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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Speech Processing | 22% | - Speech Processing Theory and Applications - Speech Processing Lab Guide |
| Natural Language Processing (NLP) | 20% | - NLP Lab Guide - NLP Theory and Applications |
| Huawei AI Ecosystem and ModelArts | 16% | - Huawei AI Strategy and Ecosystem Overview - ModelArts Platform Overview - ModelArts Lab Guide |
| AI Fundamentals and Neural Networks | 4% | - Neural Network Basics |
| Computer Vision (Image Processing) | 26% | - Image Processing Lab Guide - Image Processing Theory and Applications
|
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Deep learning model training requires a large amount of data. In actual operation, random noise can be added to the training image data set through data enhancement technology to expand the sample size of the data set.
- A. False
- B. True
Correct Answer: B 🗳️
What are the processing steps involved in image digitization? (Multiple choice)
- A. Quantification
- B. Sampling
- C. Grayscale transformation
- D. Binarization
Correct Answer: A,B 🗳️
Please match the following functions with the corresponding service modules
Reveal Solution Discussion 0Correct Answer:
The left column (matching items in brackets) is labeled team (data management) training job (training management) online service (deployment and launch) model compression (model management)
Which of the following options is not a method in the development of natural language processing?
- A. Rule-based approach
- B. Statistical-based methods
- C. Recursion-based methods
- D. Deep Learning-Based Methods
Correct Answer: C 🗳️
After the ModelArts automatic learning object detection model is deployed, users can add pictures for testing. Which of the following options are output prediction results? (Multiple choice)
- A. The true label of each detection box
- B. Four-point coordinates of each detection box
- C. Label predicted by each detection box
- D. Confidence of each detection box
Correct Answer: B,C,D 🗳️
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