QSBA2024 Exam Dumps : Qlik Sense Business Analyst Certification Exam-2024

To inform a company director when a threshold is breached,Alertingis the most appropriate feature. Qlik Sense allows users to set up automated alerts based on specific conditions or thresholds. Thesealerts can be configured to notify users when certain criteria are met, making it ideal for keeping the director informed without requiring them to constantly check the application.

Key Concepts:

Alerting: This feature allows users to set conditions (such as when a sales or performance threshold is breached) and automatically send notifications to the relevant individuals.

Automated Notifications: Alerting ensures that the director is notified promptly and automatically when a critical threshold is reached.

Why the Other Options Are Less Suitable:

A. Weekly PDF report: A weekly report would not provide real-time notifications, which are necessary when thresholds are breached.

B. Storytelling: Storytelling is used for presentations, not for monitoring thresholds.

D. Bookmark: A bookmark only saves a particular selection or view, and cannot send notifications when a condition is met.

References for Qlik Sense Business Analyst:

Alerting in Qlik Sense: Qlik Sense’s alerting functionality is designed to notify users when specific conditions are triggered, making it ideal for scenarios where timely notifications are critical.

Thus, usingAlertingis the best solution to notify the director when a threshold is breached, makingCthe verified answer.

When loading data from an Excel file with more than 100 columns, where only columns with the suffix _c are relevant, the most efficient approach is to use theData Manager. The Data Manager provides a preview of the table being loaded, allowing the business analyst todeselect columnsthat do not have the _c suffix. This is a quick and straightforward method that avoids manual editing of the Excel file and allows the analyst to focus on the necessary columns.

Key Concepts:

Data Manager Preview: The Data Manager allows you to inspect and modify which columns will be loaded into the data model. The preview panel makes it easy to deselect columns that are not needed.

Efficient Data Loading: By using the Data Manager, the business analyst can avoid loading unnecessary columns, ensuring a cleaner and more manageable data model.

Why the Other Options Are Less Suitable:

A. Load all columns: This would load unnecessary columns, leading to a bloated data model with duplicates and irrelevant data.

B. Utilize filter functionality: While filtering could work, deselecting fields directly in the preview is more efficient and straightforward.

C. Edit the Excel file: Manually editing the Excel file is unnecessary and could lead to errors, especially when Qlik Sense provides tools to handle this within the platform.

References for Qlik Sense Business Analyst:

Data Manager for Field Selection: Qlik Sense recommends using the Data Manager to inspect and selectively load data fields, which is particularly useful when dealing with large datasets.

Thus,Dis the best solution because it allows for selective loading of relevant columns, making it the correct answer.

The app is experiencing performance issues due to inefficient calculations in a master measure that processes the field TransactionName, which has a complex structure (e.g., "1_ABCDEFGHI_23454"). Let’s analyze the available options and whyOption Bis the best solution.

A. Ask the developer of the underlying database to change the structure of the field TransactionName.While modifying the data structure in the underlying database might improve performance, this approach is not ideal. It’s a time-consuming process that might not be feasible, especially when working with large datasets that have already been integrated into the Qlik Sense app. The performance improvement should focus on optimizing the Qlik app itself.

B. In the Data manager, use the Split function to split the field values with the underscore character as the separator. In the Data manager, use the Add calculated field function to multiply the 1st and the 3rd column of the split field. Reload the data.This is the most efficient approach. By using theSplit functionin the Data Manager to break down the TransactionName field based on the underscore separator, the data becomes more accessible for calculations. You can then create a calculated field that multiplies the first and third components of the split data (corresponding to the 1st part and the numeric identifier at the end). This reduces the need for complex string manipulation functions (e.g., Left, Right, Trim) within the master measure, which can be resource-intensive when applied to large datasets.

C. Change the master measure definition as follows: subfield( TransactionName, '',!) * subfield( TransactionName, '', 3)This option suggests using thesubfield() functionto split the string within the master measure itself. While this approach is valid, it doesn’t provide as significant a performance improvement compared to pre-processing the data in theData Manager. Calculating fields directly within the visualizations is more computationally expensive compared to handling it during the data load phase.

D. In the Data manager, use the Replace function to remove the middle part of the field TransactionName.TheReplace functionwould remove the middle section of the transaction name, but it doesn’t address the need to split the field for efficient multiplication. It would also result in a loss of important data that may be required for other analyses.

Key Qlik Sense Business Analyst References:

TheData Managerprovides powerful tools for transforming and optimizing data before it is used in visualizations. Pre-processing the data using functions likeSplitsignificantly reduces the load on front-end visualizations.

Splitting fields during the data load rather than in the master measures improves performance, especially in large datasets where string manipulation functions in visualizations can degrade performance.

Calculated fieldsallow analysts to create new expressions based on transformed data, ensuring that the app remains efficient while meeting analytical needs.

Thus, the correct solution is touse the Split function to separate the field values and then use a calculated field to multiply the required components, which enhances app performance.