QlikView QSBA2024 - Questions & Answers

How many students are in the system?
What percentage of students have not yet attempted an exam?

The analyst needs to focus on the StudentID field, specifically in relation to the Attempts table. This is because the Attempts table captures all exam attempts made by students, and we can deduce which students have and have not made an attempt by examining the presence of StudentID values in this table.
Key Concepts:
Total Distinct Values: This provides the total number of unique students who have attempted exams. It helps identify how many students have made at least one attempt.
Subset Ratio: This compares the values of StudentID between the Students table and the Attempts table.
The subset ratio shows how many students in the Students table are represented in the Attempts table.
This ratio helps determine the percentage of students who have not yet attempted any exams.

Why the Other Options Are Less Suitable:
B. Non-null values and Subset ratio for the StudentID field in the Students table: Thenon-null values in the
Students table are not relevant to the question about exam attempts. The focus should be on whether the StudentID is present in the Attempts table.
C. Subset ratio and Present distinct values for the ExamID field in the Attempts table: This focuses on
exams, not students. The question specifically relates to how many students have attempted exams.
D. Present distinct values and Density% for the ExamID field in the Exams table: This focuses on the
number of exams and their density, which does not help in determining how many students have attempted or not attempted an exam.

References for Qlik Sense Business Analyst: Subset Ratio and Distinct Counts: Qlik Sense's data model viewer provides valuable metadata like the distinct count of a field and its subset ratio when compared to related fields in othertables. This is particularly useful for understanding relationships and gaps in the data, such as identifying students who have not yet made an exam attempt.
By focusing on the Total distinct values and Subset ratio for the StudentID field in the Attempts table, the business analyst can easily determine the total number of students and the percentage who have not yet attempted an exam, making the verified answer. A

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 is the verified solution. B

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, is the verified answer because it ensures that each rodent is counted once, based on their attempts in A the MazeEscapes table.

A line chart is the most appropriate visualization for comparing two continuous measures (Profit and Revenue) over time. Line charts are designed to show trends and patterns over a continuous scale (such as time), making it ideal for this scenario where we need to observe how both Profit and Revenue vary across a period.

Key Concepts:
Continuous Scale: Line charts are specifically suited for continuous data like time, making them the preferred choice when tracking changes over time for multiple measures.
Dual Measure Comparison: A line chart allows you to plot two measures on the same axis, making it easy to compare their trends over the same period.

Why the Other Options Are Less Suitable:
B. Bullet chart: A bullet chart is used to compare a single measure against a target, not for tracking two
measures over time.
C. Bar chart: Bar charts are better suited for comparing categorical data, not continuous measures over
time.
D. Scatter plot: Scatter plots are used to compare relationships between two measures but are not suited
for continuous time-based comparisons.

References for Qlik Sense Business Analyst: Line Charts for Time Series Data: Line charts are the recommended visualization for comparing multiple measures over time in Qlik Sense, especially when working with continuous data like Profit and Revenue.
Thus, the line chart is the best choice for this scenario, making the correct answer. A

When users need control over how many product categories are shown in a bar chart, the most effective solution is to use a variable input object . This allows users to dynamically adjust the number of categories displayed.

A. Create a variable and variable input object and use the variable in the dimension limit field. This is the
correct solution. By creating a variable and using the Variable Input object , the usercan dynamically control the number of product categories shown in the bar chart by adjusting the dimension limit. This method provides flexibility and an intuitive interface for the user.
B. Use firstsortedvalue() function to extract the required product categories.The firstsortedvalue() function
is typically used to extract the first occurrence of a value based on sorting criteria, but it's not the best approach for controlling the number of displayed categories dynamically.
C. Create a variable and variable input object and use the variable in the sales expression.While variables
can be used in expressions, this approach is less efficient than using the dimension limit field, which is specifically designed for controlling the number of displayed values.
D. Use a rankQ function in the sales expression.The rankQ function ranks data, but it's not the most
efficient or intuitive method for dynamically controlling the number of product categories displayed in a bar chart. It would require more complex expressions compared to the straightforward use of a variable in the dimension limit field.

References:
The Variable Input object allows users to interact with and adjust variables within the app. This is ideal for giving users control over visual elements like the number of categories displayed in a chart.
The Dimension Limit field is specifically designed to control how many items (like product categories) are shown in a chart based on a ranking or expression.
Thus, the best approach to allow users to control the number of product categories displayed is tocreate a variable and variable input object, and use the variable in the dimension limit field .

