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QSBA2018 QlikView Practice Test Questions and Exam Dumps

Question No 1:

How can the business analyst efficiently update the app to meet the requirements while using the existing charts and allowing users to compare the new and old values of ProductGroup?

A Change the ProductGroup dimension using the new field, and create a new dimension in the master items for the old field.
B Create a calculated dimension on the charts to allow the users to compare the new value to the old value.
C Replace the dimensions on all charts to use the new ProductGroup field added to the data model.
D Use set analysis on the chart measures to see the difference in ProductGroup between the new field and the old field.

Answer: A

Explanation:

To meet the requirements efficiently, the solution needs to allow the business users to continue using the existing charts, while also comparing the new and old values for the ProductGroup field. Here’s how each option plays out:

• A. Change the ProductGroup dimension using the new field, and create a new dimension in the master items for the old field: This option provides a flexible and efficient solution. By updating the ProductGroup dimension to use the new field while also creating a new dimension in the master items for the old field, the business analyst can ensure that both the new and old values are available for comparison. The existing charts can continue to function without modification, as the original ProductGroup dimension is still accessible. Users will be able to compare the old and new values directly in the visualizations, fulfilling the requirement of comparing both sets of values.

• B. Create a calculated dimension on the charts to allow the users to compare the new value to the old value: While creating a calculated dimension could allow users to compare the new and old values, this solution would require changes to each chart individually. It may be more cumbersome than the previous option because it introduces additional complexity and doesn’t take advantage of reusing the existing ProductGroup dimension as a master item. Also, it’s not as clean as having both dimensions available in the master items.

• C. Replace the dimensions on all charts to use the new ProductGroup field added to the data model: This option would update the charts to use only the new ProductGroup field, which could cause issues if the business users still need to compare the old and new values. This approach doesn’t allow for side-by-side comparison of the old and new values in the same chart or dimension. It would be a significant disruption to the current setup, and wouldn't meet the need to keep the existing charts working with the new field.

• D. Use set analysis on the chart measures to see the difference in ProductGroup between the new field and the old field: Set analysis is powerful for filtering and manipulating data, but it’s not ideal for this situation. It would be more complex to implement and might not provide the most intuitive or user-friendly way for business users to compare the old and new values directly on the charts. Additionally, it doesn't fulfill the requirement of allowing the user to easily compare values from both fields side by side.

Therefore, the best solution is A, as it allows for a smooth transition to the new field while preserving the ability to compare old and new values without disrupting the existing charts. By creating both dimensions in the master items, the business analyst ensures that the app remains functional and meets the business requirements efficiently.

Question No 2:

What three steps should the business analyst take to create new master items for quarterly calendar measures with company-specific conventions based on the Date field and Sum of Sales?

A. Right-click the Date field in the assets panel and Select Create calendar measures
B. Right-click the Sales field in the assets panel and Select Create calendar measures
C. Select Date, Sales, Sum, and Quarterly
D. Sales to master items, rename master items
E. Select Date, Sales, Sum, and Yearly

Explanation:

To meet the requirements of creating new master items for quarterly calendar measures with company-specific conventions, the business analyst needs to use the correct tools and steps in the data visualization platform.

A. Right-click the Date field in the assets panel and Select Create calendar measures is one of the steps that should be taken. By selecting the Date field and creating calendar measures, the analyst can generate the appropriate date-related measures like quarterly and yearly calculations. These measures will allow the system to handle time-based calculations correctly, which is essential for the QTD and other related metrics.

B. Right-click the Sales field in the assets panel and Select Create calendar measures is not the most relevant option. While it might be tempting to create calendar measures for the Sales field, the key measure here is based on the Date field and applying specific time-based logic (like QTD). It is the Date field that should drive the creation of calendar-based measures, not the Sales field itself.

