QSDA2024 Qlik Sense Data Architect Certification Exam – 2024 Questions and Answers

In the script provided, there are two tables being loaded inline: Table_A and Table_B. The script uses the Join function to combine Table_B with Table_A based on the common field Field_1. Here’s how the join operation works:

Table_A initially contains three records with Field_1 values of 01, 01, and 02.

Table_B contains two records with Field_1 values of 01 and 03.

When Join(Table_A) is executed, Qlik Sense will perform an inner join by default, meaning it will join rows from Table_B to Table_A where Field_1 matches in both tables. The result is:

For Field_1 = 01, there are two matches in Table_A and one match in Table_B. This results in two records in the joined table where Field_4 and Field_5 values from Table_B are repeated for each match in Table_A.

For Field_1 = 02, there is no corresponding Field_1 = 02 in Table_B, so the Field_4 and Field_5 values for this record will be null.

For Field_1 = 03, there is no corresponding Field_1 = 03 in Table_A, so the record from Table_B with Field_1 = 03 is not included in the final joined table.

Thus, the correct output will look like this:

Field_1 = 01, Field_2 = AB, Field_3 = 10, Field_4 = 30%, Field_5 = 500

Field_1 = 01, Field_2 = AC, Field_3 = 50, Field_4 = 30%, Field_5 = 500

Field_1 = 02, Field_2 = AD, Field_3 = 75, Field_4 = null, Field_5 = null

In this scenario, the data architect wants to transform the input dataset, which is in a key-value pair structure, into a table where each attribute becomes a column with its corresponding value under the relevant key.

Understanding the Requirement:

The input data consists of three fields: Key, Attribute, and Value.

The desired output structure has the Key as a primary identifier, and the Attributes (like Color, Diameter, Height, etc.) are spread across the columns, with corresponding values filled in each row.

Best Method to Achieve this Transformation:

The appropriate method to convert key-value pairs into a structured table where each unique attribute becomes a separate column is the Generic Load function in Qlik Sense.

Why Generic?

Generic Load is specifically designed for situations where data is stored in a key-value format (like the one provided) and needs to be converted into a more traditional tabular format, with attributes as columns.

It creates a separate table for each combination of Key and Attribute, effectively "pivoting" the attribute values into columns in the output table.

How it Works:

When applying a GENERIC LOAD to the input dataset, Qlik Sense will generate multiple tables, one for each Attribute. However, in the final data model, Qlik Sense automatically joins these tables by the Key field, effectively producing the desired output structure.

References :

Qlik Sense Documentation on Generic Load : The documentation outlines how to use the Generic Load to handle key-value pairs and pivot them into a more traditional table format.

In this scenario, the company generates 1 GB of ticketing data daily, accumulating up to 720 GB over two years. Business users mainly require trend analysis for the past two years and rarely need to access the transaction-level data. The objective is to load only the necessary data while ensuring the system remains performant.

Option C is the optimal choice for the following reasons:

Efficiency in Data Handling :

By loading only aggregated data for the two years, the app remains lean, ensuring faster load times and better performance when users interact with the dashboard. Aggregated data is sufficient for analyzing trends, which is the primary use case mentioned.

On-Demand App Generation (ODAG) :

ODAG is a feature in Qlik Sense designed for scenarios like this one. It allows users to generate a smaller, transaction-level dataset on demand. Since users rarely need to drill down into transaction-level data, ODAG is a perfect fit. It lets users load detailed data for specific dates only when needed, thus saving resources and keeping the main application lightweight.

Performance Optimization :

Loading only aggregated data ensures that the application is optimized for performance. Users can analyze trends without the overhead of transaction-level details, and when they need more detailed data, ODAG allows for targeted loading of that data.

References :

Qlik Sense Best Practices : Using ODAG is recommended when dealing with large datasets where full transaction data isn't frequently needed but should still be accessible.

Qlik Documentation on ODAG : ODAG helps in maintaining a balance between performance and data availability by providing a method to load only the necessary details on demand.

In the provided data model, there is an issue where certain types of analysis over time are not showing the expected data. This problem is often caused by a mismatch in the data formats of the OrderDate field between the Orders and MasterCalendar tables.

Option A: DatefFloor(OrderDate)) would round down to the nearest date boundary, which might not address the root cause if the issue is related to different date and time formats.

Option B: TimeStamp#(OrderDate, 'M/D/YYYY hh.mm.ff') ensures that the date is interpreted correctly as a timestamp, but this does not resolve potential mismatches in date format directly.

Option C: TimeStamp(OrderDate) will keep both date and time, which may still cause mismatches if the MasterCalendar is dealing purely with dates.

Option D: Date(OrderDate) formats the OrderDate to show only the date portion (removing the time part). This function will ensure that the date values are consistent across the Orders and MasterCalendar tables by converting the timestamps to just dates. This is the most straightforward and effective way to ensure consistency in date-based analysis.

In Qlik Sense, dates and timestamps are stored as dual values (both text and numeric), and mismatches can lead to incomplete or incorrect analyses. By using Date(OrderDate) in both the Orders and MasterCalendar tables, you ensure that the analysis will have consistent date values, resolving the issue described.

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.

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.