Huawei H13-321_V2.5 Q&A - in .pdf

  • Exam Code: H13-321_V2.5
  • Exam Name: HCIP-AI-EI Developer V2.5
  • Updated: Oct 03, 2026
  • Q & A: 62 Questions and Answers
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  • Exam Code: H13-321_V2.5
  • Exam Name: HCIP-AI-EI Developer V2.5
  • Updated: Oct 03, 2026
  • Q & A: 62 Questions and Answers
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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

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

          Question #1

          Which of the following is not an acoustic feature of speech?

          • A. Duration
          • B. Frequency
          • C. Amplitude
          • D. Semantics
          Reveal Solution  Discussion  0

          Correct Answer: D  πŸ—³οΈ

          Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).

          Question #2

          The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.

          • A. TRUE
          • B. FALSE
          Reveal Solution  Discussion  0

          Correct Answer: A  πŸ—³οΈ

          Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).

          Question #3

          In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)

          • A. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
          • B. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
          • C. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
          • D. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
          Reveal Solution  Discussion  0

          Correct Answer: A,B,C,D  πŸ—³οΈ

          Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).

          Question #4

          When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.

          • A. TRUE
          • B. FALSE
          Reveal Solution  Discussion  0

          Correct Answer: A  πŸ—³οΈ

          Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).

          Question #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.)

          Reveal Solution  Discussion  0

          Correct Answer:

          NER
          Explanation:
          NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages.
          NER is widely used in information extraction, question answering, and knowledge graph construction.
          Exact Extract from HCIP-AI EI Developer V2.5:
          "NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Labeling Tasks

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