C. Select Date, Sales, Sum, and Quarterly is another correct step. By selecting these fields—Date, Sales, and the Sum aggregation method—the business analyst can configure the calculation to focus on quarterly periods. The quarterly aggregation will be used to compute the QTD (Quarter to Date), Current Quarter Sales, Last Quarter Sales, and other related metrics.

D. Sales to master items, rename master items is another key step. Once the necessary calendar measures are created based on Date and Sales, the next step is to assign these calculations as master items. Renaming them will help make the items easier to identify and use in future visualizations. This ensures that the QTD and other measures are clearly labeled for use in analysis.

E. Select Date, Sales, Sum, and Yearly is not the right step for this scenario. Since the requirement specifies quarterly calendar measures, focusing on "Quarterly" instead of "Yearly" is critical. Yearly aggregation is not needed when the goal is to calculate quarterly measures.

In conclusion, the three steps the business analyst should take are:

1. A. Right-click the Date field in the assets panel and Select Create calendar measures

2. C. Select Date, Sales, Sum, and Quarterly

3. D. Sales to master items, rename master items

Question No 3:

A business analyst is building a dashboard to track customer loyalty. The app has several requirements:

• A table that shows the total number of purchases by customer

• A scatter plot that shows the correlation between the number of purchases and total spent by customer

• A bar chart that shows the top five customers by sales

Which two measures should the business analyst use to meet these requirements? (Choose two.)

A. Customer Ranking
B. Number of Purchases
C. Purchase Amount
D. Number of Products
E. Correlation

Answer: B, C

Explanation:

In this scenario, the business analyst is tasked with creating a dashboard that will effectively track customer loyalty using various visualizations such as a table, scatter plot, and bar chart. To achieve this, the business analyst needs to choose the most relevant measures that directly correlate to the requirements provided.

Number of Purchases (B):

The first requirement calls for a table showing the total number of purchases by customer. This measure is directly related to the objective of tracking customer loyalty. The number of purchases is the simplest and most direct metric for quantifying how often each customer makes a purchase, which is essential for understanding loyalty. This measure would be the basis for the table that lists customers and their corresponding purchase counts.

Purchase Amount (C):

The second requirement involves a scatter plot showing the correlation between the number of purchases and total spent by the customer. For this analysis, the purchase amount is critical because it directly represents the total money spent by each customer. The correlation between the number of purchases and the purchase amount is exactly what the scatter plot aims to illustrate, making this measure essential to meet the requirement. It helps in visualizing how spending increases with the number of purchases.

Why Other Options Are Not Correct:

A. Customer Ranking:
While customer ranking can be useful in certain scenarios (such as identifying top customers), it doesn’t directly contribute to the table or scatter plot requirements. In the case of the bar chart that shows the top five customers by sales, rankings could be part of the visual but are not necessary as a standalone measure. The focus here is more on purchase volume and amount rather than the ranking itself.

D. Number of Products:
The number of products a customer buys is not explicitly required in the dashboard. The question specifies tracking purchases and sales, not product count. Thus, the number of products is not relevant to the core requirements of this dashboard.

E. Correlation:
While the correlation between number of purchases and purchase amount is important for the scatter plot, correlation itself is not a measure you would select. Correlation is a derived relationship between two variables, not a raw data measure. The actual measures needed for the scatter plot are the number of purchases and the purchase amount, which will then be used to determine the correlation.

In conclusion, to meet the requirements of the dashboard, the business analyst should focus on B. Number of Purchases and C. Purchase Amount as the key measures, as these directly support the table, scatter plot, and bar chart functionalities outlined in the requirements.

Question No 4:

A client wants to see a bar chart with a single measure and three dimensions: Region, Product Category, and Month. The business analyst creates a bar chart with the measure, Sum(Revenue). 

Which final step should the business analyst take to complete the chart?

