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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Image Processing Lab Guide | 12% | - Development environment setup - Image processing model development and deployment - Performance optimization and testing |
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Application cases - Speech signal processing foundation - Speech recognition and synthesis - Speech feature extraction |
| 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 |
| Overview of ModelArts | 4% | - Basic operation process - Core functions and service modules - ModelArts positioning and architecture |
| Natural Language Processing Lab Guide | 10% | - Text preprocessing and feature engineering - End-to-end application development - NLP model training and evaluation |
| Speech Processing Lab Guide | 12% | - Speech data processing practice - Speech model building and tuning - Application deployment and verification |
| Neural Network Basics | 4% | - Training and optimization methods - Common neural network structures - Basic concepts of neural networks |
| Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Text processing and representation - Practical application - Language model and semantic understanding - Machine translation, text generation and other technologies |
| Theoretical Knowledge and Applications of Image Processing | 26% | - Feature extraction and representation - Image classification, detection and segmentation - Typical application scenarios - Image preprocessing technology |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?
- A. 55
- B. 10
- C. 28
- D. 9
Correct Answer: C 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
Reveal Solution Discussion 0Correct Answer:
NER
Explanation:
NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages.
NER is widely used in information extraction, question answering, and knowledge graph construction.
Exact Extract from HCIP-AI EI Developer V2.5:
"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Labeling Tasks
What type of task is viewed when using the Seq2Seq model in speech recognition?
- A. Classification task
- B. Clustering task
- C. Regression task
- D. Dimensionality reduction task
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
Overfitting is a condition where a model is overly simple and excessive generalization errors occur.
- A. TRUE
- B. FALSE
Correct Answer: B 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
-------- 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 0Correct 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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