Exam Name: HCIP-AI-EI Developer V2.5
Certification Provider: Huawei
Corresponding Certification: HCIP-AI EI Developer
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Updated: Sep 29, 2026
No. of Questions: 62 Questions & Answers
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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Speech Processing Lab Guide | 12% | - Speech model building and tuning - Speech data processing practice - Application deployment and verification |
| Topic 2: Neural Network Basics | 4% | - Basic concepts of neural networks - Common neural network structures - Training and optimization methods |
| Topic 3: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Text processing and representation - Machine translation, text generation and other technologies - Practical application - Language model and semantic understanding |
| Topic 4: Image Processing Lab Guide | 12% | - Image processing model development and deployment - Development environment setup - Performance optimization and testing |
| Topic 5: Overview of ModelArts | 4% | - Core functions and service modules - ModelArts positioning and architecture - Basic operation process |
| Topic 6: Theoretical Knowledge and Applications of Image Processing | 26% | - Image preprocessing technology - Typical application scenarios - Image classification, detection and segmentation - Feature extraction and representation |
| Topic 7: Natural Language Processing Lab Guide | 10% | - End-to-end application development - NLP model training and evaluation - Text preprocessing and feature engineering |
| Topic 8: Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack AI technology system - Huawei AI development layout - All-scenario AI solutions |
| Topic 9: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech feature extraction - Application cases - Speech recognition and synthesis - Speech signal processing foundation |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
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
Correct Answer: B 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
- A. TRUE
- B. FALSE
Correct Answer: A 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
- A. TRUE
- B. FALSE
Correct Answer: B 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
- A. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
- B. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
- C. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
- D. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
Correct Answer: A,B,D 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
Which of the following are required for the image object detection algorithm?
- A. Object classification determination
- B. Object location calculation
- C. Confidence calculation
- D. Object contour calculation
Correct Answer: A,B,C 🗳️
Explanation: Only visible for Lead1Pass members. You can sign-up / login (it's free).
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