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Huawei H13-321_V2.0 Exam Overview:
| Certification Vendor: | Huawei |
|---|---|
| Exam Name: | HCIP-AI-EI Developer V2.0 |
| Exam Number: | H13-321_V2.0-ENU |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 60 |
| Related Certifications: | HCIA-AI HCIE-AI |
| Exam Price: | 300 USD |
| Exam Format: | Scenario-based, True/False, Single-choice, Multiple-choice |
| Certificate Validity Period: | 3 years |
| Passing Score: | 600/1000 |
| Available Languages: | Chinese, English |
| Recommended Training: | HCIP-AI-EI Developer V2.0 Official Training |
| Exam Registration: | Pearson VUE Registration Huawei Certification Official |
| Sample Questions: | Huawei H13-321_V2.0 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 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Natural Language Processing Lab Guide | 10% | - Application integration and deployment - Text classification and NER implementation - ModelArts NLP model training and tuning |
| Overview of ModelArts | 4% | - Data processing, training, deployment capabilities - ModelArts platform positioning and architecture - Development environment and tool usage |
| Image Processing Lab Guide | 12% | - Ascend-based deployment - ModelArts-based image classification - Object detection and segmentation practice |
| Image Processing Theory and Applications | 26% | - Image processing fundamentals - OCR and visual application development - Image classification, object detection, segmentation - Convolutional Neural Networks (CNN) |
| Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
| Speech Processing Lab Guide | 12% | - Huawei Cloud Speech Interaction Service - ModelArts speech application deployment - ASR and TTS service development |
| Natural Language Processing Theory and Applications | 10% | - BERT, GPT and pre-trained models - Text classification, NER, machine translation - Word representation and embedding - RNN, LSTM, GRU, Transformer architecture |
| Neural Network Basics | 4% | - Activation functions and regularization - Gradient descent and backpropagation - Basic concepts of neural networks - Multilayer Perceptron (MLP) |
| Speech Processing Theory and Applications | 10% | - Text-to-Speech (TTS) technology - Speech signal characteristics and processing - Acoustic and language modeling - Automatic Speech Recognition (ASR) |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Natural language processing can be defined as a discipline that studies language problems in human-to-human communication and human-to-computer communication. Natural language processing requires the development of models that represent language ability and language application, the establishment of a computational framework to implement such language models, the proposal of corresponding methods to continuously improve such language models, the design of various practical systems based on such language models, and the discussion of the evaluation techniques of these practical systems.
- A. True
- B. False
What are the tasks of machine learning scenarios? (Multiple choice)
- A. Classification
- B. Generate
- C. Regression
- D. Clustering
Histogram equalization can enhance the visual effect of an image. This technology can automatically calculate the variation function without manually setting parameters. It is simple to operate and is applicable to all situations.
- A. True
- B. False
When calling the face comparison service, the person's gender is also one of the return values.
- A. True
- B. False
What is Huawei's self- developed deep learning framework?
MindSpore
By Beacher
