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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Huawei AI Ecosystem Tools | - Huawei Cloud AI Services - AI Development Toolchain - MindSpore Framework Basics |
| Topic 2: Model Development with Huawei ModelArts | - AutoML Capabilities - ModelArts Platform Overview - Training Models on ModelArts |
| Topic 3: AI Fundamentals | - AI Development Lifecycle - Introduction to Artificial Intelligence - Common AI Use Cases in Industry |
| Topic 4: Data Processing | - Feature Engineering - Data Labeling and Preparation - Data Collection and Cleaning |
| Topic 5: AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts - AI Service Integration - Building AI Applications |
| Topic 6: Model Deployment and Operations | - Inference Services - Monitoring and Maintenance - Model Deployment Strategies |
| Topic 7: Deep Learning | - Model Training and Optimization - Neural Network Fundamentals - CNN and RNN Architectures |
| Topic 8: Machine Learning | - Supervised Learning
|
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Which of the following applications are supported by ModelArts ExeML?
A) Automatic offering classification
B) Dress code conformance monitoring in campuses
C) Predictive maintenance of manufacturing equipment
D) Anomalous sound detection in production or security scenarios
2. In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
A) TRUE
B) FALSE
3. Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
A) TRUE
B) FALSE
4. The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
A) TRUE
B) FALSE
5. Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
A) Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
B) Residual connections primarily add depth to the model but do not aid in gradient propagation.
C) In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
D) Layer normalization accelerates model convergence and does not affect model stability.
Solutions:
| Question # 1 Answer: A,B,C,D | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: A |
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