: | 90 minutes |
| Recommended Training: | HCIP-AI-EI Developer V2.0 Official Training |
| Exam Registration: | Huawei Certification Official Pearson VUE Registration |
| Sample Questions: | Huawei H13-321_V2.0-ENU Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE / Huawei authorized test centers |
| Pre Condition: | Recommended: HCIA-AI certification or equivalent knowledge; 6+ months AI development experience |
| Official Syllabus URL: | https://edu.huaweicloud.com/intl/en-us/certificationindex/career/aisd.html |
Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Natural Language Processing Lab Guide | 10% | - ModelArts NLP model training and tuning - Application integration and deployment - Text classification and NER implementation |
| Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development strategy - Full-stack and all-scenario AI technology layout |
| Speech Processing Theory and Applications | 10% | - Speech signal characteristics and processing - Automatic Speech Recognition (ASR) - Text-to-Speech (TTS) technology - Acoustic and language modeling |
| Neural Network Basics | 4% | - Activation functions and regularization - Basic concepts of neural networks - Multilayer Perceptron (MLP) - Gradient descent and backpropagation |
| Overview of ModelArts | 4% | - Data processing, training, deployment capabilities - ModelArts platform positioning and architecture - Development environment and tool usage |
| Image Processing Theory and Applications | 26% | - Convolutional Neural Networks (CNN) - Image classification, object detection, segmentation - Image processing fundamentals - OCR and visual application development |
| Image Processing Lab Guide | 12% | - Object detection and segmentation practice - Ascend-based deployment - ModelArts-based image classification |
| Speech Processing Lab Guide | 12% | - ASR and TTS service development - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service |
| Natural Language Processing Theory and Applications | 10% | - Word representation and embedding - RNN, LSTM, GRU, Transformer architecture - Text classification, NER, machine translation - BERT, GPT and pre-trained models |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Question #1
Template matching is a basic algorithm for target detection.
- A. False
- B. True
Correct Answer: B 🗳️
Question #2
The principle of Sobel operator and Laplacian operator to extract edges is to calculate the first-order gradient of the image.
- A. False
- B. True
Correct Answer: A 🗳️
Question #3
Natural language processing is a hot field at present, but there are many problems and difficulties in its engineering process. What are the difficulties in named entity recognition? (Multiple choice)
- A. The composition rules of named entities are complex.
- B. There are a large number of named entities of various types.
- C. The nesting situation is complex.
- D. The length is uncertain.
Correct Answer: A,B,C,D 🗳️
Question #4
Word segmentation is a basic step in natural language processing. What are the common word segmentation methods? (Multiple choice)
- A. Statistical-based word segmentation
- B. Rule-based word segmentation
- C. Hybrid word segmentation combining rule-based and statistical-based
- D. Word segmentation based on deep learning
Correct Answer: A,B,C,D 🗳️
Question #5
When using a convolutional neural network, it is usually sufficient to directly input the original image without artificial preprocessing and feature extraction.
- A. False
- B. True
Correct Answer: B 🗳️
By Quentin
