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

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 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.

During the transition from QlikView to Qlik Sense, the analytics team has the opportunity to improve the overall reporting. To ensure a smooth migration while optimizing the new environment, the data architect needs to consider several key factors.

Option Cis the best choice because it encompasses the essential aspects of a migration project:

QlikView Archival:

Archiving QlikView applications is crucial to ensure that historical data and applications are preserved and can be referenced if needed in the future. This step is important to maintain continuity and provide a fallback option if required during the transition.

Source Data Architecture:

Understanding the existing source data architecture is critical to ensure that the new Qlik Sense applications can seamlessly connect to the data sources. This also helps in identifying opportunities to optimize or re-architect the data pipelines for better performance in Qlik Sense.

Load Script:

The load script from QlikView might need to be revised or optimized for Qlik Sense. It's important to ensure that the script is compatible and takes advantage of Qlik Sense's capabilities, such as improved data handling, better inline transformations, and enhanced scripting functions.

Data Model:

Reviewing and possibly redesigning the data model is essential during the migration. Qlik Sense's associative engine allows for more flexibility, and this is an opportunity to improve the data model for better performance, scalability, and user experience.

Business Use Case:

Understanding the business use case is vital to ensure that the new Qlik Sense applications meet the business requirements effectively. This includes making sure that the new reports and dashboards are aligned with the business goals and provide the necessary insights.

References:

Qlik Migration Guide: When migrating from QlikView to Qlik Sense, it's important to consider not just the technical aspects but also the business implications and opportunities for improvement.

Qlik Documentation on Data Modeling and Load Script Optimization: These resources provide best practices on how to optimize load scripts and data models during migration to ensure smooth operation and better performance in Qlik Sense.

When developing an application that summarizes Qlik Sense usage by company employees and also gathers usage metrics for other systems, the data architect needs to ensure they have the correct access to sensitive data. The following roles are crucial:

IT Security Director:Responsible for the security of IT systems and data. They would ensure that the data architect has the appropriate permissions to access usage metrics and other system data securely.

Human Resources Director:They manage employee-related data, including employment records that might be necessary for matching employee IDs with usage metrics. This access is crucial for correlating usage data with specific employees.

Qlik Sense Administrator:This individual has administrative rights over the Qlik Sense environment and can grant access to usage data within Qlik Sense, ensuring that the architect has the necessary data to analyze.

Given the need to securely and correctly handle sensitive data, including employee usage metrics across multiple systems,Option Aincludes all the appropriate contacts for access and permissions.

In the provided data model, both the CurrentOffice and Transaction tables contain the fields SalesID and Office. This leads to the creation of a synthetic key in Qlik Sense because of the two common fields between the two tables. A synthetic key is created automatically by Qlik Sense when two or more tables have two or more fields in common. While synthetic keys can be useful in some scenarios, they often lead to unwanted and unexpected results, so it’s generally advisable to resolve them.

In this case, the goal is to have both the current office of the salesperson and the office where the transaction occurred visible in the data model. Here’s how each option compares:

Option A: Comment out the Office in the Transaction table:This would remove the Office field from the Transaction table, which would prevent you from seeing which office the salesperson was in when the transaction occurred. This option does not meet the requirement.

Option B: Inner Join the Transaction table to the CurrentOffice table:Performing an inner join would merge the two tables based on the common SalesID and Office fields. However, this might result in a loss of data if there are sales records in the Transaction table that don’t have a corresponding record in the CurrentOffice table or vice versa. This approach might also lead to unexpected results in your analysis.

Option C: Alias Office to CurrentOffice In the CurrentOffice table:By renaming the Office field in the CurrentOffice table to CurrentOffice, you prevent the synthetic key from being created. This allows you to differentiate between the salesperson’s current office and the office where the transaction occurred. This approach maintains the integrity of your data and allows for clear analysis.

Option D: Force concatenation between the tables:Forcing concatenation would combine the rows of both tables into a single table. This would not solve the issue of distinguishing between the current office and the office at the time of the transaction, and it could lead to incorrect data associations.

Given these considerations, the best approach to resolve the synthetic key while fulfilling the requirement of having both the current office and the office at the time of the transaction visible is toAlias Office to CurrentOffice in the CurrentOffice table. This ensures that the data model will accurately represent both pieces of information without causing synthetic key issues.

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.

In Qlik Sense, the TRACE statement is used to print custom messages to the script execution log. To output the value of a variable, particularly one that is dynamically assigned, the correct syntax must be used to ensure that the variable's value is evaluated and displayed correctly.

The variable vMaxDate is defined with the LET statement, which means it is evaluated immediately, and its value is stored.

When using the TRACE statement, to output the value of vMaxDate, you need to ensure the variable's value is expanded before being printed. This is done using the $() expansion syntax.

The correct syntax is TRACE #### $(vMaxDate) ####; which evaluates the variable vMaxDate and inserts its value into the log output.

Key Qlik Sense Data Architect References:

Variable Expansion:In Qlik Sense scripting, $(variable_name) is used to expand and insert the value of the variable into expressions or statements. This is crucial when you want to output or use the value stored in a variable.

TRACE Statement:The TRACE command is used to write messages to the script log. It is commonly used for debugging purposes to track the flow of script execution or to verify the values of variables during script execution.

When retrieving data from a REST API, particularly when the dataset is large or the data is segmented across multiple pages (which is common in REST APIs), the REST Connector in Qlik Sense needs to be configured to handle pagination.

Pagination is the process of dividing the data retrieved from the API into pages that can be loaded sequentially or as required. Qlik Sense's REST Connector supports pagination by allowing the dataarchitect to set parameters that will sequentially retrieve each page of data, ensuring that the complete dataset is retrieved.

Key Steps:

REST Connector Setup: Configure the REST connector in Qlik Sense and specify the necessary API endpoint.

Pagination Mechanism: Use the built-in pagination mechanism to define how the connector should retrieve the subsequent pages (e.g., by using query parameters like page or offset).

In this scenario, the data architect needs to validate the script and specifically ensure that the vMaxDate variable is being correctly utilized in the LOAD statement. The goal is to stop the script execution at a point where the variable's value can be previewed.

Understanding the Options:

Option Aplaces the breakpoint just after the assignment of the variable vMaxDate in the Where clause but before any data is loaded.

Option B, C, and Drepresent placements of the breakpoint after the LOAD statement begins processing the Resident table, which means that the variable vMaxDate would have already been utilized.

Correct Breakpoint Placement:

Option Ais the correct choice because placing the breakpoint at this point allows you to preview the value of vMaxDate right before it is used in the Where clause. This placement ensures that the script execution halts before loading the data, allowing you to validate whether vMaxDate is correctly defined and whether it correctly filters the data based on the [Date] field.

If the breakpoint were placed after the LOAD statement (as in Options B, C, or D), the script would have already attempted to load the data, making it too late to inspect the variable's value before it's used.

References:

Qlik Sense Debugging Best Practices: When debugging, it is crucial to set breakpoints before the execution of a critical operation where the values of variables or fields are used to ensure that they hold the expected data.

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.

In this scenario, users of a published app report incomplete visualizations, but the data architect is unable to replicate the error. This issue affects only one team, suggesting that the problem is related to how data is being restricted or filtered for that specific team.

Section Access: This is a security feature in Qlik Sense that controls user access to specific data within an app. If Section Access is misconfigured, it can restrict access to more records than intended, leading to incomplete visualizations for certain users or teams.

Restricting Too Many Records: If the Section Access is too restrictive, it might limit the data available to the affected users, causing the visualizations to display incomplete information. This could explain why the data architect, who likely has full access, cannot replicate the issue.

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.