To fulfill the CFO's request for an app that allows each regional finance team to access only their data while navigating from a high-level sheet to detail sheets, the combination of a dashboard of KPIs and Section Access is ideal. A dashboard of KPIs provides high-level insights, and Section Access ensures that users from different regions can only see the data relevant to their region. Section Access allows for controlled access to data, ensuring data security and segregation.

Key Concepts:

Dashboard of KPIs: A dashboard displaying key performance indicators (KPIs) gives a high-level overview of financial data, allowing users to quickly assess critical metrics.

Section Access: This Qlik Sense feature controls data access based on user roles, ensuring that users only have access to the data relevant to their region.

Why the Other Options Are Less Suitable:

B . Pivot table: A pivot table is useful for detailed analysis but not suitable for designing a navigation structure or controlling access to data by region.

C . Multi KPI with set analysis: While set analysis can filter data, it doesn't control access at the regional level as effectively as Section Access.

D . Dashboard with regional bookmarks: Bookmarks are user-specific and do not offer security or access control, which is required in this scenario.

References for Qlik Sense Business Analyst:

Section Access for Regional Data Control: Qlik Sense recommends Section Access for managing data access when different users need to see only specific subsets of data.

Thus, A is the best solution because it combines high-level KPIs with robust data access controls using Section Access, 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 why Option B is 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 the Split function in 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 the subfield() function to 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 the Data 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.

The Replace function would 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:

The Data Manager provides powerful tools for transforming and optimizing data before it is used in visualizations. Pre-processing the data using functions like Split significantly 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 fields allow analysts to create new expressions based on transformed data, ensuring that the app remains efficient while meeting analytical needs.

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

In Qlik Sense, the Insights Advisor Chat is the feature that allows users to interact with the app through natural language questions. This tool leverages Qlik's advanced AI and machine learning capabilities to interpret natural language queries and generate relevant insights, visualizations, or suggestions for analysis.

A . Advanced Authoring Advanced Authoring is a set of tools in Qlik Sense designed for creating detailed visualizations and reports, but it does not include natural language interaction capabilities. It is focused more on customization and precise design rather than conversational querying.

B . Story and Bookmarks Storytelling and bookmarks in Qlik Sense are tools for narrative data presentations and saving specific states of analysis. They do not provide the ability to ask natural language questions or automatically generate insights.

C . Insights Advisor Chat Insights Advisor Chat is the correct answer. This feature allows users to interact with their data by typing natural language questions, which the system interprets to generate appropriate responses, including charts, KPIs, and other insights. It is designed to assist non-technical users by making data exploration more intuitive and accessible through natural language.

D . Associative Engine The Associative Engine is the underlying technology that allows Qlik Sense to handle large datasets and perform associative searches across them. While it is powerful for data exploration, it does not provide a direct interface for natural language querying like Insights Advisor Chat does.

Key Qlik Sense Business Analyst References:

Insights Advisor Chat is a feature in Qlik Sense that empowers users to ask questions in natural language and get meaningful responses without needing to be data experts.

It is part of Qlik Sense's broader set of augmented intelligence tools that enhance the user experience by providing guided insights and helping users discover relationships in data through natural language queries.

This feature simplifies data exploration for business users who might not be familiar with complex data querying techniques.

Thus, the feature that allows users to use natural language questions in Qlik Sense is Insights Advisor Chat.

The business analyst needs to optimize the app for performance and ensure that users only see data from the past two years, without cardholder information, and that the data is updated daily. By deselecting the Cardholder and time fields in the Data Manager, the analyst ensures that sensitive information is not loaded. Applying a filter to extract data based on transaction year ensures that only relevant data (the last two years) is included in the app, improving performance. Lastly, requesting a daily reload task from the system administrator ensures that the app stays up to date.

Key Concepts:

Deselecting Fields: This removes unnecessary fields, such as Cardholder information, from the data model, which improves performance and ensures privacy.

Filtering Data: Applying a filter to limit data to the last two years reduces the dataset size and improves app responsiveness.

Daily Reload Task: Requesting a daily reload ensures that the app's data stays current, meeting the requirement for daily updates.

Why the Other Options Are Less Suitable:

A . Delete Cardholder and time fields, use bookmark: A bookmark is not an efficient solution for filtering by transaction year.

B . Set analysis and API reload: Set analysis works within the app but does not optimize the data load itself. Using an API for reload tasks is unnecessarily complex.

C . Use filter pane and auto-calendar: While auto-calendar fields can be useful, they don't optimize the data loading process for performance.

References for Qlik Sense Business Analyst:

Efficient Data Loading: Qlik Sense recommends filtering data at the load stage to improve performance, especially when dealing with large datasets.

Thus, D is the correct solution, making it the verified answer.

A line chart is 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, the line chart is the best choice for this scenario, making A the correct answer.