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The Qlik Sense Data Architect Certification Exam certification exam covers a wide range of topics, including data modeling, data connections, data security, and data transformation. QSDA2019 exam includes 50 multiple-choice questions and has a time limit of 120 minutes. The passing score for the Qlik QSDA2019 is 70%, and it is a proctored exam.
Qlik QSDA2019 Certification Exam is intended for individuals who want to demonstrate their expertise in data architecture using Qlik Sense. It is suitable for data architects, data analysts, data scientists, business analysts, and other professionals who work with data on a regular basis. QSDA2019 exam is designed to validate the candidate's ability to design, develop and deploy Qlik Sense data models and visualizations.
Reference: https://www.qlik.com/us/services/training/certifications-and-qualifications
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Qlik QSDA2019 certification exam is an excellent opportunity for data professionals to showcase their skills and knowledge in Qlik Sense data architecture. Qlik Sense Data Architect Certification Exam certification is globally recognized and can help professionals advance their career in the field of business intelligence and data analytics. If you are interested in taking the Qlik QSDA2019 exam, it is recommended that you have a minimum of two years of experience in data architecture and have completed the Qlik Sense Data Architect course.
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Qlik QSDA2019 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Identify Model Requirements | 10% | - Identify data load and refresh requirements
- Identify Qlik Sense features to meet business requirements
- Determine security requirements
|
| Topic 2: Design Data Models | 30% | - Determine appropriate data model architecture
- Design scalable and reusable data models
- Manage complex modeling scenarios
- Implement calendars, flags, and set analysis support structures
|
| Topic 3: Build Data Models | 40% | - Use data transformation techniques in Data Load Editor and Data Manager
- Perform joins, keeps, resident loads, and incremental loads
- Create and transform data models using load scripts
- Manage data connections and data sources
- Implement Section Access security
|
| Topic 4: Validate Data | 20% | - Resolve data quality and formatting issues
- Validate data models against source systems
- Debug and troubleshoot script errors
- Resolve synthetic keys and circular references
|