Qlik Sense Business Analyst Certification Exam - 2024 QSBA2024 Exam Practice Test

Answer : B

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.

Answer : C

To efficiently categorize support cases based on age (New, Aging, Beyond SLA) for use in multiple places across the dashboard, the Bucket option in the Data Manager is the most efficient approach. Bucketing allows the business analyst to create new categories based on the values in an existing field (in this case, the age of support cases). Since the dataset is expected to grow, creating the categories directly within Qlik Sense ensures that the process is scalable without the need for external tools or extensive coding.

Key Concepts:

Bucket Function: This allows you to group numeric fields into predefined ranges or categories. The function is highly scalable, making it suitable for large datasets.

Efficiency: Creating a new field using Bucketing ensures that the categorization is done directly in the app, avoiding the need for external data sources or nested IF statements, which could impact performance.

Why the Other Options Are Less Suitable:

A . Ask the ServiceNow team to create the field: This would create a dependency on external teams and could delay the development process.

B . Create an Excel sheet: This adds unnecessary complexity and isn't scalable as the dataset grows.

D . Write a master dimension with a nested IF statement: While this could work, it's less efficient for handling large datasets and could result in slower performance.

References for Qlik Sense Business Analyst:

Bucketing Data: Qlik Sense recommends using the Bucketing feature for creating predefined ranges or categories, especially when dealing with large datasets.

Thus, using the Bucket option to create a new field for categories is the most efficient solution, making C the correct answer.

Answer : C

In Qlik Sense, the Insights Advisor Chat is the feature that allows users to interact with the app through natural language questions. This tool leverages Qlik's advanced AI and machine learning capabilities to interpret natural language queries and generate relevant insights, visualizations, or suggestions for analysis.

A . Advanced Authoring Advanced Authoring is a set of tools in Qlik Sense designed for creating detailed visualizations and reports, but it does not include natural language interaction capabilities. It is focused more on customization and precise design rather than conversational querying.

B . Story and Bookmarks Storytelling and bookmarks in Qlik Sense are tools for narrative data presentations and saving specific states of analysis. They do not provide the ability to ask natural language questions or automatically generate insights.

C . Insights Advisor Chat Insights Advisor Chat is the correct answer. This feature allows users to interact with their data by typing natural language questions, which the system interprets to generate appropriate responses, including charts, KPIs, and other insights. It is designed to assist non-technical users by making data exploration more intuitive and accessible through natural language.

D . Associative Engine The Associative Engine is the underlying technology that allows Qlik Sense to handle large datasets and perform associative searches across them. While it is powerful for data exploration, it does not provide a direct interface for natural language querying like Insights Advisor Chat does.

Key Qlik Sense Business Analyst References:

Insights Advisor Chat is a feature in Qlik Sense that empowers users to ask questions in natural language and get meaningful responses without needing to be data experts.

It is part of Qlik Sense's broader set of augmented intelligence tools that enhance the user experience by providing guided insights and helping users discover relationships in data through natural language queries.

This feature simplifies data exploration for business users who might not be familiar with complex data querying techniques.

Thus, the feature that allows users to use natural language questions in Qlik Sense is Insights Advisor Chat.

Answer : A

To meet the requirement of dynamically changing the number of countries and cities displayed in the visualizations, the business analyst should use variables to control the number of countries and cities shown. By creating two variables (vCountry and vCity), the analyst can use sliders to allow the user to adjust the maximum number of countries (up to 10) and cities (up to 5 per country) that are displayed. These variables can then be applied to each visualization to control the displayed data.

Key Concepts:

Variables: Variables allow users to dynamically adjust values in Qlik Sense visualizations. In this case, sliders can be used to change the number of countries and cities displayed.

Slider Object: Adding a slider enables users to easily adjust the values of the variables vCountry and vCity in a user-friendly manner.

Why the Other Options Are Less Suitable:

B . Input field and dimension limitation: This option is unnecessarily complex and doesn't provide the same dynamic control as using variables and sliders.