A. Add Region, Product Category, and Month as three different dimensions
B. Create a single master dimension with Region, Product Category, and Month
C. Add Region as a dimension, add Product Category and Month as alternate dimensions
D. Create a cyclic group with Region, Product Category, and Month as dimensions

Correct answer:  A

Explanation:

The question describes a situation where a bar chart is being created with a single measure, Sum(Revenue), and three dimensions: Region, Product Category, and Month. To effectively create this bar chart, the business analyst must ensure that the chart is able to handle all three dimensions properly.

Let’s break down why A is the correct answer and why the other options are less suitable.

A. Add Region, Product Category, and Month as three different dimensions:
In this case, the most straightforward and correct approach is to add each of the three dimensions—Region, Product Category, and Month—individually as separate dimensions to the bar chart. This allows the data to be grouped and displayed accurately for each of these dimensions. By adding these as separate dimensions, the chart will correctly show the Sum(Revenue) for each combination of Region, Product Category, and Month, providing the desired breakdown.

B. Create a single master dimension with Region, Product Category, and Month:
While combining dimensions can sometimes be useful for simplifying the analysis, creating a single master dimension by combining Region, Product Category, and Month is not necessary in this scenario. A master dimension would combine all these categories into a single field, making it difficult to analyze them separately. Instead, it’s better to keep them as distinct dimensions to allow for proper grouping and comparison. This would not be the best approach for the business requirement of analyzing these dimensions individually.

C. Add Region as a dimension, add Product Category and Month as alternate dimensions:
Adding Region as a primary dimension and Product Category and Month as alternate dimensions is not the best solution. Alternate dimensions are useful for drilling down into the data after the primary dimension has been selected. However, in this case, all three dimensions—Region, Product Category, and Month—are equally important and should be treated as distinct dimensions, not just as alternate dimensions. This approach could lead to limitations in how the data is presented or interacted with.

D. Create a cyclic group with Region, Product Category, and Month as dimensions:
A cyclic group is a useful feature in some scenarios where the user wants to toggle between different sets of dimensions. However, for the case at hand, it would not be necessary. The goal is to display all three dimensions simultaneously in the bar chart, and using a cyclic group would add unnecessary complexity. It would allow users to switch between dimensions but wouldn’t meet the requirement of displaying all three dimensions together on the chart.

In conclusion, the most effective way to complete the bar chart with the desired breakdown of data by Region, Product Category, and Month is to add Region, Product Category, and Month as three different dimensions to the chart. This will ensure that the data is displayed in a clear and organized way, allowing for an accurate and comprehensive visualization of the Sum(Revenue) across these dimensions.

Question No 5:

A large organization with more than 100 departments wants to raise money for a donation in the next 30 days. This year, leadership decides to increase employee participation through a competition. Team members of departments that raise $10,000 or more receive two additional holidays. Leadership needs the following capabilities:
✑ Ability to view the total donation amount
✑ Ability to identify departments that raise $10,000 or more

Which two visualizations should the business analyst use without set analysis to meet these requirements? (Choose two.)

A. Pie chart
B. Box plot
C. Bar chart
D. KPI
E. Treemap

Correct answer: C,D

Explanation:

In this scenario, the business analyst needs to help leadership easily track the total donations and identify departments that meet the $10,000 target. Two effective visualizations can provide both of these insights clearly and without the need for set analysis.

C: A bar chart is an ideal choice for this requirement because it can clearly display the donation amount for each department. Each bar can represent a department, and the length of the bar will show how much money was raised by that department. A bar chart allows the business analyst to quickly identify departments that meet the $10,000 threshold. By setting clear thresholds or coloring the bars differently (e.g., for departments raising $10,000 or more), it helps to visually highlight the departments that qualify for the additional holidays.

D: A KPI (Key Performance Indicator) visualization is perfect for showing the total donation amount. This visualization is used to highlight key metrics in a simple, straightforward manner. The total donation amount can be shown as a single number, providing an immediate snapshot of the progress toward the fundraising goal. KPIs are effective in keeping track of important figures without clutter and can be supplemented with additional KPIs to show the number of departments meeting the $10,000 target.

