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NEW QUESTION # 35
A customer needs to distribute sales data to a variety of teams. The internal analyst team requires a global view of dat a. The sales team requires mobile device access.
Which solution will meet the needs of both teams?
Answer: D
Explanation:
To meet the needs of both the internal analyst team and the sales team, the best solution is to create two separate apps: one designed specifically for mobile use and another for internal use. Mobile devices require different UI considerations, such as simpler, touch-optimized layouts, while the internal team can benefit from a more detailed app optimized for desktop use. Designing separate apps ensures that both teams have a tailored experience that suits their specific devices and use cases.
Key Concepts:
Mobile Optimization: Mobile devices require apps that are streamlined and optimized for smaller screens, while internal users on desktop computers can handle more complex layouts and detailed reports.
Separate Apps: Creating separate apps ensures that each team gets the best user experience tailored to their needs.
Why the Other Options Are Less Suitable:
A . One app with a specific extension for mobile users: While extensions can provide some mobile functionality, they don't offer the flexibility and optimization needed for a fully mobile-friendly experience.
C . A mashup with various objects: A mashup may provide flexibility, but it could be overly complex for this requirement and wouldn't necessarily offer an optimal mobile experience.
D . One app with various objects: This could complicate the user experience for both teams, as mobile users may struggle with objects that are not optimized for their devices.
References for Qlik Sense Business Analyst:
Mobile vs. Desktop App Design: Qlik Sense recommends optimizing apps for specific devices to ensure the best user experience for both desktop and mobile users.
Thus, B is the correct answer because it provides the best solution for both the mobile sales team and the internal analyst team, making it the verified answer.
NEW QUESTION # 36
A customer needs to demonstrate the value of sales for each month of the year with a rolling 3-month summary. Which visualization should the business analyst recommend to meet the customer's needs?
Answer: C
Explanation:
A combo chart is the most suitable visualization to show the value of sales for each month along with a rolling 3-month summary. The combo chart allows you to combine different types of visualizations, such as bars for monthly sales values and a line for the rolling 3-month summary. This provides a clear comparison and tracking of sales trends over time.
Key Concepts:
Rolling Summary: In this case, a 3-month rolling summary can be shown as a line measure in the combo chart, while the sales values for each month can be shown as bars.
Combo Chart: This visualization is ideal for comparing multiple measures on the same axis, such as individual sales values and aggregated rolling summaries.
Why the Other Options Are Less Suitable:
A . Scatter plot: A scatter plot is used to display the relationship between two variables, not to show time-based trends or rolling summaries.
B . Mekko chart: Mekko charts are used for categorical data and comparisons across categories, not for time-based analysis.
D . Pie chart: Pie charts are best suited for showing parts of a whole and are not appropriate for visualizing time-based data or rolling summaries.
References for Qlik Sense Business Analyst:
Combo Charts for Time Series Data: Combo charts are highly recommended when there is a need to compare different types of measures (like individual sales vs. rolling averages) over time in Qlik Sense.
Thus, a combo chart provides the most effective solution for showing both monthly sales values and the rolling 3-month summary, making C the correct answer.
NEW QUESTION # 37
An app needs to load a few hundred rows of data from a .csv text file. The file is the result of a concatenated data dump by multiple divisions across several countries. These divisions use different internal systems and processes, which causes country names to appear differently. For example, the United States of America appears in several places as 'USA', 'U.S.A.', or 'US'.
For the country dimension to work properly in the app, the naming of countries must be standardized in the data model.
Which action should the business analyst complete to address this issue?
Answer: A
Explanation:
In Qlik Sense, when dealing with inconsistent naming conventions across different systems or divisions (like the variation in country names), the best practice is to standardize the data during the loading process. Using a lookup table is the most efficient approach to achieve this. This involves loading a separate table that contains all variations of a country name along with the standardized version. During the load process, Qlik Sense can then map the varying names to a common value.
Key Concepts:
Lookup Table: A lookup table contains key-value pairs where different versions of a data element (like country names) are mapped to a single standard value. In this case, the lookup table could have entries like USA, U.S.A., US all mapped to United States of America.
Data Standardization: This is crucial in ensuring consistent analysis across datasets. By converting variations of country names into a single consistent value, the business analyst ensures that all data visualizations and analysis will treat "USA", "US", etc., as the same entity.
Why the Other Options Are Less Suitable:
A . Create a calculated master dimension expression: While this could theoretically work by creating a calculated expression to handle variations, it's not scalable or maintainable, especially as new variations in country names could appear in future data loads.
C . Cleanse the source text file prior to loading: This option would require modifying the raw data files manually, which is time-consuming and not sustainable if data is frequently updated or if the number of variations is extensive.
D . Use the Replace option in Data manager: The Replace option in the Data Manager could work on a small scale, but it requires manual intervention each time, which is not efficient or sustainable when new data is loaded. Also, it's more useful for one-off corrections than for handling systemic issues across multiple data loads.
References for Qlik Sense Business Analyst:
Data Modeling Best Practices: Lookup tables are a common approach to resolve issues of inconsistent data across multiple sources. They ensure that data is consistently represented in visualizations and reduce the need for manual intervention.
Data Cleansing During Loading: Qlik Sense allows for transformation and data cleansing during the data load process. A lookup table is part of this capability and ensures that the data loaded into the app is clean and consistent.
Using a lookup table is the most scalable and maintainable approach to standardizing country names in this scenario, which is why option B is the verified solution.
NEW QUESTION # 38
A marketing team needs to display sales for a limited number of products by providing a bar chart that the user can control. The visualization has several requirements:
* Starts with the top five products
* Allows the user to change the number of products displayed
* Allows the user to scroll through all products on a mini chart
The business analyst creates a bar chat and a variable. Which steps should the business analyst complete next?
Answer: C
Explanation:
To meet the requirement of controlling the number of products displayed in the bar chart, the business analyst should use a slider object tied to a variable. The variable will store the number of products the user wants to display. In the Appearance section of the bar chart's properties, the analyst can set the number of bars to a custom value using the variable, ensuring that the user can dynamically change the number of displayed products.
Key Concepts:
Slider Object: This provides a user-friendly way for users to adjust the number of products displayed in the bar chart.
Custom Bar Limitation: By setting the number of bars displayed to a custom value controlled by the variable, the business analyst ensures that the user can dynamically adjust how many products are shown.
Why the Other Options Are Less Suitable:
B . Use the variable to fix the limitation and add an input box: While this could work, sliders provide a better, more intuitive user experience than input boxes for adjusting values dynamically.
C . Use the variable to fix the limitation and add a slider: This is almost correct, but it misses the step of setting the number of bars to a custom value using the variable.
D . Add the slider object and set its value, but without custom bar settings: While adding a slider is correct, not setting the number of bars to custom using the variable means the user wouldn't be able to dynamically control the number of displayed products.
References for Qlik Sense Business Analyst:
Dynamic Control with Variables and Sliders: Qlik Sense best practices recommend using sliders and variables to give users control over visualizations, particularly when it comes to dynamically limiting data displayed.
Thus, adding the slider object and setting the number of bars to a custom value controlled by the variable is the best solution, making A the verified answer.
NEW QUESTION # 39
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?
Answer: C
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 # 40
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