Free Qlik QSDA2024 Practice Exam with Questions & Answers | Set: 2

In the provided scenario, the goal is to export tables from a Qlik Sense model based on rules specified in an external text file. The structure of the text file indicates which table to export, the filename to use, and how many copies to create.

Given this structure, the data architect needs to:

Loop through each row in the text file to process each table.

Use an IF statement to check whether the specified table exists in the model (though it's mentioned they are verified to exist, this step may involve conditional logic to ensure the rules are correctly followed).

Use another IF statement to handle the creation of multiple copies, ensuring each file is named incrementally (e.g., Clients1.qvd, Clients2.qvd, etc.).

Key Script Strategies:

Loop: A loop is necessary to iterate through each row of the text file to process the tables specified for export.

IF Statements: The first IF statement checks conditions such as whether the table should be exported (based on additional logic if needed). The second IF statement handles the creation of multiple copies by incrementing the filename.

This approach covers all the necessary logic with the minimum set of scripting strategies, ensuring that each table is exported according to the rules defined.

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.

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.

In the scenario where the startup company is preparing for an IPO, there is an increased need for meticulous record-keeping, including the recording of variable values used in Qlik Sense applications. The TRACE statement is the most suitable option for logging variable values during script execution.

TRACE: This statement writes custom messages, including variable values, to the script execution log. By using TRACE, you can ensure that every reload log contains the names and current values of all relevant variables, providing the necessary transparency and traceability.

For example, the script could include:

TRACE $(VariableName);

This command will output the variable's value in the script log, ensuring it is recorded for audit purposes.

The scenario describes a need to track social media data over the past 10 years, capturing all changes (inserts, updates, deletes) while reloading the data four times a day.

To manage this:

ModificationTime: This field is essential for tracking changes over time. It indicates when a record was last modified, allowing the script to determine whether it needs to insert, update, or delete records.

Primary Key Field: A primary key is crucial for uniquely identifying records. It enables the script to match records in the source with those already loaded, facilitating updates and deletions.

Insert and Update Records: The script should handle both inserting new records and updating existing ones based on the ModificationTime.

Remove Records: If records are deleted in the source, they should also be removed in the Qlik Sense data model to maintain consistency.

This approach ensures that all changes in the social media data are accurately captured and reflected in the Qlik Sense application.