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Huawei H13-731 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Architecture & Governance | 12% | - Spark framework and components - Huawei FusionInsight platform - Hadoop ecosystem architecture - Data governance, security and quality |
| Topic 2: Regression & Predictive Modeling | 10% | - Model tuning and regularization - Linear and Logistic Regression |
| Topic 3: Clustering Algorithms | 12% | - K-Means, DBSCAN, Hierarchical clustering - Clustering validation techniques |
| Topic 4: Big Data Analysis & Optimization | 10% | - Spark SQL, Spark MLlib - Performance tuning and optimization - End-to-end mining project design |
| Topic 5: Introduction to Data Mining | 5% | - Big Data and Data Mining integration - Data Mining concepts and process - CRISP-DM framework |
| Topic 6: Association Rule Mining | 8% | - Apriori, FP-Growth algorithms - Rule evaluation and application |
| Topic 7: Classification Algorithms | 15% | - Decision Tree, Random Forest - SVM, Naive Bayes, KNN - Model evaluation and metrics |
| Topic 8: Machine Learning & Deep Learning | 15% | - Distributed machine learning - Neural Networks basics - Huawei ModelArts and MindSpore |
| Topic 9: Data Preprocessing | 13% | - Handling missing values and outliers - Data cleaning, integration, transformation - Feature selection and dimensionality reduction |
