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Qualifications
Data Literacy Qualification
The Data Literacy Qualification Exam is a non-technical, product-agnostic exam, measuring an individual’s fundamental-level and applied understanding of skills and abilities to read, work with, analyze and communicate with data. This exam has 30 multiple-choice questions to answer in 1 hour.
A candidate for the Data Literacy Qualification exam will have experience and ability to demonstrate proficiency in
Awareness of the value of data
Describing Data: Describe that the data is showing
Reasoning with data: Understand what the data means relative to the question
Communicating with data: Sharing insights using data, narrative and visualizations to gain support
Making decisions that are influenced by data
All courses in the Overview of Data Literacy section
All courses in the Data Fundamentals section
All courses in the Data-Informed Decision Making section
Under the Analytical Techniques section, the Understanding Signal and Noise and Correlation vs Causation courses
Interpret business requirements
Change a business question into an analytical question
Explain data sources and refresh frequency needed to implement requirements
Discuss KPIs, dimensions, and measures for analysis
Understand and transform data
Explain various data types and implications for analysis
Use basic statistics
Explain aggregations needed for analysis
Contrast types of distributions and explain implications in analysis
Interpret visualizations
Determine whether visualization is valid and can answer the analytical question
Interpret visualizations to create observations
Explain outliers, trends, and relationships between data elements
Analyze results
Examine your point of view for objectivity
Question visualizations to develop insights
Test for correlations and causations in the data
Act on results
Recommend actions based on analysis
Predict potential consequences of actions to minimize unintended consequences
Assess actual results of actions
Share results
Summarize audience member types and what they care about
Justify your analysis, observations, and insights
Support a data literacy culture
Rewarding and incentivizing the use of data
Challenging non-data influenced decisions
Why Data Literacy is important
Qlik Sense Business Analyst Qualification
The Qlik Sense Business Analyst Qualification measures your fundamental skills and applied knowledge. It requires you to build a Qlik Sense application and complete a multiple-choice question exam. You will have 1 hour 15 minutes to complete this exam.
NOTE: This is not our expert-level certification.
Prerequisites are identified on the Qualification Course Page
Ability to build a Qlik Sense application
Business Analyst courses in the Qlik Continuous Classroom
Create Visualizations with Qlik Sense Instructor-Led Training
Describe the various views in Qlik Sense
Explain the value of stories
Demonstrate building stories
Describe circumstances in which to use stories
Describe the use of effects in stories
Identify the components of an app
Demonstrate creating basic sheets
Examine and conduct an analysis in an app
Tell a simple story
Describe how to create a bookmark
Describe elements saved in a bookmark
Manage bookmarks
Search for and apply bookmarks
Explain the value of creating master items
Explain the process to create master items
Analyze data for other users
Demonstrate configuring master dimensions
Compare the 'Add data' and 'Data load editor' options
Compare the app sections for loading data into an app
Demonstrate loading tables from multiple data sources
Explain how key fields are identified and associations created
Use various chart types
Select the appropriate chart type
Apply visual analysis features
Distinguish between Qlik Sense Enterprise, Qlik Sense Desktop, and Qlik Sense Cloud
Determine which features are locked upon publishing
Describe the value of sharing apps in Qlik Sense Cloud
Describe the benefits of 'in-memory' data
Reload data in Qlik Sense Desktop
Explain data reload tasks in Qlik Sense Enterprise
Describe the benefits of the associative selection model
Demonstrate selecting data in visualizations
Describe the benefits of the selections bar and selections tool
Explain the uses and benefits of snapshots
Demonstrate taking a snapshot
Use snapshot annotations
Demonstrate managing the snapshot library
Demonstrate adding a snapshot to a story
Explain the difference between dimensions and measures
Apply dimensions and measures to visualizations
Determine which chart type to use based on dimensions and measures
Demonstrate configuring alternative dimensions and measures
Identify the various types of searches
Explain what is searched for each type
Demonstrate the various types of searched
Qlik Sense Data Architect Qualification
The Qlik Sense Data Architect Qualification measures your fundamental skills and applied knowledge. It requires you to build a Qlik Sense application and complete a multiple-choice question exam. You will have 1 hour 30 minutes to complete this exam.
NOTE: This is not our expert-level certification.
Prerequisites are identified on the Qualification Course Page
Ability to build a Qlik Sense application
Data Architect courses in the Qlik Continuous Classroom or
Data Modeling for Qlik Sense Instructor-Led Training
Describe the various views in Qlik Sense
Evaluate when to use the 'Add data' option or the data load editor
Demonstrate loading tables from multiple data sources
Explain how key fields are identified and associations created
Demonstrate using the data model viewer to evaluate the underlying data
Explain the capabilities of the data manager view
Explain how the data manager creates and edits data load script
Demonstrate accessing and using association recommendations
Describe the purpose of the data load script
Apply techniques resolve data issues
Demonstrate creating a connection to a database
Use various database file connection options
Use the data connections during execution
Evaluate associations created by aliasing fields in the data load script
Describe a table loading structure
Demonstrate transforming data using expressions
Evaluate scenarios in which to use a stacked preceding load
Determine causes of synthetic keys in a data model
Recommend solutions for resolving synthetic keys
Recommend solutions for resolving circular references
Demonstrate creating concatenated keys
Demonstrate fully qualifying field names
Examine solutions to script errors using the debugging tool
Demonstrate locating and identifying errors in the data load script
Demonstrate creating a data comparison table in a data model
Demonstrate creating a validation object in an app
Use counter fields for row counting
Appreciate what is meant by 'in-memory' data
Describe how data reload tasks are handled in Qlik Sense Enterprise
Explain visibility of variables in an INCLUDE statement
Demonstrate creating a custom sort order with INLINE statement
Demonstrate creating a subset table with RESIDENT statement
Determine when to create a dedicated calendar dimension
Demonstrate creating a master calendar table
Evaluate the various methods of combining tables
Demonstrate various types of table concatenation
Demonstrate various types of joins
Evaluate scenarios for using mapping tables
Demonstrate loading data from Qlik DataMarket into a data model
Evaluate the changes to the data load script when using Qlik DataMarket
Explain the value of creating master items
Explain the process to create master items