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Free PDF 2025 Qlik Marvelous QSBA2024 Authorized Exam Dumps

Are you planning to take the Qlik Sense Business Analyst Certification Exam - 2024 (QSBA2024) certification test and don't know where to download real and updated QSBA2024 exam questions? Actualtests4sure is offering Qlik QSBA2024 Dumps questions, especially for applicants who want to prepare quickly for the Qlik Sense Business Analyst Certification Exam - 2024 test. Candidates who don't study from real dumps questions fail to clear the Qlik Sense Business Analyst Certification Exam - 2024 examination in a short time.

Qlik QSBA2024 Exam Syllabus Topics:

Topic
Details

Topic 1

Design Applications: In this portion of the exam, professionals such as business intelligence developers and Qlik Sense designers will be tested on their competency in creating application layouts tailored to specific business demands. Candidates are also expected to showcase how they adapt and improve application designs based on input from stakeholders and evolving business needs.

Topic 2

Prepare and Load Data: This part of the [code] exam assesses the skills of developers and data analysts in preparing and loading data efficiently. It involves the creation and improvement of data models using Data Manager and verifying them through the data model viewer.

Topic 3

Develop Application: The final section of the exam measures the ability of developers to build and fine-tune Qlik Sense applications. It covers the entire development process, from initial design to the refinement of applications in real-world scenarios.

Topic 4

Identify Requirements: This section of the exam evaluates the expertise of professionals like solution architects. It emphasizes identifying Qlik Sense's capabilities to address various business needs. Participants will be tested on their ability to propose Key Performance Indicators (KPIs), dimensions, and measures that align with business objectives.

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Qlik Sense Business Analyst Certification Exam - 2024 Sample Questions (Q20-Q25):

NEW QUESTION # 20
Refer to the exhibit.

An app that will track experiments for rodents (e.g., rats and mice) that navigate mazes (labyrinths) is being developed. Individual rodents are catalogued in the Rodent table, while the Mazes table has metadata for the mazes. The MazeEscapes table holds a record of each attempt at a maze by a rodent. A business analyst needs to build a KPI that will allow users to see how many rodents have made at least one attempt at any maze.
How should the analyst construct the KPI?

A. * Create 1 AS RodentID_Counter in the Rodent table.
* Use Sum (RodentlD Counter) as the KPI expression.
B. * Create RodentlD AS RodentlD Counter in the Rodent table.
* Use Count (Distinct RodentID Counter) as the KPI expression.
C. Create 1 AS RodentID_Counter in the MazeEscapes table.
Use Sum (RodentlD Counter) as the KPI expression.
D. * Create RodentlD AS RodentID_Counter in the MazeEscapes table.
* Use Count (Distinct RodentlD Counter) as the KPI expression.

Answer: D

Explanation:
In this case, we need to count how many unique rodents have made at least one attempt at any maze. Since the relationship between the Rodent table and the MazeEscapes table is a one-to-many relationship (each rodent may attempt several mazes), the best approach is to base the calculation on the MazeEscapes table, as it holds the actual records of rodent attempts.
By creating a new field RodentID_Counter in the MazeEscapes table, the analyst ensures that each rodent's attempt is captured. Using the Count (Distinct RodentID_Counter) as the KPI expression then accurately counts each unique rodent that has made at least one attempt, without counting multiple attempts by the same rodent.
Key Concepts:
Distinct Count: The distinct count ensures that rodents who have made multiple maze attempts are only counted once.
MazeEscapes Table: This table holds the records of each attempt, making it the correct source for this KPI.
Why the Other Options Are Less Suitable:
B . Sum (RodentID_Counter) in the Rodent table: This would incorrectly sum the number of rodents rather than counting unique attempts, leading to inaccurate results.
C . Sum (RodentID_Counter) in the MazeEscapes table: Summing the counter would not account for the distinct rodents, leading to incorrect counting.
D . RodentID_Counter in the Rodent table: This would not correctly reflect the rodents that have made an attempt, as not every rodent in the Rodent table may have attempted a maze.
References for Qlik Sense Business Analyst:
Distinct Count in KPI: It is best practice to use Count(Distinct ...) when working with KPIs that require unique counts, such as counting distinct rodents in this scenario.
Thus, A is the verified answer because it ensures that each rodent is counted once, based on their attempts in the MazeEscapes table.

NEW QUESTION # 21
The VP of Sales asks a business analyst to include a KPI object on the sales dashboard that shows total sales value for the year 2022, regardless of selections. Existing fields in the data model include Sales and Year.
How should the business analyst write the measure for the KPI object?

