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Last Updated: Aug 03, 2026

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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

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

          1. 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) FALSE
          B) TRUE


          2. Which of the following ModelArts training parameters is used to customize hyperparameters?

          A) Algorithm Type
          B) Resource Pool
          C) Compute Nodes
          D) Hyperparameter


          3. What type of task is viewed when using the Seq2Seq model in speech recognition?

          A) Clustering task
          B) Classification task
          C) Dimensionality reduction task
          D) Regression task


          4. Which of the following statements about the multi-head attention mechanism of the Transformer are true?

          A) The concatenated output is fed directly into the multi-headed attention mechanism.
          B) The multi-head attention mechanism captures information about different subspaces within a sequence.
          C) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
          D) Each header's query, key, and value undergo a shared linear transformation to obtain them.


          5. The mAP evaluation metric in object detection combines accuracy and recall.

          A) FALSE
          B) TRUE


          Solutions:

          Question # 1
          Answer: B
          Question # 2
          Answer: D
          Question # 3
          Answer: B
          Question # 4
          Answer: B,C
          Question # 5
          Answer: A

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