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Huawei H13-321_V2.5 Q&A - in .pdf
- Exam Code: H13-321_V2.5
- Exam Name: HCIP-AI-EI Developer V2.5
- Updated: Sep 30, 2026
- Q & A: 62 Questions and Answers
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- 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: Sep 30, 2026
- Q & A: 62 Questions and Answers
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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: 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 3: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Speech feature extraction - Application cases - Speech signal processing foundation |
| Topic 4: Theoretical Knowledge and Applications of Image Processing | 26% | - Typical application scenarios - Image classification, detection and segmentation - Image preprocessing technology - Feature extraction and representation |
| Topic 5: Overview of ModelArts | 4% | - Basic operation process - ModelArts positioning and architecture - Core functions and service modules |
| Topic 6: Image Processing Lab Guide | 12% | - Image processing model development and deployment - Development environment setup - Performance optimization and testing |
| Topic 7: Neural Network Basics | 4% | - Basic concepts of neural networks - Common neural network structures - Training and optimization methods |
| Topic 8: Speech Processing Lab Guide | 12% | - Application deployment and verification - Speech data processing practice - Speech model building and tuning |
| Topic 9: Natural Language Processing Lab Guide | 10% | - Text preprocessing and feature engineering - NLP model training and evaluation - End-to-end application development |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Question #1
The natural language processing field usually uses distributed semantic representation to represent words.
Each word is no longer a completely orthogonal 0-1 vector, but a point in a multi-dimensional real number space, which is specifically represented as a real number vector.
- A. FALSE
- B. TRUE
Correct Answer: B 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #2
Among image preprocessing techniques, gamma correction is a common non-linear brightness adjustment method. Which of the following statements are true about the application and features of gamma correction?
- A. Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.
- B. When # > 1, the input low grayscale range is compressed, and the high grayscale range is stretched, enhancing the bright areas while compressing the dark areas.
- C. Gamma correction applies only to grayscale images and does not apply to color images.
- D. When # < 1, the input high grayscale range is compressed, and the low grayscale range is stretched, enhancing the dark areas while compressing the bright areas.
Correct Answer: A,B,D 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #3
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
- A. Sigmoid
- B. Tanh
- C. SeLU
- D. ReLU
Correct Answer: A 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #4
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. 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.
- B. 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".
- C. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
- D. 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.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #5
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
- A. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
- B. It can handle small datasets with minimal labeling required.
- C. It can process high-resolution images to enhance classification accuracy.
- D. It achieves fast convergence without using pre-trained models.
Correct Answer: A 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
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