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Updated: Sep 13, 2026
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| Certification Vendor: | Huawei |
|---|---|
| Exam Name: | Huawei Certified ICT Expert-Big Data-Data Mining (Written) V3.0 |
| Exam Number: | H13-731 |
| Exam Price: | USD 300 |
| Exam Format: | Scenario-based, Drag and Drop, Fill-in-the-blank, True/False, Multiple Choice, Multiple Response |
| Related Certifications: | HCIP-Big Data HCIE-Big Data Lab Exam HCIA-Big Data |
| Real Exam Qty: | 60–70 |
| Available Languages: | English, Chinese |
| Exam Duration: | 90 minutes |
| Passing Score: | 600 / 1000 |
| Certificate Validity Period: | 3 years |
| Recommended Training: | HCIE-Big Data-Data Mining V3.0 Official Training Huawei ICT Academy |
| Exam Registration: | Pearson VUE Registration Huawei Talent Certification Portal |
| Exam Way: | Online proctored or onsite at authorized Pearson VUE/Huawei test centers |
| Pre Condition: | No mandatory prerequisites; recommended: HCIP-Big Data or equivalent experience, Python proficiency, ML/DL knowledge |
| Official Syllabus URL: | https://e.huawei.com/en/talent/certification/exam/h13-731 |
| Section | Weight | Objectives |
|---|---|---|
| Regression & Predictive Modeling | 10% | - Model tuning and regularization - Linear and Logistic Regression |
| Big Data Architecture & Governance | 12% | - Data governance, security and quality - Hadoop ecosystem architecture - Spark framework and components - Huawei FusionInsight platform |
| Data Preprocessing | 13% | - Handling missing values and outliers - Feature selection and dimensionality reduction - Data cleaning, integration, transformation |
| Machine Learning & Deep Learning | 15% | - Huawei ModelArts and MindSpore - Neural Networks basics - Distributed machine learning |
| Association Rule Mining | 8% | - Apriori, FP-Growth algorithms - Rule evaluation and application |
| Big Data Analysis & Optimization | 10% | - Performance tuning and optimization - Spark SQL, Spark MLlib - End-to-end mining project design |
| Classification Algorithms | 15% | - Model evaluation and metrics - Decision Tree, Random Forest - SVM, Naive Bayes, KNN |
| Introduction to Data Mining | 5% | - CRISP-DM framework - Data Mining concepts and process - Big Data and Data Mining integration |
| Clustering Algorithms | 12% | - K-Means, DBSCAN, Hierarchical clustering - Clustering validation techniques |
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