A: A pie chart typically works well for representing parts of a whole, but it is less effective when tracking quantitative amounts per department. In this case, it would be challenging to use a pie chart to identify departments raising over $10,000 or to show individual amounts clearly, as pie charts are not as effective with large numbers of categories or variations in the amounts.

B: A box plot is more useful for statistical distributions and outlier detection, but it doesn't clearly show total donation amounts or easily highlight departments that meet the $10,000 threshold. It's not the best option for this specific use case.

E: A treemap shows hierarchical data and can represent totals, but it's not the most efficient visualization for identifying departments with specific donation thresholds like the $10,000 target. It’s better suited for showing relative proportions, not for isolating specific data points or providing a clear count of departments meeting the criteria.

Question No 6:

A business analyst is building an app for a customer: The customer wants to be able to:
✑ Show row-level transaction details
✑ Access an overview of the most important numbers
✑ Analyze data

How should the business analyst order the sheets to meet these requirements?

A. 1. Dashboard: sheet for the overview 2. Report: sheet for the row-level details 3. Analysis: sheet for the analysis
B. 1. Analysis: sheet for the analysis 2. Dashboard: sheet for the overview 3. Report: sheet for the row-level details
C. 1. Report: sheet for the row-level details 2. Dashboard: sheet for the overview 3. Analysis: sheet for the analysis
D. 1. Dashboard: sheet for the overview 2. Analysis: sheet for the analysis 3. Report: sheet for the row-level details

Correct answer: A

Explanation: 

The best approach for ordering the sheets to meet the customer’s requirements is to start with the Dashboard to provide an overview of the most important numbers, followed by the Report to allow access to row-level transaction details, and concluding with the Analysis to enable further exploration and data analysis.

1. Dashboard: sheet for the overview – The customer requires an overview of key numbers, which is best presented in a Dashboard sheet. Dashboards are typically designed to give users a high-level view of important metrics or KPIs in an easily digestible format. This is the logical starting point for any user, as it offers quick insight into the most critical data points.

2. Report: sheet for the row-level details – Once the user has the overview, the next step would be to dig deeper into the data. The Report sheet will display detailed, row-level transaction information, allowing the customer to view the underlying details behind the numbers shown on the dashboard. This step enables users to explore specific data points and understand the finer details of their data.

3. Analysis: sheet for the analysis – After reviewing the dashboard and detailed reports, the final step would be to provide an Analysis sheet where the user can conduct deeper analysis, trends, and insights based on the data. This might involve visualizations, comparisons, or more advanced data manipulation techniques to help the user make informed decisions.

Ordering the sheets in this way ensures that the user starts with a high-level view, can drill down into specific details, and then move on to perform in-depth analysis. It’s a logical progression that mirrors the typical flow of how a user would interact with a business intelligence or analytics tool.

Option B and C place the analysis step inappropriately at the beginning or middle, which would not make sense since users typically need to see an overview and detailed data before performing analysis. Option D starts with the overview and analysis but places the row-level details last, which could be confusing since users generally need to view the detailed data after the high-level metrics are presented.

Thus, A offers the most intuitive and user-friendly sequence for achieving the customer's objectives.

Question No 7:

A retailer with 300 locations worldwide needs to analyze its workforce to prepare for its next board meeting. The two most important items to the board members are total compensation and number of employees by city. 

Which visualization should a business analyst use to meet this requirement?

A. Scatter plot
B. Bar chart
C. Pivot table
D. Map

Correct answer: D

Explanation:

When the goal is to analyze the number of employees by city and total compensation, it's crucial to select a visualization that effectively represents both the geographical distribution of the data (by city) and the size of each category (number of employees and compensation). A map is the most suitable option for this scenario.

