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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

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

          1. The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.

          A) TRUE
          B) FALSE


          2. Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.

          A) TRUE
          B) FALSE


          3. In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.

          A) TRUE
          B) FALSE


          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) Each header's query, key, and value undergo a shared linear transformation to obtain them.
          D) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.


          5. The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)


          Solutions:

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

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