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

H13-321_V2.5 real exams

Exam Code: H13-321_V2.5

Exam Name: HCIP-AI-EI Developer V2.5

Updated: Sep 29, 2026

Q & A: 62 Questions and Answers

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Huawei H13-321_V2.5 Exam Syllabus Topics:

SectionWeightObjectives
Theoretical Knowledge and Applications of Image Processing26%- Typical application scenarios
- Image classification, detection and segmentation
- Image preprocessing technology
- Feature extraction and representation
Overview of ModelArts4%- ModelArts positioning and architecture
- Basic operation process
- Core functions and service modules
Image Processing Lab Guide12%- Image processing model development and deployment
- Performance optimization and testing
- Development environment setup
Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio2%- Huawei AI development layout
- Full-stack AI technology system
- All-scenario AI solutions
Neural Network Basics4%- Training and optimization methods
- Common neural network structures
- Basic concepts of neural networks
Speech Processing Lab Guide12%- Speech model building and tuning
- Application deployment and verification
- Speech data processing practice
Theoretical Knowledge and Applications of Natural Language Processing10%- Language model and semantic understanding
- Text processing and representation
- Machine translation, text generation and other technologies
- Practical application
Natural Language Processing Lab Guide10%- End-to-end application development
- NLP model training and evaluation
- Text preprocessing and feature engineering
Theoretical Knowledge and Applications of Speech Processing10%- Speech recognition and synthesis
- Speech signal processing foundation
- Speech feature extraction
- Application cases

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

Question #1

------- is a model that uses a convolutional neural network (CNN) to classify texts.

Reveal Solution  Discussion  0

Correct Answer:

Text CNN
Explanation:
Text CNN applies convolutional layers directly to text data represented as word embeddings. By using multiple kernel sizes, Text CNN captures features from n-grams of varying lengths. These features are pooled and passed to fully connected layers for classification tasks such as sentiment analysis or spam detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text CNN applies convolution and pooling over word embeddings to extract local features for text classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN Applications in NLP

Question #2

Which of the following has never been used as a method in the history of NLP?

  • A. Recursion-based method
  • B. Deep learning-based method
  • C. Statistics-based method
  • D. Rule-based method
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?

  • A. Tanh
  • B. ReLU
  • C. Sigmoid
  • D. SeLU
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

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

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

Correct Answer: B  🗳️

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Question #5

-------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)

Reveal Solution  Discussion  0

Correct Answer:

FastText
Explanation:
FastTextis an extension of Word2Vec developed by Facebook AI Research. Unlike Word2Vec, which learns embeddings for whole words, FastText represents each word as a sum of its character n-gram embeddings.
This helps in handling rare words and morphologically rich languages by generating embeddings for unseen words from their subword components.
Exact Extract from HCIP-AI EI Developer V2.5:
"FastText decomposes words into character n-grams and represents words as the sum of their n-gram vectors, improving representation for rare and out-of-vocabulary words." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Subword Embedding Models

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