Huawei HCIP-AI-EI Developer V2.5 : H13-321_V2.5

  • Exam Code: H13-321_V2.5
  • Exam Name: HCIP-AI-EI Developer V2.5
  • Updated: Sep 06, 2026     Q & A: 62 Questions and Answers

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Huawei H13-321_V2.5 Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning- Unsupervised Learning
  • 1. Dimensionality Reduction
    • 2. Clustering Methods
      - Supervised Learning
      • 1. Classification Algorithms
        • 2. Regression Algorithms
          Topic 2: Model Development with Huawei ModelArts- Training Models on ModelArts
          - AutoML Capabilities
          - ModelArts Platform Overview
          Topic 3: Model Deployment and Operations- Inference Services
          - Model Deployment Strategies
          - Monitoring and Maintenance
          Topic 4: Deep Learning- Model Training and Optimization
          - Neural Network Fundamentals
          - CNN and RNN Architectures
          Topic 5: AI Fundamentals- Common AI Use Cases in Industry
          - Introduction to Artificial Intelligence
          - AI Development Lifecycle
          Topic 6: Huawei AI Ecosystem Tools- AI Development Toolchain
          - Huawei Cloud AI Services
          - MindSpore Framework Basics
          Topic 7: Data Processing- Data Collection and Cleaning
          - Feature Engineering
          - Data Labeling and Preparation
          Topic 8: AI Application Development (EI)- Enterprise Intelligence (EI) Concepts
          - AI Service Integration
          - Building AI Applications

          Huawei HCIP-AI-EI Developer V2.5 Sample Questions:

          Question 1

          When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
          In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
          In Python's scikit-learn library, this is implemented using:


          Question 2

          In NLP tasks, transformer models perform well in multiple tasks due to their self-attention mechanism and parallel computing capability. Which of the following statements about transformer models are true?

          A. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
          B. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
          C. Multi-head attention is the core component of a transformer model. It computes multiple attention heads in parallel to capture semantic information in different subspaces.
          D. A transformer model directly captures the dependency between different positions in the input sequence through the self-attention mechanism, without using the recurrent neural network (RNN) or convolutional neural network (CNN).


          Question 3

          In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?

          A. Hue indicates the basic color attributes, such as red, green, and blue.
          B. The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
          C. Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
          D. Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.


          Question 4

          Overfitting is a condition where a model is overly simple and excessive generalization errors occur.

          A. TRUE
          B. FALSE


          Question 5

          Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?

          A. It can process high-resolution images to enhance classification accuracy.
          B. It can handle small datasets with minimal labeling required.
          C. It achieves fast convergence without using pre-trained models.
          D. The self-attention mechanism is used to capture global features of images, improving classification accuracy.


          Solutions:

          Question 1
          Answer: Only visible for members
          Question 2
          Answer: B,C,D
          Question 3
          Answer: A,C,D
          Question 4
          Answer: B
          Question 5
          Answer: D

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