Huawei H13-321_V2.0 : HCIP-AI-EI Developer V2.0

  • Exam Code: H13-321_V2.0
  • Exam Name: HCIP-AI-EI Developer V2.0
  • Updated: Oct 01, 2026

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Huawei H13-321_V2.0 Exam Syllabus Topics:

SectionObjectives
Huawei AI Development Strategy and AI Portfolio- Huawei AI Ecosystem
  • 1. All-Scenario AI Architecture
  • 2. Full-Stack AI Strategy
  • 3. Huawei Cloud EI Services
Image Processing Theory and Applications- Computer Vision Fundamentals
  • 1. Image Classification
  • 2. Image Recognition Applications
  • 3. Object Detection
Neural Network Basics- Neural Network Fundamentals
  • 1. Common Neural Network Models
  • 2. Artificial Neural Networks
  • 3. Deep Learning Basics
Speech Processing Theory and Applications- Speech AI Technologies
  • 1. Speech Processing Scenarios
  • 2. Speech Synthesis
  • 3. Speech Recognition
Natural Language Processing Labs- Practical Development
  • 1. Text Analysis Experiments
  • 2. NLP Model Applications
Speech Processing Labs- Practical Development
  • 1. Speech Recognition Experiments
  • 2. Speech AI Applications
Image Processing Labs- Practical Development
  • 1. Image Processing Experiments
  • 2. Computer Vision Implementation
Natural Language Processing Theory and Applications- NLP Fundamentals
  • 1. NLP Application Development
  • 2. Text Processing
  • 3. Language Understanding
ModelArts Platform Overview- AI Development Platform
  • 1. Model Deployment
  • 2. AI Workflow Management
  • 3. Model Training

Huawei HCIP-AI-EI Developer V2.0 Sample Questions:

Question #1

When using gensim to implement word2vec, which parameter represents the dimension of the output word vector?

  • A. hs
  • B. size
  • C. window
  • D. min_count
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

Image filter operations are also called template operations. Commonly used template operations include template (convolution) and template (sorting).

  • A. True
  • B. False
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #3

Which of the following descriptions of the shortcomings of the co -occurrence matrix + SVD is wrong?

  • A. SVD calculation is large
  • B. The co-occurrence matrix does not take into account the order of words (correct)
  • C. The co-occurrence matrix is high-dimensional and sparse (correct)
  • D. SVD only learns shallow semantics (correct)
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

Regarding CBOW and Skip-Gram algorithms, which of the following statements is incorrect?

  • A. Skip-Gram calculates the probabilities of several words before and after a certain word.
  • B. CBOW calculates the probability of a word appearing based on the n words preceding it or the n consecutive words before and after it.
  • C. Both the CBOW model and the skip-gram model are based on the Huffman tree.
  • D. Both CBOW and Skip-Gram can be used to train word embeddings, but Skip-Gram is faster than CBOW.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

Which of the following layers does a convolutional neural network usually contain? (Multiple choice)

  • A. Output layer
  • B. Fully connected layer
  • C. Input layer
  • D. Convolutional Layer
Reveal Solution  Discussion  0

Correct Answer: A,B,C,D  🗳️

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