Exam 70-768: Developing SQL Data Models
This exam retired on January 31, 2021. For currently available options, please refer to the Browse Certifications and Exams page.
This exam is intended for business intelligence (BI) developers who focus on creating BI solutions that require implementing multidimensional data models, implementing and maintaining OLAP cubes, and implementing tabular data models.
Part of the requirements for: MCSA: SQL 2016 BI Development MCSE: Data Management and Analytics
How much 70-768 Exam Cost
The price of the Microsoft 70-768 exam is $165 USD.
Reference: https://www.microsoft.com/en-us/learning/exam-70-768.aspx
Microsoft 70-768日本語 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Developing SQL Data Models |
| Exam Number: | 70-768 |
| Certificate Validity Period: | Retired |
| Related Certifications: | MCSA: SQL Server 2016 BI Development |
| Exam Format: | Multiple choice, Case study, Lab-based scenarios |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Recommended Training: | SQL Server Analysis Services (SSAS) learning modules Microsoft Learn - SQL Server Data Models training paths |
| Exam Registration: | Microsoft Credentials Exam Registration |
| Sample Questions: | Microsoft 70-768日本語 Sample Questions |
| Exam Way: | Online proctored or authorized testing center (historically via Pearson VUE for Microsoft certification exams) |
| Pre Condition: | No formal prerequisite, but recommended experience with SQL Server and data modeling concepts. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ |
Microsoft 70-768日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Develop and deploy SQL Server Analysis Services (SSAS) models | - Create tabular models - Create multidimensional models - Configure partitions and storage modes |
| Implement DAX calculations and expressions | - Create calculated columns and measures - Use DAX functions for filtering and time intelligence |
| Implement data integrity and performance optimization | - Indexing and query optimization - Data refresh and processing strategies |
| Design and implement data models | - Implement tables, relationships, and constraints - Design conceptual, logical, and physical data models - Normalize and optimize data structures |


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