QSDA2024 Exam Dumps - Qlik Sense Data Architect Certification Questions and Answers

When developing multiple Qlik Sense applications (Sales, Finance, Operations) that share numerous identical calculation expressions, it is crucial to have a centralized, reusable script to avoid redundancy, save time on modifications, and increase the reusability of the assets.

The best approach in Qlik Sense to achieve these goals is to use theIncludescript function. This function allows the data architect to reference a script file that is stored on a file server. The Include function willinject the contents of the external script file into the Qlik Sense script at the point where the Include statement is called. This means that all three apps (Sales, Finance, Operations) can include this common script, and any updates made to the script will automatically apply to all apps that include it.

This method provides a highly maintainable solution because:

No Duplicate Script:The shared logic is maintained in a single file, eliminating redundancy.

Ease of Modifications:Any changes made to the script are propagated to all applications that include it.

Reusable Assets:The script can be reused across different applications, enhancing efficiency and consistency.

In this scenario, the key is composed of three components: Country, UserID, and Department, separated by spaces. The UserID itself consists of a two-character country code followed by a unique code of two or three digits. The objective is to extract only this unique numeric code from the UserID field.

Explanation of the Correct Function:

Option A: RIGHT(SUBFIELD(Key, ' ', 2), 3)

SUBFIELD(Key, ' ', 2):This function extracts the second part of the key (i.e., the UserID) by splitting the string using spaces as delimiters.

RIGHT(..., 3):After extracting the UserID, the RIGHT() function takes the last three characters of the string. This works because the unique code is either two or three digits, and the RIGHT() function will retrieve these digits from the UserID.

This combination ensures that the data architect extracts the unique code from the UserID field correctly.

The requirement is to create a measure that identifies the salesperson with the highest sales. If multiple salespeople have the same highest sales amount, the measure should return all of those salespeople.

Explanation of Option A:

Rank(Sum(Sales), 1):The Rank() function is used to rank salespersons based on the sum of their sales. The rank 1 indicates the top position.

Aggr() Function:This function aggregates the data and returns the results grouped by the SalesPerson field.

IF() Condition:The IF condition checks if the salesperson's rank is 1 (highest sales).

Concat(DISTINCT ...):The Concat() function concatenates all the salespersons who have the highest sales, separated by spaces or another delimiter, ensuring that all top performers are returned.

Example:

If three salespersons have the highest sales, this expression will return all three names separated by a space.

In the given data model, there are several issues related to table relationships and key fields that need to be addressed to create a functional and optimized data model. Here's how each step in the chosen solution (Option C) resolves these issues:

Create a key with OrderID and ProductID in the OrderDetails table and in the Shipments table:

By creating a composite key with OrderID and ProductID, you uniquely identify each line item in both the OrderDetails and Shipments tables. This step is crucial for ensuring that each product within an order is correctly associated with its respective shipment.

Delete the ShipmentID in the Orders table:

The ShipmentID in the Orders table is redundant because the Shipments table already captures this information at a more granular level (i.e., at the product level). Removing ShipmentID avoids potential circular references or synthetic keys.

Delete the ProductID and OrderID in the Shipments table:

After creating the composite key in step 1, the individual ProductID and OrderID fields in the Shipments table are no longer necessary for joins. Removing them reduces redundancy and simplifies the table structure.

Concatenate Orders and OrderDetails:

Concatenating Orders and OrderDetails into a single table creates a unified table that contains all necessary order-related information. This helps in simplifying the model and avoiding issues related to managing separate but related tables.

Create a link table using the MasterCalendar table and create a concatenated field between OrderDate and ShipmentDate:

A link table is created to associate the combined table with the MasterCalendar. By creating a concatenated field that combines OrderDate and ShipmentDate, you ensure that both dates are properly linked to the calendar, allowing for accurate time-based analysis.

In the scenario described, the Dynamic Dimension data tracks changes in department assignments for salespeople over time. To correctly link the transaction data with the salesperson data and ensure that sales are associated with the correct department based on the date, an IntervalMatch function should be used.

IntervalMatchis designed to match discrete data (like transaction dates) with a range of dates. In this case, each salesperson's department assignment is valid over a period of time, and the IntervalMatch function can be used to link the transaction data with the correct department for each salesperson based on the transaction date.

Option A (Merge):This option is incorrect as it refers to combining data sets, which doesn't address the need to handle the dynamic, date-based department assignments.

Option B (IntervalMatch):This is the correct choice because it allows you to match each transaction with the correct department assignment based on the ChangeDate in the Dynamic Dimension data.

Option C (Partial Reload):This refers to reloading only part of the data, which is not relevant to linking tables based on date ranges.

Option D (Semantic):This option is not applicable as it refers to a broader approach to data modeling and interpretation rather than specifically linking data based on time intervals.

Thus,IntervalMatchis the correct method for linking the transaction data with the dynamic salesperson dimension, ensuring that each transaction is associated with the correct department based on the historical assignment data.