QSDA2024 Sample Questions Answers

In this scenario, the data architect is loading two tables, Orders and Shipments, into Qlik Sense, and a synthetic key is being generated due to the presence of shared fields (OrderID and LineNo) between these tables.

Understanding the Issue:

Synthetic Keys: Qlik Sense automatically creates synthetic keys when two or more tables share multiple fields with the same names. While synthetic keys aren't necessarily problematic, they can sometimes lead to incorrect or unexpected data associations and should be resolved when possible to maintain clarity and control over the data model.

The tables Orders and Shipments share the fields OrderID and LineNo. In this context, these fields together uniquely identify each record, so they are both necessary for accurate data linkage.

Correct Resolution Approach:

Option C: Create a composite key using OrderID and LineNois the best approach.

Here's why:

Composite Key Creation:

By creating a composite key that combines OrderID and LineNo (e.g., OrderID & '-' & LineNo), you ensure that each line in the orders and shipments tables is uniquely identified. This composite key will accurately link the related records from the Orders and Shipments tables.

Avoiding Synthetic Keys:

By manually creating this composite key, you eliminate the need for Qlik Sense to generate a synthetic key, thereby simplifying the data model and ensuring that data associations are clear and controlled.

Retaining Both Fields:

This approach allows you to keep both OrderID and LineNo as separate fields in your tables if needed for other analyses or reporting purposes, while using the composite key for linking the tables.

References:

Qlik Sense Data Modeling Best Practices: When dealing with multiple fields that are used together to uniquely identify records, it is recommended to create composite keys rather than relying on Qlik Sense's synthetic keys for clarity and better control.

To meet the requirements of displaying the current year's total sales in a way that responds to user selections, the correct approach involves using both SET and LET statements to define the necessary variables in the data load editor.

Explanation of Option C:

SET vCurrentYear = Year(Today());

The SET statement is used here to assign the current year to the variable vCurrentYear. The SET statement treats the variable as a text string without evaluation. This is appropriate for a variable that will be used as part of an expression, ensuring the correct year is dynamically set based on the current date.

LET vCurrentYTDSales = '=SUM({$<Year={'$(vCurrentYear)'}>} [Sales Amount])';

The LET statement is used here to assign an evaluated expression to the variable vCurrentYTDSales. This expression calculates the Year-to-Date (YTD) sales for the current year by filtering the Year field to match vCurrentYear. The LET statement ensures that the expression inside the variable is evaluated, meaning that when vCurrentYTDSales is called in a chart or KPI, it dynamically calculates the YTD sales based on the current year and any user selections.

Key Points:

Dynamic Year Calculation: Year(Today()) dynamically calculates the current year every time the script runs.

Responsive to Selections: The set analysis syntax {$} ensures that the sales totals respond to user selections while still focusing on the current year's data.

Appropriate Use of SET and LET: The combination of SET for storing the year and LET for storing the evaluated sum expression ensures that the variables are used effectively in the application.

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.

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

Table_Bcontains 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 the scenario, two tables (OrderDetails and Customers) are being loaded into the Qlik Sense data model, and these tables are related via the fields CustomerID and CustomerKey. The goal is to ensure that the relationship between these two tables is correctly established in Qlik Sense without creating synthetic keys or data inconsistencies.

Option A:Renaming CustomerKey to CustomerID in the OrderDetails table ensures that the fields will have the same name across both tables, which is necessary to create the relationship. However, renaming is done using AS, which might create an issue if the fields in the original data source have a different meaning.

Option B and C:These options use AUTONUMBER to convert the CustomerKey and CustomerID to unique numeric values. However, using AUTONUMBER for both fields without ensuring they are aligned correctly might lead to incorrect associations since AUTONUMBER generates unique values based on the order of data loading, and these might not match across tables.

Option D:This approach loads the tables with their original field names and then uses the RENAME FIELD statement to align the field names (CustomerKey to CustomerID). This ensures that the key fields are correctly aligned across both tables, maintaining their relationship without introducing synthetic keys or mismatches.

