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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| AI Application Deployment and Operations | - AI service monitoring and optimization - Inference service deployment |
| Data Processing and Analysis | - Feature engineering - Data preprocessing techniques |
| Huawei AI Development Platform (ModelArts) | - Model training and deployment on ModelArts - Dataset management - ModelArts usage and workflow |
| AI Model Development | - Model training and evaluation - Hyperparameter tuning |
| Artificial Intelligence Fundamentals | - Machine learning basics - Deep learning fundamentals - Introduction to AI concepts |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. Bidirectional RNN solves the problem that the next state affects the current state.
A) True
B) False
2. In REST API, what is the correct way to use HTTP method request message headers? (Multiple choice)
A) PUT, requesting the server to update the specified resource
B) PATCH, requesting the server to add new resources or perform special operations
C) DELETE, requesting the server to delete a specified resource, such as deleting an object
D) GET, requesting the server to return a specified resource
3. In an 8-bit RGB color image, what color does (255, 255, 255) represent?
A) Red
B) White
C) Blue
D) Black
4. In the image preprocessing histogram experiment, the cv2. equalizeHist (im) function represents the call to the OpenCV histogram equalization API.
A) True
B) False
5. In the process of model training and evaluation, overfitting and underfitting often occur. Which of the following descriptions of overfitting and underfitting is correct?
A) Underfitting refers to a situation where the model performs well both during training and prediction.
B) Overfitting refers to a situation where the model does not fit the training data sufficiently.
C) Overfitting is reflected in the evaluation indicators, that is, the model performs well on the training set, but performs poorly on the test set and new data.
D) Underfitting refers to the situation where the model performs well during training but performs poorly during prediction.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,B,C,D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: C |
