Qlik QSBA2024 Exam With Confidence Using Practice Dumps

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 the provided exhibit, the movie analyst filters by theLength Rangefield and notices a hyphen (“-”), which typically indicatesmissing datain Qlik Sense. When the analyst selects this hyphen, it filters out the rows where no data exists for the Length Range field.

Let’s review the options:

A. Six movies in the source data contain illegal characters for the Length Range field.This statement is incorrect. The hyphen (“-”) is not an indicator of illegal characters. In Qlik Sense, it usually representsnull values or missing data, not improperly formatted data.

B. Movies at the start of the 20th century often varied in length.This answer is not relevant to the specific observation of the hyphen in the Length Range field. The hyphen does not provide insights into the variation of movie lengths; it indicates missing data.

C. All movies from the 1920s or 1930s contain no data for Length Range.While the screenshot shows some movies from different decades, we cannot definitivelyconclude that all movies from a particular era are missing the Length Range data. The hyphen indicates that some movies across various years are missing data, but this does not apply to all movies from the 1920s or 1930s.

D. The source data for six movies is missing a Length Range.This is the correct interpretation. The hyphen in theLength Rangecolumn indicates that these six movies lack data for this field. Selecting the hyphen allows the analyst to identify movies with missing values in the Length Range field.

Key Qlik Sense Business Analyst References:

Hyphen (“-”) in Qlik Sensetypically represents null or missing values in a field.

Filter panesin Qlik Sense allow users to quickly see where data may be incomplete or missing, and selecting these null values provides immediate insight into which records are affected.

Thus, the correct determination is thatthe source data for six movies is missing a Length Range.