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

  • Exam Code: H13-321_V2.5
  • Exam Name: HCIP-AI-EI Developer V2.5
  • Updated: Aug 03, 2026
  • Q&As: 62 Questions and Answers

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Huawei H13-321_V2.5 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: EI Model Development Fundamentals15%- HiLens Framework and Skills
- Model Development Process
- Development Environment Setup
- EI Service and Architecture
Topic 2: HiLens Platform Development20%- Real-time Inference Optimization
- Skill Development Framework
- Multi-modal Data Processing
- Edge Deployment Strategy
Topic 3: ModelArts Pro Development20%- AutoML and Automatic Model Training
- Hyperparameter Optimization
- Model Deployment and Management
- Inference Service Configuration
Topic 4: Natural Language Processing Application15%- Named Entity Recognition
- Text Preprocessing and Embedding
- Text Classification Models
- Language Model Fine-tuning
Topic 5: Deep Learning Fundamentals15%- Neural Network Basics
- Training and Fine-tuning
- CNN and RNN Architectures
- Optimization Algorithms
Topic 6: Image Recognition Application Development15%- Transfer Learning with Pre-trained Models
- Image Segmentation
- Object Detection Implementation
- Image Classification Models

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

1. The image saturation can be enhanced by processing the ________ component of the HSV color space. (Enter H, S, or V.)


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

A) TRUE
B) FALSE


3. John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?

A) Model security
B) Demand for computing power
C) Output delay
D) Model development framework


4. What are the adjacency relationships between two pixels whose coordinates are (21,13) and (22,12)?

A) Diagonal adjacency
B) No adjacency relationship
C) 8-adjacency
D) 4-adjacency


5. Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?

A) Layer normalization accelerates model convergence and does not affect model stability.
B) Residual connections primarily add depth to the model but do not aid in gradient propagation.
C) Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
D) In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.


Solutions:

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

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