The Qlik Sense Data Architect Certification Exam - 2024 (QSDA2024) validates your ability to design and implement scalable data architectures using Qlik Sense. This exam is intended for professionals who build data models, manage data connectivity, and ensure data quality in enterprise environments. This page provides a structured study guide, topic breakdown, and practical preparation strategies to help you approach the exam with confidence. Whether you're advancing your Qlik career or seeking formal recognition of your expertise, understanding the exam structure and content domains is the first step toward success.
Use this topic map to guide your study for Qlik QSDA2024 (Qlik Sense Data Architect Certification Exam - 2024) within the Qlik Sense path.
The QSDA2024 exam uses multiple question formats to assess both theoretical knowledge and practical decision-making skills. Questions progress in difficulty and reflect real-world scenarios that data architects encounter.
Questions are designed to measure not just recall but your ability to apply concepts to solve actual architecture and data quality problems.
An effective study plan maps each topic to a weekly focus area, allowing you to build knowledge progressively and reinforce connections between concepts. Dedicate time to both theory and hands-on practice to develop the judgment needed for scenario-based questions.
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Data Model Design and Data Transformations typically account for a larger portion of the exam because they directly impact the performance and usability of Qlik Sense applications. However, all five domains are tested, and a weakness in any area, especially Validation, can affect your overall score. Allocate study time proportionally, but ensure you have foundational knowledge across all topics.
Requirements gathering directly informs your model design decisions. When you understand the business questions and analytical needs, you can design a data model that answers those questions efficiently. Skipping or rushing the requirements phase often leads to models that require rework later. On the exam, scenario questions frequently test your ability to trace from a stated requirement to the appropriate model structure.
Building at least two complete data solutions in Qlik Sense, from connection setup through validation, is ideal. Focus on projects that involve multiple data sources, transformation logic, and quality checks. Lab work with different data types (relational databases, flat files, APIs) and different model complexities (simple star schema to more complex scenarios) will prepare you well for scenario-based questions.
Frequent errors include overlooking data quality issues before loading, misunderstanding the difference between fact and dimension tables, and choosing a connectivity approach without considering security or performance implications. Many candidates also underestimate the importance of documentation and validation steps. Review scenario questions carefully to ensure you're addressing all aspects of the problem, not just the most obvious one.
Spend the first half of the week reviewing high-weight topics and taking a full-length practice test under exam conditions. Use the second half to review mistakes, focus on weak areas, and do targeted question sets rather than trying to re-learn entire topics. Rest well the night before the exam, and do a light review of key definitions and decision trees on exam day morning to build confidence without overloading your mind.
The data architect has been tasked with building a sales reporting application.
* Part way through the year, the company realigned the sales territories
* Sales reps need to track both their overall performance, and their performance in their current territory
* Regional managers need to track performance for their region based on the date of the sale transaction
* There is a data table from HR that contains the Sales Rep ID, the manager, the region, and the start and end dates for that assignment
* Sales transactions have the salesperson in them, but not the manager or region.
What is the first step the data architect should take to build this data model to accurately reflect performance?
In the provided scenario, the sales territories were realigned during the year, and it is necessary to track performance based on the date of the sale and the salesperson's assignment during that period. The IntervalMatch function is the best approach to create a time-based relationship between the sales transactions and the sales territory assignments.
IntervalMatch: This function is used to match discrete values (e.g., transaction dates) with intervals (e.g., start and end dates for sales territory assignments). By matching the transaction dates with the intervals in the HR table, you can accurately determine which territory and manager were in effect at the time of each sale.
Using IntervalMatch, you can generate point-in-time data that accurately reflects the dynamic nature of sales territory assignments, allowing both sales reps and regional managers to track performance over time.
Refer to the exhibit.
Refer to the exhibit.
A data architect needs to create a data model for a new app. Users must be able to see:
* Total sales for each customer
* Total sales for a given state
* Customers that have not had any sales
* Names of salesperson and regional account managers
* Total number of sales by date
Which steps should the data architect perform to meet these requirements?
Which steps should the data architect perform to meet these requirements?
In the provided scenario, the data architect needs to create a data model that supports various analyses, including total sales for each customer, total sales by state, identifying customers with no sales, and displaying the names of salespersons and regional account managers.
Here's why Option C is the correct choice:
Loading the Sales Table: The Sales table contains key information related to sales transactions, including SaleID, CustomerID, Amount, SaleDate, SalesPersonID, and RegionalAcctMgrID. This table must be loaded first as it will be central to the analysis.
Loading the Customers Table: The Customers table includes customer details such as CustID, CustName, Address, City, State, and Zip. Loading this table and linking it to the Sales table via the CustomerID field allows you to perform analyses such as total sales per customer and total sales by state. Importantly, loading the customers separately will also allow the identification of customers without any sales.
Loading the Employees Table Twice: The Employees table must be loaded twice because it is used to look up two different roles in the sales process: the SalesPersonID and the RegionalAcctMgrID. When loading the table twice:
The first instance of the Employees table will be used to map the SalesPersonID to EmployeeName.
