The Qlik Sense Data Architect certification exam measures your ability to identify requirements for data models, design and build data models, and validate the data. This exam is platform-neutral, which means the content applies to both client-managed and Qlik Cloud.
Certification Exam details
Duration: 120 minutes
Number of questions: 45
Passing score: 60%
Exam content is updated periodically. The number and difficulty of questions may change. The passing score is adjusted to maintain a consistent standard.
Recommended experience
Practical experience developing multiple production-quality applications in Qlik Sense Ability to write Qlik Sense load scripts and validate data Basic understanding of Extract, Transform, Load (ETL) Create and use connectors to various data sources Understand the QVD layer and architecture of the Qlik platform Ability to architect data to provide optimal performance Familiar with SQL and relational databases
Preparation
Data Modeling with Qlik Cloud Analytics / Qlik Sense Link
Advanced Data Modeling with Qlik Cloud Analytics / Qlik Sense Link
Badge
After passing this certification exam, you are awarded the Qlik Sense Data Architect certification badge. To learn more about the criteria for earning this badge, visit our Credly badging page.
Certification Exam topics
Introductory Concepts in Data Modeling
Describe Data Modeling.
Data Connectivity
Determine the data sources and connectors needed. Determine the appropriate method to create connections for data sources.
Data Model Design
Determine the measures and attributes from each data source. Identify the appropriate type of data model. Determine the correct method to optimize the data model for Qlik Sense. Determine the correct method to implement data structures efficiently.
Data Transformations
Determine the correct method to build data content based on requirements. Analyze and evaluate null and blank data handling required to support filtering. Determine the correct method to document Data Load scripts. Determine the correct method for date handling techniques. Determine the correct method to perform script organization and cleansing. Determine the correct method to perform script organization and cleansing. Analyze relevant variables to build scripts for incremental loading for the extract layer.
Validation
Determine the appropriate method to validate and test scripts. Determine the appropriate method to validate and test data.
