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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Development with Huawei ModelArts | - AutoML Capabilities - ModelArts Platform Overview - Training Models on ModelArts |
| Topic 2: Deep Learning | - Neural Network Fundamentals - Model Training and Optimization - CNN and RNN Architectures |
| Topic 3: Model Deployment and Operations | - Monitoring and Maintenance - Inference Services - Model Deployment Strategies |
| Topic 4: Huawei AI Ecosystem Tools | - AI Development Toolchain - Huawei Cloud AI Services - MindSpore Framework Basics |
| Topic 5: AI Fundamentals | - Introduction to Artificial Intelligence - AI Development Lifecycle - Common AI Use Cases in Industry |
| Topic 6: Machine Learning | - Supervised Learning
|
| Topic 7: AI Application Development (EI) | - AI Service Integration - Enterprise Intelligence (EI) Concepts - Building AI Applications |
| Topic 8: Data Processing | - Data Collection and Cleaning - Feature Engineering - Data Labeling and Preparation |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Which of the following are the impacts of the development of large models?
A) Data privacy and security issues will be exacerbated
B) The accuracy and efficiency of natural language processing tasks will improve
C) Model pre-training costs will be reduced
D) Large models will completely replace small and domain-specific models
2. 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 process high-resolution images to enhance classification accuracy.
C) It can handle small datasets with minimal labeling required.
D) It achieves fast convergence without using pre-trained models.
3. 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) 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.
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.
4. Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
A) TRUE
B) FALSE
5. In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: A | Question # 3 Answer: A,B,C | Question # 4 Answer: A | Question # 5 Answer: Only visible for members |






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