The second instance will be used to map the RegionalAcctMgrID to EmployeeName.
Aliasing the EmployeeID field appropriately in each instance is crucial to prevent creating synthetic keys and to ensure the correct association with the roles in the sales process.
This approach ensures that the data model will correctly support all the required analyses, including identifying customers without sales, which is crucial for meeting the business requirements.
Option A and Option B propose using a mapping load and ApplyMap, which can complicate the model and does not directly address all the business requirements.
Option D involves aliasing fields in a way that could create unnecessary complexity and might not accurately reflect the relationships in the data.
Thus, Option C is the correct answer as it best meets the requirements while maintaining a clear and functional data model.
Refer to the exhibit.
A data architect needs to build a dashboard that displays the aggregated sates for each sales representative. All aggregations on the data must be performed in the script.
Which script should the data architect use to meet these requirements?
A)
B)
C)
D)
The goal is to display the aggregated sales for each sales representative, with all aggregations being performed in the script. Option C is the correct choice because it performs the aggregation correctly using a Group by clause, ensuring that the sum of sales for each employee is calculated within the script.
Data Load:
The Data table is loaded first from the Sales table. This includes the OrderID, OrderDate, CustomerID, EmployeeID, and Sales.
Next, the Emp table is loaded containing EmployeeID and EmployeeName.
Joining Data:
A Left Join is performed between the Data table and the Emp table on EmployeeID, enriching the data with EmployeeName.
Aggregation:
The Summary table is created by loading the EmployeeName and calculating the total sales using the sum([Sales]) function.
The Resident keyword indicates that the data is pulled from the existing tables in memory, specifically the Data table.
The Group by clause ensures that the aggregation is performed correctly for each EmployeeName, summarizing the total sales for each employee.
Key Qlik Sense Data Architect Reference:
Resident Load: This is a method to reuse data that is already loaded into the app's memory. By using a Resident load, you can create new tables or perform calculations like aggregation on the existing data.
Group by Clause: The Group by clause is essential when performing aggregations in the script. It groups the data by specified fields and performs the desired aggregation function (e.g., sum, count).
Left Join: Used to combine data from two tables. In this case, Left Join is used to enrich the sales data with employee names, ensuring that the sales data is associated correctly with the respective employee.
Conclusion: Option C is the most appropriate script for this task because it correctly performs the necessary joins and aggregations in the script. This ensures that the dashboard will display the correct aggregated sales per employee, meeting the data architect's requirements.
A data architect in the Enterprise Architecture team wants to develop a new application summarizing Qlik Sense usage by all company employees. They also want to gather usage metrics for other systems.
Who should the data architect contact to be granted access to the data?
When developing an application that summarizes Qlik Sense usage by company employees and also gathers usage metrics for other systems, the data architect needs to ensure they have the correct access to sensitive data. The following roles are crucial:
IT Security Director: Responsible for the security of IT systems and data. They would ensure that the data architect has the appropriate permissions to access usage metrics and other system data securely.
Human Resources Director: They manage employee-related data, including employment records that might be necessary for matching employee IDs with usage metrics. This access is crucial for correlating usage data with specific employees.
Qlik Sense Administrator: This individual has administrative rights over the Qlik Sense environment and can grant access to usage data within Qlik Sense, ensuring that the architect has the necessary data to analyze.
Given the need to securely and correctly handle sensitive data, including employee usage metrics across multiple systems, Option A includes all the appropriate contacts for access and permissions.
Exhibit.
Refer to the exhibit.
A major healthcare organization requests a new app with the following requirements:
* Users can filter AdmissionDate and DischargeDate by all fields in the Master Calendar table
* Use an existing QVD file, which includes dates 20 years into the future
* Users should not be able to filter on dates that have no associated encounters
Which approach should the data architect take to meet these requirements?
In the scenario presented, a major healthcare organization needs an app that allows users to filter AdmissionDate and DischargeDate by all fields in the Master Calendar table, while also ensuring that users cannot filter on dates that have no associated encounters.
To meet these requirements, the most appropriate approach is to:
Load the Master Calendar twice, once as AdmissionCalendar and once as DischargeCalendar. Each instance should have its fields appropriately aliased to reflect whether they pertain to Admission or Discharge dates.
Load the Encounters table as usual, but now you have two separate calendar tables that can be linked to the appropriate date fields (AdmissionDate and DischargeDate) in the Encounters table.
This approach ensures:
Users can filter both AdmissionDate and DischargeDate independently using the fields in their respective calendar tables.
Only relevant dates associated with actual encounters will be available for filtering, as the calendars are linked specifically to the AdmissionDate and DischargeDate fields.
Efficiency and clarity in the data model, as the fields from the Master Calendar are distinctly assigned to either Admission or Discharge, avoiding any confusion or incorrect filtering.
This method avoids unnecessary complexity and directly meets the healthcare organization's requirements in a straightforward and scalable manner.