A Network chart is the most suitable visualization for representing processes and tasks that have dependencies, such as projects with co-dependent tasks and subtasks. The network chart allows you to visualize relationships between nodes (in this case, tasks and subtasks) and can display them in a structured manner with lines connecting them based on their relationships. The colors of the boxes (or nodes) can be determined by the status of each project or task, which matches the requirements.

Key Concepts:
Network Chart: It's designed for showing interconnections or relationships between various elements. It is ideal when tasks or processes have dependencies or subtasks that need to be visually represented with links between them.
Color Representation: In a Network Chart, you can easily apply colors to nodes based on specific criteria, such as the status of the task, making it easier for users to track project progress at a glance.

Why the Other Options Are Less Suitable:
A. Sankey chart: While Sankey charts are used to show flow and relationships between categories, they
are better suited for representing flows of data or values between stages, not hierarchical or task-related dependencies.
B. Grid chart: A grid chart is used to display values in a matrix but does not provide the relational and
hierarchical representation needed for tasks and subtasks.
C. Org chart: Org charts are useful for showing hierarchies but are more structured for organization
personnel or roles rather than co-dependent tasks and workflows.

References for Qlik Sense Business Analyst: Network Diagram: Network charts are widely used for visualizing complex relationships between entities, which aligns with the need to visualize tasks and subtasks in project management.
Thus, a Network chart provides the best solution for visualizing tasks and subtasks with their dependencies, making the correct answer. D

A scatter plot is the most appropriate visualization for analyzing the correlation between Product Sales ($) , Order Volume , and Margin % . Scatter plots are ideal for showing relationships between two or more continuous variables, which is crucial for identifying trends or correlations among these measures.

Key Concepts:
Scatter Plot: This chart type is specifically designed to display correlations between measures, making it the ideal choice for visualizing relationships between Product Sales, Order Volume, and Margin %.
Multiple Measures: Scatter plots in Qlik Sense can plot two measures on the X and Y axes and can use colors or bubbles to represent additional measures (such as Margin %).

Why the Other Options Are Less Suitable:
A. Multi KPI: A Multi KPI displays multiple metrics but doesn't show correlations between them.
B. Combo chart: A combo chart combines bar and line charts but is not suited for analyzing correlations
between multiple continuous measures.
D. Pivot table: While useful for data aggregation, a pivot table does not provide a clear visualization of
correlations between measures.

References for Qlik Sense Business Analyst: Scatter Plot for Correlation Analysis: Scatter plots are recommended in Qlik Sense when exploring relationships between multiple continuous variables.
Thus, the scatter plot is the most effective visualization for analyzing the correlation between Product Sales, Order Volume, and Margin %, making the correct answer. C

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 amedian 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 the verified answer. D

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 the verified answer. B

In this scenario, the second measure (Count of MajorCities) is likely not being displayed because the two measures-- Sum(Knowing sections) and Count(MajorCities) --are on vastly different scales. When two measures have significantly different ranges, one of them may not be visible on the same Y-axis, causing the issue you're seeing where no bars are visible for the second measure.

By converting the bar chart to a combo chart , the business analyst can display both measures with appropriate configurations. The combo chart allows you to display different measures in different ways, such as using one axis for the first measure (e.g., bars for Sum(Knowing sections) ) and another axis for the second measure (e. g., bars for Count(MajorCities) ), ensuring that both are visible on the chart.

Key Concepts:
Combo Chart: This type of chart allows you to display multiple measures using different axis scales or types of visualization (e.g., bars and lines).
Scale Mismatch: When two measures differ significantly in scale, they may not be displayed properly on the same axis. A combo chart helps by allowing separate Y-axes for each measure.

Why the Other Options Are Less Suitable:
A. Enable Value labels: While value labels can help show specific data points, they won't resolve the issue
of one measure being invisible due to scale differences.
B. Recreate as an alternative measure: This would allow switching between measures, but the
requirement is to show both measures simultaneously.
C. Change Y-axis Range to Custom: While adjusting the Y-axis manually might help, it's not the best
solution because the scale difference between the two measures might still cause issues, and it would be harder to adjust dynamically.

References for Qlik Sense Business Analyst: Combo Charts for Multiple Measures: Combo charts are recommended in Qlik Sense when you need to display multiple measures with different scales.
Thus, converting the bar chart to a combo chart ensures both measures are properly displayed, making the D correct answer.