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Qlik Sense Business Analyst Certification Exam - 2024 Sample Questions (Q27-Q32):
NEW QUESTION # 27
The CEO has requested a dashboard that tracks the company's performance. The main requirements for the dashboard:
* Minimal number of visualizations or objects
* Display multiple key measures
* Ability to easily navigate to other sheets
Which features would meet the requirements?
- A. A container with multiple visualizations
- B. A bookmark to selected key measures
- C. Multi KPI with master visualizations
- D. Dynamic text and image linked objects
Answer: C
Explanation:
The Multi KPI object is designed to display multiple key measures in a compact and visually appealing format, making it ideal for a dashboard that requires a minimal number of visualizations but must present multiple key metrics. By using master visualizations, the business analyst ensures consistency and reusability across different sheets. This setup also allows for easy navigation through the app, as users can drill down into more detailed sheets when needed.
Key Concepts:
Multi KPI: This visualization allows multiple key performance indicators (KPIs) to be displayed in a single object, reducing the need for multiple charts or visualizations while ensuring that critical metrics are visible.
Master Visualizations: These ensure that KPIs are consistent across the app and can be reused as needed, helping to maintain a clean and efficient dashboard.
Why the Other Options Are Less Suitable:
B . Dynamic text and image linked objects: This could work for navigation, but it wouldn't meet the requirement of displaying multiple key measures in a minimal number of visualizations.
C . Bookmark: A bookmark is used to save a particular state of selections but isn't useful for showing multiple KPIs simultaneously.
D . Container: While containers can hold multiple visualizations, the Multi KPI object is more suited to displaying multiple key measures in a compact space.
References for Qlik Sense Business Analyst:
Multi KPI for Compact Dashboards: Qlik Sense recommends using Multi KPI objects when there is a need to display multiple key metrics in a minimalistic format.
Thus, A is the best choice to meet the CEO's requirements, making it the correct answer.
NEW QUESTION # 28
A business analyst needs to rapidly prototype an application design for a prospective customer. The only dataset provided by the customer contains 30 fields, but has over one billion rows. It will take too long to keep loading in its entirety while the analyst develops the data model.
Which action should the business analyst complete in the Data manager?
- A. Truncate text fields longer than 256 characters to create preview fields
- B. Use the Filter data option to reduce the number of rows
- C. Split the dataset and create a normalized star schema of associated tables
- D. Deselect text columns with unique data values to reduce the memory footprint
Answer: B
Explanation:
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 a Filter data option 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 the Filter data option allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes option C the most effective solution.
NEW QUESTION # 29
A business analyst is developing an app that requires a complex visualization. The visualization is very similar in style and configuration to another visualization in a different app, but the data models are completely different.
Which action should the business analyst take to most efficiently create the new visualization?
- A. Note the properties of the base visualization and create the new visualization from scratch.
- B. Open both apps at the same time. Drag the base visualization between apps, then update the data properties.
- C. Copy and paste the visualization between the apps, and update the data properties in the new app.
- D. Add the base visualization to the master items and use it as a template for the new visualization.
Answer: C
Explanation:
When working with Qlik Sense apps, a business analyst often encounters situations where visualizations may be highly similar between different apps, even if the underlying data models differ. In such cases, efficiency is crucial, and Qlik Sense provides several methods to reuse visualizations across apps. Let's break down the options:
A . Add the base visualization to the master items and use it as a template for the new visualization.
This option suggests adding the base visualization to the master items. While master items are useful for reusing dimensions, measures, and visualizations within the same app, they do not easily transfer across apps. In this case, since the visualization is required in a different app, this approach would not be the most efficient or feasible.
B . Note the properties of the base visualization and create the new visualization from scratch.
This option involves manually noting the properties and then replicating them in the new app. While this would work, it is labor-intensive and increases the likelihood of human error, especially in complex visualizations. It is not an efficient solution for business analysts looking to save time.
C . Copy and paste the visualization between the apps, and update the data properties in the new app.
This is the most efficient solution. Qlik Sense allows for the copying and pasting of visualizations between different apps, and you can then adjust the properties to fit the new data model. This option enables the business analyst to leverage existing visual work without having to recreate it from scratch. Updating the data properties, such as dimensions and measures, ensures that the visualization functions correctly with the new data model.
D . Open both apps at the same time. Drag the base visualization between apps, then update the data properties.
While this seems like a practical option, Qlik Sense does not allow users to drag and drop visualizations directly between different apps. As a result, this method is not possible.
Key Qlik Sense Business Analyst References:
Copying and pasting visualizations is a common practice in Qlik Sense when working between different apps. The ability to quickly replicate and adapt visualizations across apps helps streamline the development process.
Adjusting data properties such as dimensions and measures ensures that visualizations adapt to different data models without the need for full recreation.
Efficiency and error reduction are critical in app development, and copy-paste functionalities are specifically designed to reduce manual work in such scenarios.
In conclusion, the correct and most efficient action for the business analyst to take is C, copy and paste the visualization, and then update the relevant data properties.
NEW QUESTION # 30
Refer to the exhibit.
The users of a Qlik Sense app report slow performance. The app contains approximately 10 million rows of dat a. The business analyst notices the following KPI master measure definition:
Left{ Trim( TransactionName), 1 ) * Right ( TransactionName, 5) Which steps should the business analyst complete to improve app performance?
- A. Change the master measure definition as follows:
subfield( TransactionName, '',!)* subfieldl TransactionName, ' ', 3) - B. In the Data manager, use the Replace function to remove the middle part of the field TransactionName
- C. Ask the developer of the underlying database to change the structure of the field TransactionName.
- D. * 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.
Answer: D
Explanation:
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.
NEW QUESTION # 31
The human resources department needs to see a distribution of salaries broken down by department with standard deviation indicators.
Which visualization should the developer use?
- A. Distribution plot
- B. Histogram
- C. Box plot
- D. Scatter plot
Answer: C
Explanation:
A box plot is the best visualization for displaying the distribution of salaries broken down by department with standard deviation indicators. Box plots show the spread of data, including key measures like quartiles, median, and outliers, which are useful for analyzing salary distributions. They also naturally incorporate standard deviation indicators through the spread of data.
Key Concepts:
Box Plot: This type of chart is designed for analyzing the distribution of data across different categories (in this case, departments). It shows the spread and variability of data, which can include standard deviations.
Why the Other Options Are Less Suitable:
A . Distribution plot: While a distribution plot can show spread, it's not as effective for showing standard deviation and is less suited for categorical breakdowns.
C . Histogram: A histogram shows the distribution of a single variable, but it doesn't provide the same detailed breakdown as a box plot.
D . Scatter plot: Scatter plots are used for showing relationships between two variables and are not suitable for showing standard deviation across departments.
References for Qlik Sense Business Analyst:
Box Plot for Distribution Analysis: Box plots are ideal for visualizing data distribution and variability across categories, making them the preferred choice for analyzing salary distribution by department.
Thus, the box plot is the best choice for visualizing salary distribution with standard deviation indicators, making B the verified answer.
NEW QUESTION # 32
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