QSDA2024 Qlik Sense Data Architect Certification Exam – 2024 Free Practice Exam Questions (2025 Updated)

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 the scenario, the data architect needs to load data from two different databases, and additional data is located in a folder containing QVDs, text files, and Excel files.

Minimum Number of Data Connections Required:

Database Connections:

Each database requires a separate data connection. Therefore, two data connections are needed for the two databases.

Folder Connection:

A single folder data connection can be used to access all the QVDs, text files, and Excel files in the specified folder. Qlik Sense allows you to create a folder connection that can access multiple file types within that folder.

Total Connections:

Two Database Connections: One for each database.

One Folder Connection: To access the QVDs, text files, and Excel files.

Therefore, the minimum number of data connections required istwo.

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.

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.

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 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 this scenario, the data architect wants to transform the input dataset, which is in a key-value pair structure, into a table where each attribute becomes a column with its corresponding value under the relevant key.

Understanding the Requirement:

Theinputdata consists of three fields: Key, Attribute, and Value.

The desiredoutputstructure has the Key as a primary identifier, and the Attributes (like Color, Diameter, Height, etc.) are spread across the columns, with corresponding values filled in each row.

Best Method to Achieve this Transformation:

The appropriate method to convert key-value pairs into a structured table where each unique attribute becomes a separate column is theGeneric Loadfunction in Qlik Sense.

Why Generic?

Generic Loadis specifically designed for situations where data is stored in a key-value format (like the one provided) and needs to be converted into a more traditional tabular format, with attributes as columns.

It creates a separate table for each combination of Key and Attribute, effectively "pivoting" the attribute values into columns in the output table.

How it Works:

When applying a GENERIC LOAD to the input dataset, Qlik Sense will generate multiple tables, one for each Attribute. However, in the final data model, Qlik Sense automatically joins these tables by the Key field, effectively producing the desired output structure.

References:

Qlik Sense Documentation on Generic Load: The documentation outlines how to use the Generic Load to handle key-value pairs and pivot them into a more traditional table format.

In this scenario, the data architect needs to verify the existence of files before attempting to load them and then proceed accordingly. The correct approach involves using the FileExists() function to check for the presence of each file. If the file exists, the script should execute the file loading routine. The FOR EACH loop will handle multiple files, and the IF statement will control the conditional loading.

FileExists(): This function checks whether a specific file exists at the specified path. If the file exists, it returns TRUE, allowing the script to proceed with loading the file.

FOR EACH: This loop iterates over a list of items (in this case, file paths) and executes the enclosed code for each item.

IF: This statement checks the condition returned by FileExists(). If TRUE, it executes the code block for loading the file; otherwise, it skips to the next iteration.

This combination ensures that the script loads data only if the files are present, optimizing the data loading process and preventing unnecessary errors.

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.

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.

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.

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 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 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.