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

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.

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.

In scenarios where a customer needs to standardize colors across multiple apps and be able to update them quickly, usingvariablesin combination with aninclude statementis the most flexible and maintainable approach.

A. Design all base objects as master visualizations and link each object in each app to the relevant master visualization.While master visualizations help with consistency within a single app, they don't offer an easy way to update all apps globally. You would need to manually update the colors in every master visualization in each app, which is not efficient for large-scale management.

B. Develop the first app with every variation of object and visualization and duplicate this app.Duplicating apps will create maintenance challenges. Each app would need to be updated individually if colors or other settings change, which is not scalable for over 100 apps.

C. Create all color expressions as variables in a text file, load it in each app with an include statement, and use these variables in the color property of all objects.This is the most efficient solution. By storing color definitions in a text file and loading them with aninclude statement, the business analyst can update the colors in one place, and these updates will be reflected across all apps that use the file. This method ensures easy maintenance and flexibility.

D. Store color definitions within a .qvd file and load it as a data island.While using a .qvd file is possible, it’s not as straightforward as using variables and an include statement. Data islands are typically used for selection purposes, and this method would introduce unnecessary complexity in managing colors.

Key Qlik Sense Business Analyst References:

Variablesare widely used in Qlik Sense for managing repeated expressions or values like colors. They can be defined once and reused throughout the app.

Include statementsallow external files (like text files containing variables) to be loaded into apps, ensuring that updates made to the text file are automatically reflected in all apps that use it. This creates a flexible and scalable solution for managing standardization across multiple apps.

Thus, the best way to maintain a standardized color palette across all apps is tocreate all color expressions as variables in a text file and load them into each app using an include statement.