Qlik Sense Data Architect Practice Exam

The Qlik Sense Data Architect Certification Exam validates the candidate's skills in identifying requirements for data models, designing and building data models, and measuring the data. This test works regardless of the platform, so it's suitable for both versions of Qlik Sense: the client-managed one and the SaaS edition.

Who should take the exam?

The target audience for the Qlik Sense Data Architect Certification Exam is primarily:

• Data architects
• Data modelers
• Database administrators
• Business analysts
• IT professionals
• Individuals with solid understanding of data modeling concepts and best practices.
• Those having experience working with relational databases and data warehouses.
• Those wanting to demonstrate their expertise in designing and building Qlik Sense data architectures.

Prerequisites for the exam:

Candidates taking the exam must have:

• Hands-on experience building several high-quality applications in Qlik Sense.
• Skill to write Qlik Sense LOAD scripts and ensure data accuracy.
• Basic grasp of Extract-Transform-Load (ETL) processes.
• Capability to establish connections with different data sources using connectors.
• Knowledge of the QVD layer and the architecture of the Qlik platform.
• Aptitude to design data structures for best performance.
• Familiarity with SQL and relational databases.

Exam Details of Qlik Sense Data Architect

• Exam Name: Qlik Sense Data Architect
  
• Exam Languages: English
• Exam Questions: 50 Questions
• Time Duration: 2 hours
• Passing Score: 62%

Qlik Sense Data Architect Exam Course Outline 

The Qlik Sense Data Architect Exam covers the given topics  - 

• Determining primary requirements with business users.
• Given a scenario, choosing stakeholders.
• Finding metrics and levels of granularity and aggregation.
• Determining dimensionality and need for slowly changing dimensionality support.
• Determining the appropriate level of security.

Topic 2: Data Connectivity (8%)

• Determining the data sources and connectors needed.
• Determining the appropriate method to create connections for data sources.

Topic 3: Learn Data Model Design (28%)

• Determining the measures and attributes from each data source.
• Discovering the appropriate type of data model.
• Determining the correct method to optimize the data model for Qlik Sense.
• Determining the correct method to implement data structures efficiently.

Topic 4: Learn about Data Transformations (38%)

• Determining the correct method to build data content based on requirements.
• Evaluating null and blank data handling required to support filtering.
• Determining the correct method to document Data Load scripts.
• Determining the correct method for date handling techniques.
• Determining the correct method to perform script organization and cleansing.
• Analyzing relevant variables to build scripts for incremental loading for extract layer.

Topic 5: Understand Validation (6%)

• Determining the appropriate method to validate and test scripts.
• Determining the appropriate method to validate and test data.