In this scenario, the focus of the project is to enhance inventory analytics, specifically targeting forecasting inventory levels. The primary goal is to understand the factors influencing inventory management and to build a model that helps in predicting future inventory needs.

Option A: Product Buyeris the correct stakeholder to consult first.

Here’s why:

Direct Involvement in Inventory Management:

The Product Buyer is typically responsible for making decisions related to purchasing and maintaining inventory levels. They have a deep understanding of the factors that influence inventory needs, such as lead times, supplier reliability, demand forecasting, and purchasing cycles.

Knowledge of Inventory Requirements:

Since the project’s primary focus is forecasting inventory levels, the Product Buyer will provide crucial insights into the variables that affect inventory and the data needed for accurate forecasting. They can guide what historical data is essential and what external factors might need to be considered in the forecasting model.

Alignment with Business Objectives:

By consulting the Product Buyer, the project can ensure that the inventory forecasting models align with the company’s inventory management objectives, avoiding overstocking or understocking, and thus optimizing costs.

References:

Qlik Project Management Best Practices: In analytics projects, particularly those focused on specific operational areas like inventory management, consulting the stakeholders who are closest to the operational data and decision-making processes ensures that the solution will be relevant and effective.

In this scenario, the data architect needs to load Table_A from an Excel file and ensure that the data is sorted by Field_2. The key here is to correctly load and sort the data in the script.

Understanding the Options:

Option A:

First, it loads the data into a temporary table (Temp) from the Excel file.

Then, it loads the data from the temporary table (Temp) into Table_A, using the ORDER BY Field_2 ASC clause to sort the data by Field_2.

Finally, it drops the temporary table (Temp), leaving the sorted data in Table_A.

Option B:

Directly loads the data from the Excel file into Table_A and applies the ORDER BY Field_2 ASC clause in the same step.

However, the ORDER BY clause in a direct load from an external source like Excel might not work as expected because Qlik Sense does not support ORDER BY when loading directly from a file.

Option C:

Similar to Option A but uses the NoConcatenate keyword to prevent concatenation, which is unnecessary since Temp and Table_A have different names.

While this script works, the NoConcatenate keyword is redundant in this context.

Option D:

The ORDER BY Field_2 ASC is placed before the LOAD statement, which is not a correct usage in Qlik Sense script syntax.

Correct Script Choice:

Option Ais the correct script because it correctly sorts the data after loading it into a temporary table and then loads the sorted data into Table_A. This method ensures that the data is sorted by Field_2 and avoids any issues related to sorting during the initial data load.

References:

Qlik Sense Scripting Best Practices: When sorting data in Qlik Sense, the correct approach is to use a RESIDENT LOAD with an ORDER BY clause after loading the data into a temporary table.

In Qlik Sense, when a data load script is executed and an error occurs, the script execution is halted immediately, and any tables that were being loaded at the time of the error are discarded. However, the existing data model—i.e., the last successfully loaded data model—remains intact and is not affected by the failed script. This ensures that the application retains the last known good state of the data, avoiding any partial or inconsistent data loads that could occur due to an error.

When the script encounters an error:

The tables that were successfully loaded prior to the error are retained in the session, but these tables are not merged with the existing data model.

The existing data model before the script was executed remains unchanged and is maintained.

No partial or incomplete data is loaded into the application; hence, the data model remains consistent and reliable.

Qlik Sense Data Architect ReferencesThis behavior is designed to protect the integrity of the data model. In scenarios where script execution fails, the user can debug and fix the script without risking the data integrity of the existing application. The key references include:

Qlik Help Documentation: Provides detailed information on how Qlik Sense handles script errors, highlighting that the existing data model remains unchanged after an error.

Data Load Editor Practices: Best practices dictate ensuring that the script is fully functional before executing it to avoid data inconsistency. In cases where an error occurs, understanding that the current data model is maintained helps in strategic debugging and script correction.

In this scenario, the data architect needs to conform the revenue data from Company B to match the data standard of Company A, where $0 is explicitly used to represent journeys without revenue.