A. Sum( 1 { < year="(" '2022'="" )="" /> ) Sales)
B. Sum( { 1 < year="{" '2022'="" )="M /> ) Sales)
C. Sum( { < year="|" '2022,="" }="" /> } Sales)
D. Sum( { $ < year="{" '2022*="" )="" /> } Sales)

Answer: B

Explanation:
In this scenario, the VP of Sales wants to see the total sales for the year 2022 regardless of selections. This requirement is best handled using Set Analysis in Qlik Sense. The key to achieving this is using the set identifier 1, which ignores the user's selections and ensures the calculation is based on the entire dataset. The expression Sum( { 1 < year = { '2022' } > } Sales) calculates the sum of sales for the year 2022, irrespective of any other selections made in the app.
Key Concepts:
Set Identifier 1: This identifier ensures that the calculation is performed on the entire dataset, ignoring any selections or filters applied by the user.
Year Filtering: The expression < year = { '2022' } > ensures that only sales from the year 2022 are included in the calculation.
Why the Other Options Are Less Suitable:
A . Sum( { < year = '2022' > } Sales): This is incorrectly formatted, and the pipe (|) symbol should not be used in this context.
B . Sum( { $ < year = { '2022' } > } Sales): The $ set identifier respects the current selections, which is not what is needed in this case, as the VP wants the value to be shown regardless of selections.
D . Sum( 1 { < year = { '2022' } > } Sales): The placement of 1 is incorrect in this expression.
References for Qlik Sense Business Analyst:
Set Analysis: Qlik Sense's Set Analysis allows analysts to control what data is used in a calculation, regardless of user selections. The set identifier 1 ensures that selections are ignored, which is essential for showing fixed data such as total sales for a specific year.
Thus, the correct expression to show the total sales for 2022, regardless of user selections, is C.

NEW QUESTION # 22
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?

A. Load a lookup table to convert values
B. Create a calculated master dimension expression
C. Use the Replace option in Data manager
D. Cleanse the source text file prior to loading

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 # 23
A business analyst is creating a new app with sales dat
a. The visualizations must meet several requirements:
A Bar chart that shows sales by product group is used in multiple sheets
* A KPI object that visualizes that the total amount of sales is used once
* A Treemap that shows margin by product group is used one time inside a Container Which visualization should be added to the master items library?

A. Bar chart
B. Container
C. KPI
D. Treemap

Answer: A

Explanation:
The Bar chart is used multiple times across various sheets, which makes it a good candidate to be added to the Master Items library. Master items are reusable components that can be added to multiple visualizations across different sheets without needing to recreate them. Since the bar chart will be reused multiple times, adding it to the Master Items ensures consistency and reusability.
Key Concepts:
Master Items: These are predefined dimensions, measures, or visualizations that can be reused across multiple sheets in an app, ensuring consistency and reducing development time.
Why the Other Options Are Less Suitable:
A . Container: The container is only used once in this scenario, so it doesn't need to be a master item.
B . KPI: The KPI is only used once, so it does not require master item status.
D . Treemap: The treemap is only used once in a container, so it doesn't need to be a master item.
References for Qlik Sense Business Analyst:
Master Items for Reusability: Qlik Sense recommends adding frequently used charts or dimensions to the master items for easy reuse and consistency.
Thus, the bar chart is the best choice for adding to the master items, making C the correct answer.

NEW QUESTION # 24
A customer is developing over 100 apps, each with several sheets that contain multiple visualizations and text objects. The customer wants to standardize all colors used every object across every app. The customer also needs to be able to change these colors quickly, as required.
Which steps should the business analyst take to make sure the color palette is easily maintained in every app?

A. * Design all base objects as master visualizations
* Link each object in each app to the relevant master visualization
* Adjust the data properties as required
B. * Create all color expressions as variables in a text file
* Load it in each app with an include statement
* Use these variables in the color property of all objects
C. * Store color definitions within a .qvd file
* Have each app load this file as a data island in the model
* Have every object select its required color property from the rows within the data island
D. * Develop the first app with every variation of object and visualization that will be required
* Duplicate this app to create every other app
* Remove the variations that are not required and adjust the ones needed

Answer: B

Explanation:
In scenarios where a customer needs to standardize colors across multiple apps and be able to update them quickly, using variables in combination with an include statement is 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 an include 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:
Variables are widely used in Qlik Sense for managing repeated expressions or values like colors. They can be defined once and reused throughout the app.
Include statements allow 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 to create all color expressions as variables in a text file and load them into each app using an include statement.

NEW QUESTION # 25
......

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