Exam Name: HCIP-AI-EI Developer V2.5
Certification Provider: Huawei
Corresponding Certification: HCIP-AI EI Developer
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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Machine Learning | - Unsupervised Learning
|
| Topic 2: Data Processing | - Feature Engineering - Data Labeling and Preparation - Data Collection and Cleaning |
| Topic 3: Huawei AI Ecosystem Tools | - AI Development Toolchain - MindSpore Framework Basics - Huawei Cloud AI Services |
| Topic 4: Model Deployment and Operations | - Monitoring and Maintenance - Model Deployment Strategies - Inference Services |
| Topic 5: Deep Learning | - CNN and RNN Architectures - Model Training and Optimization - Neural Network Fundamentals |
| Topic 6: AI Fundamentals | - Introduction to Artificial Intelligence - Common AI Use Cases in Industry - AI Development Lifecycle |
| Topic 7: AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts - AI Service Integration - Building AI Applications |
| Topic 8: Model Development with Huawei ModelArts | - Training Models on ModelArts - AutoML Capabilities - ModelArts Platform Overview |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
- A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
- B. The multi-head attention mechanism captures information about different subspaces within a sequence.
- C. The concatenated output is fed directly into the multi-headed attention mechanism.
- D. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
Correct Answer: B,D 🗳️
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using:
Correct Answer:
python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
- A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
- B. The multi-head attention mechanism captures information about different subspaces within a sequence.
- C. The concatenated output is fed directly into the multi-headed attention mechanism.
- D. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
Correct Answer: B,D 🗳️
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
- A. FALSE
- B. TRUE
Correct Answer: B 🗳️
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
The U-Net uses an upsampling mechanism and has a fully-connected layer.
- A. FALSE
- B. TRUE
Correct Answer: A 🗳️
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
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