Pass the Qlik Qlik Sense Data Architect QSDA2024 Questions and answers with CertsForce

The given Excel spreadsheet has a Region field where the region value is only specified for the first record within each region. The data architect needs to fill in the missing region values for subsequent rows.

Previous() Function: The Previous() function in Qlik Sense returns the value of the expression from the previous row. In this case, it can be used to fill down the Region values so that each row contains the correct region information.

Implementation: The script can be designed to check if the current row’s Region value is missing (null). If it is missing, the script can assign the value from the previous row using the Previous() function.

LOAD

If(IsNull(Region), Previous(Region), Region) AS Region,

This logic fills in the missing Region values with the value from the preceding row, which effectively resolves the issue shown in the spreadsheet.

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 References:

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.

In the provided scenario, the goal is to export tables from a Qlik Sense model based on rules specified in an external text file. The structure of the text file indicates which table to export, the filename to use, and how many copies to create.

Given this structure, the data architect needs to:

Loop through each row in the text file to process each table.

Use an IF statement to check whether the specified table exists in the model (though it's mentioned they are verified to exist, this step may involve conditional logic to ensure the rules are correctly followed).

Use another IF statement to handle the creation of multiple copies, ensuring each file is named incrementally (e.g., Clients1.qvd, Clients2.qvd, etc.).

Key Script Strategies:

Loop: A loop is necessary to iterate through each row of the text file to process the tables specified for export.

IF Statements: The first IF statement checks conditions such as whether the table should be exported (based on additional logic if needed). The second IF statement handles the creation of multiple copies by incrementing the filename.

This approach covers all the necessary logic with the minimum set of scripting strategies, ensuring that each table is exported according to the rules defined.

In the scenario described, the goal is to load all CSV files from a directory into Qlik Sense, while ignoring the log files that are also present in the same directory. The correct approach should allow for dynamic file loading without needing to manually specify each file name, especially since the log files change regularly.

Here’s whyOption Bis the correct choice:

Option A:This method involves manually specifying a list of files (Day1, Day2, Day3) and then iterating through them to load each one. While this method would work, it requires knowing the exact file names in advance, which is not practical given that new files are added regularly. Also, it doesn’t handle dynamic file name changes or new files added to the folder automatically.

Option B:This approach uses a wildcard (*) in the file path, which tells Qlik Sense to load all files matching the pattern (in this case, all CSV files in the directory). Since the csv file extension is explicitly specified, only the CSV files will be loaded, and the log files will be ignored. This method is efficient and handles the dynamic nature of the file names without needing manual updates to the script.

Option C:This option is similar to Option B but targets text files (txt) instead of CSV files. Since the requirement is to load CSV files, this option would not meet the needs.

Option D:This option uses a more complex approach with filelist() and a loop, which could work, but it’s more complex than necessary. Option B achieves the same result more simply and directly.

Therefore,Option Bis the most efficient and straightforward solution, dynamically loading all CSV files from the specified directory while ignoring the log files, as required.

In the scenario where the startup company is preparing for an IPO, there is an increased need for meticulous record-keeping, including the recording of variable values used in Qlik Sense applications. The TRACE statement is the most suitable option for logging variable values during script execution.

TRACE: This statement writes custom messages, including variable values, to the script execution log. By using TRACE, you can ensure that every reload log contains the names and current values of all relevant variables, providing the necessary transparency and traceability.

For example, the script could include:

TRACE $(VariableName);

This command will output the variable's value in the script log, ensuring it is recorded for audit purposes.