Now, let’s break down the options:

A. Scatter plot
A scatter plot is typically used to visualize the relationship between two continuous variables. While scatter plots are excellent for spotting trends or correlations (for example, plotting total compensation against the number of employees), it is not the best option for geographical data. This type of plot would not effectively show the number of employees by city unless the cities were encoded in some way that made sense for a scatter plot, which could lead to unnecessary complexity and confusion.

B. Bar chart
A bar chart is great for comparing discrete categories, such as total compensation or number of employees across different cities. However, it does not show the geographical aspect of the data. While you could use a bar chart to represent the data, the chart would not immediately convey where the cities are located geographically. In this case, a map would be more effective for showing the locations of the cities.

C. Pivot table
A pivot table is a powerful tool for summarizing and analyzing data in a tabular format. It is often used for aggregating data, such as calculating total compensation or counting the number of employees by city. While a pivot table can help prepare the data, it is not a visualization. It does not provide a visual representation of the data that would be suitable for the board meeting. Board members are likely looking for a clear, intuitive view of both the location and size of the data, which a pivot table alone cannot provide.

D. Map
A map is the most appropriate visualization for this scenario. It allows for the geographical representation of cities, showing the distribution of employees and their total compensation across different locations. The map provides an intuitive and visually compelling way for board members to see where the workforce is located globally and how compensation varies between cities. This is particularly helpful for stakeholders to get a quick sense of the global workforce dynamics without delving into raw numbers or abstract charts.

Thus, D (Map) is the correct choice because it combines both location data and workforce metrics (employees and compensation), which is exactly what the board members are interested in for this meeting.

Question No 8:

Which two measures should the business analyst use to meet the requirements for developing a Qlik Sense app with key performance indicators on the dashboard? (Choose two.)

A. Margin by region
B. Number of products by customer
C. Number of customers
D. Number of products sold
E. Number of customers by region

Answer: C, D

Explanation:

When developing a Qlik Sense app to showcase key performance indicators (KPIs), the goal is to select measures that reflect important business metrics. KPIs typically aim to give a quick, clear view of key business drivers, so it's essential to choose metrics that are broad, easily understandable, and reflective of performance. Let's analyze the two best choices:

• C. Number of customers is an essential business metric because it provides insight into the size of the customer base, which is foundational to understanding growth, sales opportunities, and overall business performance. It can be used as a straightforward KPI to track the total number of customers, helping the organization gauge customer acquisition and retention.

• D. Number of products sold is another important metric, reflecting the volume of products sold, which directly impacts revenue, market performance, and operational efficiency. This measure is highly relevant for assessing sales performance and should be used as one of the KPIs on the dashboard to monitor business success.

Now, let's examine why the other options are less suitable as KPIs for the dashboard:

• A. Margin by region is a relevant measure for profitability but might be more detailed than a basic KPI. While this metric could be useful for a more advanced analysis, it's not as straightforward or easy to interpret as the total number of customers or products sold. Margin by region can be included in a more detailed report but might not be as effective as a high-level KPI in a general dashboard context.

• B. Number of products by customer is an interesting metric but is somewhat more granular and specific. It's useful for understanding customer behavior, but it may not provide a broad, easily interpretable view of overall business performance as a KPI would.

• E. Number of customers by region is a detailed measure that can certainly help in understanding customer distribution across different regions. However, this level of granularity might be more appropriate for a secondary analysis or drill-down rather than a high-level KPI, as it divides the customer base into regions, which is more specific than just tracking the total number of customers.

Therefore, C (Number of customers) and D (Number of products sold) are the most appropriate measures to use as key performance indicators because they are straightforward, widely applicable, and give a high-level overview of business performance.

Question No 9:

A bus company wants to analyze customer travel patterns to add additional services or create new routes. The business analyst needs to consider the following data:
✑ 190 routes across the city
✑ Start and end location of each route
✑ Volume of customers travelling per hour
✑ Customer complaints when buses are full.
 

Which visualization should a business analyst use to meet this requirement?