Explanation of the Correct Script:

Option A:money(replace(Revenue, chr(32), 0)) AS [Revenue Conformed]

replace(Revenue, chr(32), 0):This part of the expression replaces any spaces (ASCII character code 32) in the Revenue field with 0.

money(...):This function formats the resulting value as currency. Since Company B may have either null values or spaces where 0 should be, this script ensures that any blanks are replaced with 0 and then formatted as currency.

Why Option A is Correct:

Handling Spaces:The replace() function is effective in replacing spaces with 0, conforming to Company A's standard of using $0 for non-revenue journeys.

Handling NULL Values:The money() function is used to ensure the final output is formatted as currency. However, it's important to note that NULL values are not directly handled by the replace() function, which is why it is applied before money() to deal with spaces.

The expression sum([OrderNetAmount]) sums the values in the OrderNetAmount field across the dataset. Given that the dataset includes an inline table that is joined with another, the expression calculates the sum of OrderNetAmount for all selected rows. In this scenario, all values in LineNo are selected, which doesn't affect the summation of OrderNetAmount because LineNo isn't directly used in the sum calculation.

Step-by-step Calculation:

The Orders table contains the OrderNetAmount for each order. The values provided are 90, 500, 100, and 120.

Adding these values together:90+500+100+120=81090 + 500 + 100 + 120 = 81090+500+100+120=810

However, after the Left Join operation with the OrderDetails table, some of these rows might be duplicated if the join results in multiple matches. But since the field being summed, OrderNetAmount, is from the original Orders table and not affected by the details in OrderDetails, the sum still remains consistent with the original values in the Orders table.

Thus, the sum of OrderNetAmount is 149014901490, based on the combined effects of the original data structure and the join operation.

The issue described is that not all orders for a selected date are shown. This issue arises because the original script uses the Date(OrderTime) function, which only extracts the date part of the OrderTime timestamp, potentially resulting in incorrect matching when filtering by date due to the time component still being present in the underlying data.

Explanation of Option D:

Floor(OrderTime): The Floor() function truncates the OrderTime timestamp to remove the time component, leaving only the date part. This ensures that all orders on the same date are treated equally, without any interference from the time component.

Date(Floor(OrderTime), 'YYYY-MM-DD'): The Date() function formats the floored value into a date format (YYYY-MM-DD), which is essential for consistent date comparison.

This approach ensures that when you select a date in the application, all orders for that date are shown, as the time component has been effectively removed.

In the scenario provided, the data architect needs to load two tables (Budget and Sales) without creating a synthetic key, while ensuring that expressions like Sum([Budgeted Sales]) are calculated correctly.

Here is a breakdown of the options:

Option A (Outer Join):This option uses an outer join between the Sales table and the Budget table. While this approach will combine the tables based on the common fields (Year and Region), it will result in a single table that contains all fields from both tables. This approach prevents the creation of a synthetic key and retains all records from both tables, ensuring that all budgeted and actual sales data is available. As a result, calculations like Sum([Budgeted Sales]) will work correctly.This is the correct solution.

Option B (Concatenate):This option uses concatenate, which combines the tables by stacking them on top of each other as if they were one table. This approach will not prevent synthetic keys and could cause issues with calculations since Budgeted Sales and Actual Sales would be in the same column, leading to incorrect aggregation results.

Option C (Separate Load):This option simply loads the tables separately without any join or concatenation. While this will not create a synthetic key, it will result in two separate tables in the data model. Without any connection between these tables, calculations involving both Budgeted Sales and Actual Sales will not work correctly.

Option D (Inner Join):This option uses an inner join, which will combine only the records that match in both tables based on Year and Region. While this approach avoids synthetic keys, it may exclude records that do not have a corresponding match in both tables, potentially leading to incomplete data.

Given the requirements to avoid synthetic keys and ensure correct calculations,Option A (Outer Join)is the most appropriate approach. It ensures all relevant data is included and that the data model remains free from synthetic keys, while also allowing accurate calculations.