C . Input field without sliders: While input fields could work, sliders offer a more intuitive way for users to adjust the values dynamically.

D . Sheet action: Setting a sheet action to limit the data displayed is less flexible and doesn't provide the same dynamic interaction as using variables and sliders.

References for Qlik Sense Business Analyst:

Dynamic Visualizations with Variables: Qlik Sense recommends using variables and interactive objects like sliders to give users control over dynamic data visualizations.

Thus, the most effective solution is to create variables and use sliders to dynamically control the number of countries and cities, making A the correct answer.

Answer : A

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.

Answer : B

In Qlik Sense, Set Analysis is one of the most powerful tools available to a Business Analyst for managing different subsets of data within the same app. Since both customers are working with the same finance dataset but have different objectives, creating multiple visualizations using set analysis allows the analyst to tailor the data views for each customer without duplicating the app or creating complex data models.

Key Concepts:

Set Analysis: This feature enables the creation of expressions that define subsets of data, allowing you to filter data within specific visualizations. This is ideal when multiple users need different insights from the same underlying dataset.

Flexibility: Using set analysis, you can specify conditions within individual visualizations so that each user can focus on their own segment of the data without impacting others.

Efficiency: This method avoids redundancy by ensuring you only need one app and one data model, instead of duplicating and maintaining multiple apps or applying complex logic such as Section Access.

Why the Other Options Are Less Suitable:

A . Apply Section Access: While Section Access is useful for managing security and limiting what users can see in the entire dataset, it is primarily designed to restrict data access based on user roles. In this case, both users need access to the same dataset but will conduct different analyses. Section Access would be an overly restrictive and complex solution for this scenario.

C . Duplicate and rename the apps: This is inefficient because it leads to redundancy and makes maintenance harder (e.g., any changes to the dataset or visualizations would need to be applied to both apps). It also increases the risk of inconsistencies across versions of the app.

D . Unpivot and re-associate the data tables: This option is not relevant to the problem, as unpivoting is more appropriate for transforming datasets rather than tailoring views for different users within the same app. It does not address the need for customer-specific analysis objectives.

References for Qlik Sense Business Analyst:

Set Analysis: In the Qlik Sense Business Analyst's toolkit, Set Analysis is covered as a method to manage diverse data subsets within single apps, providing the flexibility needed in multi-user environments without duplicating content.

Efficient Application Design: Best practices suggest maintaining a single app where possible to ensure consistency and ease of maintenance, which aligns with the approach of using Set Analysis.

By using Set Analysis, you provide both customers with tailored data views that are easily managed and updated within a single app. This is why option B is the most effective and verified solution.

Answer : D

A scatter plot is the best choice for visualizing outliers, clustering, and products that are above the median sales volume. Scatter plots are excellent for showing relationships between two variables (like sales volume and margin %) while providing the ability to highlight outliers. By using K-Means clustering to color the products, the business analyst can visually group similar products. Additionally, adding a median sales volume reference line makes it easy to identify which products are above or below the median.

Key Concepts:

Scatter Plot: This visualization is ideal for detecting outliers and understanding the distribution and clustering of data points.

K-Means Clustering: This technique groups similar data points (products) based on their values, which helps to identify patterns visually.

Median Reference Line: Adding a reference line for median sales volume ensures that the analyst can easily see which products are performing above or below the median.

Why the Other Options Are Less Suitable:

A . Pivot table: While a pivot table could show aggregated data, it is not as effective for visualizing outliers or clustering as a scatter plot.

B . Treemap: A treemap can show hierarchical relationships but is not as effective for identifying clusters or outliers.

C . Combo chart: A combo chart is not as well-suited for visualizing clustering and outliers as a scatter plot.

References for Qlik Sense Business Analyst:

Outliers and Clustering Visualization: Qlik Sense recommends scatter plots for visualizing relationships between two measures and for detecting outliers and clustering in the data.

Thus, creating a scatter plot with K-Means clustering and a median sales volume reference line is the best approach, making D the verified answer.