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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

The issue described relates to the incorrect display of month and year values on the x-axis of a chart. The source data has dates in the M/D/YYYY format, and a calculated field named Month-Year was created using this date format.

To correct the issue:

The correct approach is to use the MonthStart() function, which returns the first date of the month for the provided date. This ensures consistency in month-year representation.

The Date() function is then used to format the result of MonthStart() to the desired format of MMM-YYYY (e.g., Feb-2018).

Explanation of the Correct Expression:

MonthStart([Common Date]): This ensures that all dates within a month are treated as the first day of that month, which is critical for accurate monthly aggregation.

Date(..., 'MMM-YYYY'): This formats the result to show just the month and year in the correct format.

Using this expression ensures that the x-axis correctly displays the month-year values.

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 company needs to analyze daily sales data from different countries and also measure customer satisfaction of products as reported on a social media website. This suggests that the data is likely coming from different sources, including possibly an API or a web service (social media website).

TheQlik REST Connectoris the appropriate tool for this job. It allows you to connect to RESTful web services and retrieve data directly into Qlik Sense. This is especially useful for integrating data from various online sources, such as social media platforms, which typically expose data via REST APIs. The REST Connector enables the extraction of large datasets from these sources, which is necessary given the requirement to produce 30 reports with an average of 20,000 rows each.

Microsoft SQL Serveris not suitable for fetching data from web services or social media platforms.

Qlik GeoAnalyticsis used for mapping and geographical data visualization, not for connecting to RESTful services.

Mailbox IMAPis for connecting to email servers and is not applicable to the data extraction needs described here.

Thus,Qlik REST Connectoris the correct answer for this scenario.

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 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.

When dealing with a database that is continuously updated with new records, updates, and deletions, an efficient data load strategy is necessary to minimize the load time and keep the Qlik Sense data model up-to-date.

Explanation of Steps:

Load the existing data from the QVD:

This step retrieves the already loaded and processed data from a previous session. It acts as a base to which new or updated records will be added.

Load new and updated data from the database. Concatenate with the table loaded from the QVD:

The next step is to load only the new and updated records from the database. This minimizes the amount of data being loaded and focuses on just the changes.

The new and updated records are then concatenated with the existing data from the QVD, creating a combined dataset that includes all relevant information.

Create a separate table for the deleted rows and use a WHERE NOT EXISTS to remove these records:

A separate table is created to handle deletions. The WHERE NOT EXISTS clause is used to identify and remove records from the combined dataset that have been deleted in the source database.

In this scenario, the data architect needs to replace the SupplierID in the Orders table with the corresponding SupplierName from the Suppliers table, and the desired output should be a single table that includes all the order details along with the SupplierName instead of the SupplierID.

Analyzing the Options:

Option A:

Uses a MAPPING LOAD followed by an APPLYMAP to replace SupplierID with SupplierName in the Orders table. However, the table is dropped afterward, which means it won’t produce the required output.

The MAPPING LOAD approach is generally used to map values but is not necessary in this context as we are combining data from two tables directly.

Option B:

This option attempts to LEFT JOIN the Products table with the Suppliers table, but it does not directly address replacing SupplierID with SupplierName in the Orders table.

Additionally, it does not remove the SupplierID after the join, which is essential for the correct output.

Option C:

This option uses a LEFT JOIN with the DISTINCT keyword on the SupplierID field to avoid duplicates. The SupplierName is correctly joined to the Orders table, replacing the SupplierID.

This approach is the most appropriate because it results in a single table containing all order details with the SupplierName instead of the SupplierID.

Option D:

Similar to Option A, but it also introduces an unnecessary renaming step with MAPPING LOAD. It’s redundant and does not improve the solution over Option C.

Correct Script Choice:

Option Cis the correct script because:

It ensures that SupplierName replaces SupplierID in the Orders table using a LEFT JOIN.

The DISTINCT keyword is applied to the SupplierID field to prevent duplicate rows during the join.

The result is a single table containing the required information with SupplierName in place of SupplierID.

References:

Qlik Sense Join Operations: Using the correct JOIN type and ensuring proper deduplication (with DISTINCT if necessary) is key to merging tables in Qlik Sense.

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 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 whyOption Cis 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 AandOption Bpropose using a mapping load and ApplyMap, which can complicate the model and does not directly address all the business requirements.

Option Dinvolves aliasing fields in a way that could create unnecessary complexity and might not accurately reflect the relationships in the data.

Thus,Option Cis the correct answer as it best meets the requirements while maintaining a clear and functional data model.