Huawei : H13-321_V2.0 Questions & Answers
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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Network Basics | 4% | - Multilayer Perceptron (MLP) - Gradient descent and backpropagation - Activation functions and regularization - Basic concepts of neural networks |
| Topic 2: Natural Language Processing Lab Guide | 10% | - Application integration and deployment - ModelArts NLP model training and tuning - Text classification and NER implementation |
| Topic 3: Speech Processing Theory and Applications | 10% | - Speech signal characteristics and processing - Acoustic and language modeling - Automatic Speech Recognition (ASR) - Text-to-Speech (TTS) technology |
| Topic 4: Natural Language Processing Theory and Applications | 10% | - Word representation and embedding - Text classification, NER, machine translation - RNN, LSTM, GRU, Transformer architecture - BERT, GPT and pre-trained models |
| Topic 5: Overview of ModelArts | 4% | - ModelArts platform positioning and architecture - Data processing, training, deployment capabilities - Development environment and tool usage |
| Topic 6: Image Processing Theory and Applications | 26% | - Image processing fundamentals - Convolutional Neural Networks (CNN) - Image classification, object detection, segmentation - OCR and visual application development |
| Topic 7: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
| Topic 8: Speech Processing Lab Guide | 12% | - ASR and TTS service development - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service |
| Topic 9: Image Processing Lab Guide | 12% | - Object detection and segmentation practice - Ascend-based deployment - ModelArts-based image classification |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. In the figure below, the chessboard distance between pixels p and s is () (& symbol represents a space)
P. &q
& r&t
&&&s
A) 5
B) 8
C) 3
D) 2
2. Which of the following models can be used for natural language processing?
A) SVM
B) HMM
C) DT
D) Linear regression
3. The output of most optical sensors is a continuous signal. The computer can only perform digital image processing after the continuous signal is converted into a discrete signal.
A) True
B) False
4. The denoising effect of mean filtering is better than that of Gaussian filtering for the same size.
A) True
B) False
5. Which of the following options is not a feature of ModelArts?
A) Support automatic tuning
B) Support model management
C) Support Tensorflow and Pytorch
D) Multiple built-in models
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: A |
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