A. Area layer map
B. Treemap
C. Line layer map
D. Scatter plot

Answer: C

Explanation:

To analyze customer travel patterns and make decisions about new routes or additional services, the business analyst needs a visualization that allows for the representation of both geographical routes and customer data, such as travel volume and complaints. The best choice in this scenario would be a Line layer map. Here's why:

• A. Area layer map: An area layer map is typically used to display data in areas or regions, where shaded areas represent quantitative values (e.g., population density, sales volume, etc.). However, for visualizing individual routes, customer volumes, and specific complaints along those routes, an area map would not effectively display the flow or path of travel. It’s more suited for showing aggregated data over large areas, rather than detailed route-based analysis.

• B. Treemap: A treemap is used to display hierarchical data as a set of nested rectangles, where each rectangle’s size is proportional to a specific data point. While it could potentially show the proportion of customer complaints or volumes by route, it would not visualize the geographic nature of bus routes or the start/end locations effectively. It would lack the geographical context needed for this analysis.

• C. Line layer map: A Line layer map is ideal for this type of analysis because it can plot the start and end locations of each route as well as show the volume of customers traveling per hour along those routes. It can also be enhanced to display customer complaints on the routes with the help of additional markers or color coding. This visualization would allow the analyst to visually assess the coverage of routes across the city, identify areas of high customer traffic, and pinpoint routes with customer complaints due to overcrowding. The ability to map routes and incorporate various data layers (such as customer volumes and complaints) makes this the most appropriate choice for the business analyst’s needs.

• D. Scatter plot: A scatter plot is used to show the relationship between two continuous variables, typically with data points plotted on an X-Y axis. While it could show customer volumes and complaints, it would not be effective in showing the geographical aspect of bus routes or their start/end locations. The lack of spatial context would make it difficult to analyze route-specific patterns or the need for new routes.

Thus, a Line layer map allows for the most effective visualization of route-specific data, customer volumes, and complaints, providing a clear and actionable insight for decision-making regarding service expansion or route creation.

Question No 10:

Which two actions can a business analyst perform in a published app on the hub in Qlik Sense Enterprise when adjustments and new visualizations are needed quickly? (Choose two.)

A. Duplicate sheets to edit visualizations
B. Create new sheets and visualizations
C. Add data to the app
D. Create and edit master items
E. Create variables in the app

Correct answer: A,B 

Explanation:

In Qlik Sense Enterprise, business analysts can make quick adjustments and modifications to a published app, but they are generally limited to non-destructive changes that don't require altering the core data model or publishing new app versions. The following actions are feasible in this context:

A. Duplicate sheets to edit visualizations:
This action allows the business analyst to quickly make changes without modifying the original content. By duplicating a sheet, they can freely adjust or experiment with visualizations without affecting the primary work that other users may be viewing. This is a fast and efficient way to make adjustments.

B. Create new sheets and visualizations:
A business analyst can create new sheets and visualizations within the app on the hub. This allows them to add additional analysis or visualizations quickly without needing to alter existing sheets or the underlying data. Creating new sheets and visualizations is a core capability for a business analyst working in Qlik Sense.

C. Add data to the app:
This action typically requires administrative privileges and is not available for business analysts working with a published app. Adding data to the app involves modifying the app's data model, which is generally outside the scope of standard user permissions in a published environment.

D. Create and edit master items:
Master items are generally created and managed by the app developer or administrator. In many organizations, business analysts may not have permission to create or edit master items if they are not part of the app's development process. This action is more restrictive than actions like duplicating sheets or creating new visualizations.

E. Create variables in the app:
Creating and managing variables is typically reserved for developers or administrators, as variables can have a significant impact on the app's behavior. Business analysts working with published apps may not have permissions to create or modify variables, as these changes can affect how the visualizations behave.

In summary, the most likely actions a business analyst can perform in a published app on the hub are duplicating sheets to edit visualizations and creating new sheets and visualizations.