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

Aline chartis the most appropriate visualization for comparing two continuous measures (Profit and Revenue) over time. Line charts are designed to show trends and patterns over a continuous scale (such as time), making it ideal for this scenario where we need to observe how both Profit and Revenue vary across a period.

Key Concepts:

Continuous Scale: Line charts are specifically suited for continuous data like time, making them the preferred choice when tracking changes over time for multiple measures.

Dual Measure Comparison: A line chart allows you to plot two measures on the same axis, making it easy to compare their trends over the same period.

Why the Other Options Are Less Suitable:

B. Bullet chart: A bullet chart is used to compare a single measure against a target, not for tracking two measures over time.

C. Bar chart: Bar charts are better suited for comparing categorical data, not continuous measures over time.

D. Scatter plot: Scatter plots are used to compare relationships between two measures but are not suited for continuous time-based comparisons.

References for Qlik Sense Business Analyst:

Line Charts for Time Series Data: Line charts are the recommended visualization for comparing multiple measures over time in Qlik Sense, especially when working with continuous data like Profit and Revenue.

Thus, theline chartis the best choice for this scenario, makingAthe correct answer.

The most effective way to control sheet access based on group assignment is to use theGroup fieldin theSection Access tableand apply it to theshow/hide conditionof the sheets. Each Sales Account ID is already mapped to a Group, so the business analyst can use this Group field to dynamically control which sheets are visible based on the user’s Group assignment. This ensures that each Sales Account only sees their respective Group's sheet.

Key Concepts:

Section Access with Groups: By incorporating the Group field into Section Access, the analyst can restrict sheet visibility based on group membership.

Show/Hide Condition: This feature in Qlik Sense allows certain sheets or objects to be displayed or hidden based on conditions, in this case, the user’s group.

Why the Other Options Are Less Suitable:

A. Sales Account ID as a filter: The Sales Account ID is used for data filtering but is not the appropriate condition for controlling sheet visibility.

B. Group as a filter on the sheet: While Group can be used as a condition, it must be managed through Section Access to ensure proper visibility control.

D. OSUser() function: While OSUser() can capture the user’s login information, using the Group field in Section Access is more efficient for controlling access to specific sheets based on group assignments.

References for Qlik Sense Business Analyst:

Show/Hide Sheets Based on Section Access: Qlik Sense supports the use of Section Access for controlling access to sheets based on user roles and group assignments.

Thus,Cis the most appropriate solution because it uses the Group field within Section Access to control sheet visibility, 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.