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

When working with large datasets, such as the one containing over a billion rows in this scenario, loading the entire dataset can be time-consuming, especially during the development phase. Qlik Sense provides aFilter dataoption in the Data Manager, which allows business analysts to work with a subset of the data during development. This is particularly useful for rapidly prototyping the application design.

Key Concepts:

Filter Data Option: This feature in Qlik Sense allows the analyst to load a smaller sample of the dataset for analysis and development purposes. By filtering out unnecessary rows, the business analyst can quickly build and prototype the application without waiting for the full dataset to load. Once the design is finalized, the full dataset can be reloaded.

Prototyping with Reduced Data: It’s often more efficient to work with a smaller dataset during the design phase. This allows for faster iterations and design cycles, especially when working with large datasets.

Why the Other Options Are Less Suitable:

A. Split the dataset and create a normalized star schema of associated tables: This would involve complex data modeling that is not necessarily related to the immediate need of reducing the size of the dataset for prototyping. While star schemas can optimize data models, it’s not the quickest way to reduce the number of rows for initial testing.

B. Deselect text columns with unique data values to reduce the memory footprint: This may reduce the memory usage but won't necessarily address the issue of reducing the number of rows. Also, the text columns might be important for the analysis and should not be removed without careful consideration.

D. Truncate text fields longer than 256 characters to create preview fields: Truncating text fields will not significantly reduce the dataset size or the load time. It may also result in losing critical information, which is not ideal for prototyping.

References for Qlik Sense Business Analyst:

Rapid Prototyping: Qlik Sense encourages rapid development of applications by allowing business analysts to work with subsets of the data. The Filter Data option is an important tool for managing large datasets efficiently.

Data Manager Tools: The Data Manager in Qlik Sense provides several tools for reducing the dataset size, and filtering is one of the key options for improving performance during development.

Using theFilter dataoption allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes optionCthe most effective solution.

Ascatter plotis the best choice for visualizing outliers, clustering, and products that are above the median sales volume. Scatter plots are excellent for showing relationships between two variables (like sales volume and margin %) while providing the ability to highlight outliers. By usingK-Means clusteringto color the products, the business analyst can visually group similar products. Additionally, adding amedian sales volume reference linemakes it easy to identify which products are above or below the median.

Key Concepts:

Scatter Plot: This visualization is ideal for detecting outliers and understanding the distribution and clustering of data points.

K-Means Clustering: This technique groups similar data points (products) based on their values, which helps to identify patterns visually.

Median Reference Line: Adding a reference line for median sales volume ensures that the analyst can easily see which products are performing above or below the median.

Why the Other Options Are Less Suitable:

A. Pivot table: While a pivot table could show aggregated data, it is not as effective for visualizing outliers or clustering as a scatter plot.

B. Treemap: A treemap can show hierarchical relationships but is not as effective for identifying clusters or outliers.

C. Combo chart: A combo chart is not as well-suited for visualizing clustering and outliers as a scatter plot.

References for Qlik Sense Business Analyst:

Outliers and Clustering Visualization: Qlik Sense recommends scatter plots for visualizing relationships between two measures and for detecting outliers and clustering in the data.

Thus, creating ascatter plotwith K-Means clustering and a median sales volume reference line is the best approach, makingDthe verified answer.

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 adashboard of KPIsandSection Accessis ideal. A dashboard of KPIs provides high-level insights, andSection Accessensures 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,Ais the best solution because it combines high-level KPIs with robust data access controls using Section Access, making it the correct answer.