Free Qlik QSDA2024 Practice Exam with Questions & Answers

In this scenario, the Section Access security table controls user access to data within the Qlik Sense app. The user in question, ABC\PPP, has a specific entry in the security table that determines their access rights to the LEVEL field.

Understanding Section Access:

Section Accessis used to enforce security by restricting access to certain data based on the user's credentials.

In the security table provided, the USER role for ABC\PPP is set to have access to all data (* in the LINK field), but the OMIT field is set to LEVEL. The OMIT field in Section Access specifies fields that should be omitted from the user's view.

Outcome:

Since the OMIT field for user ABC\PPP is set to LEVEL, this user will not have access to the LEVEL field in the Qlik Sense application.

Option D: The table is displayed without the LEVEL columnis the correct outcome.

Explanation: When user ABC\PPP opens the app, the LEVEL field is omitted from their view. Any table or visualization that uses the LEVEL field will have that field excluded from display. The rest of the data and columns in the table will be visible, but the LEVEL column will not be shown.

References:

Qlik Sense Security and Section Access Documentation: The OMIT functionality in Section Access is specifically designed to remove fields from the user's access, ensuring that sensitive or unnecessary data is not exposed.

When implementing Section Access in Qlik Sense, it is crucial that all accounts that need to access the data—including the service account that performs the scheduled reload—are included in the Section Access table. If the service account is not included, Qlik Sense will not be able to access any data, leading to a failure in the reload process.

Here’s a breakdown of why the other options are less likely:

A. The ACCESS column in the Section Access table has been added in lowercase:This would generally result in a syntax error, but it would not allow the script to execute successfully without causing an immediate failure, unrelated to Section Access.

C. A user name listed in the Section Access table is spelled incorrectly:While this could lead to some users not having the correct access, it would not cause the entire reload to fail. The issue here is broader, affecting the entire application load process.

D. The data architect does not have rights to reload the app:If the architect did not have rights, the script would not run successfully even without Section Access.

The correct issue in this scenario is thatthe service account running the task is not included in the Section Access table. This is a common cause of load failures after adding Section Access. To resolve this, ensure that the service account is added with sufficient privileges in the Section Access table

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 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.

The expression sum([OrderNetAmount]) sums the values in the OrderNetAmount field across the dataset. Given that the dataset includes an inline table that is joined with another, the expression calculates the sum of OrderNetAmount for all selected rows. In this scenario, all values in LineNo are selected, which doesn't affect the summation of OrderNetAmount because LineNo isn't directly used in the sum calculation.

Step-by-step Calculation:

The Orders table contains the OrderNetAmount for each order. The values provided are 90, 500, 100, and 120.

Adding these values together:90+500+100+120=81090 + 500 + 100 + 120 = 81090+500+100+120=810

However, after the Left Join operation with the OrderDetails table, some of these rows might be duplicated if the join results in multiple matches. But since the field being summed, OrderNetAmount, is from the original Orders table and not affected by the details in OrderDetails, the sum still remains consistent with the original values in the Orders table.

Thus, the sum of OrderNetAmount is 149014901490, based on the combined effects of the original data structure and the join operation.

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.

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 script provided, the alt() function is used to handle various date formats. The alt() function in Qlik Sense evaluates a list of expressions and returns the first valid expression. If none of the expressions are valid, it returns the last argument provided (in this case, '31/12/2022').

Step-by-step breakdown:

The alt() function checks the Date field for three different formats:

YYYYMMDD

YYYY/MM/DD

DD/MM/YYYY

If none of these formats match the value in the Date field, the default date '31/12/2022' is assigned.

Values in the Date field:

20210131: Matches the first format YYYYMMDD.

2020/01/31: Matches the second format YYYY/MM/DD.

31/01/2019: Matches the third format DD/MM/YYYY.

9999: Does not match any of the formats, so the alt() function returns the default value '31/12/2022'.

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.

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.