QlikView QSBA2018 Exam Dumps & Practice Test Questions

Question 1:

What is the most effective way for a business analyst to update an app to meet requirements while retaining existing charts and allowing users to compare both the new and old ProductGroup values?

A Modify the ProductGroup dimension to use the new field and create a new master item dimension for the old field.
B Create calculated dimensions directly on the charts to compare new and old values.
C Replace all chart dimensions with the new ProductGroup field from the data model.
D Apply set analysis on chart measures to highlight differences between the new and old ProductGroup fields.

Answer: A

Explanation:

When a business analyst needs to update an application while preserving existing charts and enabling users to compare both new and old ProductGroup values, the solution must be efficient and maintain usability.

Option A offers the best approach. By updating the existing ProductGroup dimension to reference the new field and simultaneously creating a new dimension for the old field within master items, the analyst ensures flexibility. This method allows all existing charts to continue functioning without modification, as they can still use the original ProductGroup dimension if needed. Meanwhile, users gain the ability to compare new and old ProductGroup values side-by-side within visualizations. Using master items centralizes dimension management, making maintenance and updates easier.

Option B, creating calculated dimensions directly on individual charts, is less ideal. Although it might technically allow comparison, it requires modifying each chart separately. This increases complexity, risks inconsistency, and makes ongoing maintenance cumbersome. It also wastes the advantage of master items, which provide reusable and standardized dimensions.

Option C, replacing all dimensions on the charts with the new ProductGroup field, fails to preserve access to the old values. This prevents users from comparing old and new values within the same chart, violating the requirement to allow side-by-side comparison. It disrupts current reports and reduces user flexibility.

Option D, using set analysis on chart measures, introduces complexity and is less intuitive for users. Set analysis is more suited for filtering and comparing aggregated data but is not the best way to display direct comparisons between two fields within the same visualization dimension. It also doesn't maintain dimension-level control as master items do.

In summary, option A ensures a smooth transition, preserves existing charts, centralizes dimension management, and meets the business need for clear comparisons between old and new ProductGroup values efficiently and effectively.

Question 2:

Which three steps should a business analyst follow to create new master items for quarterly calendar-based sales measures using company-specific conventions from 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 Choose Date, Sales, Sum aggregation, and Quarterly period for calculation.
D Add Sales to master items and rename these master items.
E Choose Date, Sales, Sum aggregation, and Yearly period for calculation.

Answer: A, C, D

Explanation:

To create accurate and company-specific quarterly calendar measures for sales, a business analyst must carefully select and configure the relevant fields and calculations.

Step A is fundamental: right-clicking the Date field in the assets panel and selecting “Create calendar measures” triggers the generation of time-based metrics like quarterly, monthly, or yearly periods. This process is key because calendar measures depend on the Date field to interpret time correctly. It enables the creation of measures such as Quarter-to-Date (QTD), Current Quarter Sales, and Last Quarter Sales.

Step B, right-clicking the Sales field to create calendar measures, is not appropriate. The Sales field contains numeric values but does not provide the necessary temporal context for calendar-based aggregation. The calendar calculations rely on the Date field, which defines the time intervals over which the aggregation occurs.

Step C involves selecting the Date and Sales fields, specifying the Sum aggregation for sales, and choosing the Quarterly period. This step configures the actual calculation method to sum sales data over quarterly intervals, aligning with the business need to measure sales performance quarterly.

Step D addresses the organization of these calculations: after creating the calendar measures, the analyst should add them to master items and rename them clearly. This step ensures the measures are reusable across reports and easily identifiable, improving consistency and clarity in analysis.

Step E, selecting yearly aggregation instead of quarterly, contradicts the requirement focused specifically on quarterly measures and is thus not relevant.

In conclusion, the correct process combines steps A, C, and D to efficiently create, configure, and manage quarterly calendar sales measures based on company conventions, ensuring accuracy and usability in the app’s analytics.

Question 3:

A business analyst is tasked with creating a dashboard to monitor customer loyalty. The dashboard must include:

• A table listing the total purchases made by each customer

• A scatter plot illustrating the relationship between the number of purchases and the total amount spent per customer

• A bar chart highlighting the top five customers based on sales

Which two measures should the analyst select to fulfill these requirements? (Choose two.)

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

Answer: B, C

Explanation:

To build a dashboard that accurately tracks customer loyalty with the specified visualizations, the business analyst needs to choose measures that directly align with each requirement.

Number of Purchases (B):
The table requires showing how many purchases each customer has made. This measure is straightforward and essential for evaluating customer loyalty by quantifying how often customers buy from the business. It provides a clear numeric value that can be aggregated per customer and displayed effectively in a table format.

Purchase Amount (C):
The scatter plot is designed to show the correlation between the number of purchases and how much each customer spends in total. To illustrate this relationship, the analyst must include the purchase amount, representing the monetary value spent by customers. This measure, when plotted against the number of purchases, helps reveal spending behavior patterns, highlighting if customers who buy more also spend more overall.

Why the Other Options Don’t Fit:

Customer Ranking (A) is useful for ordering customers by performance but is not a fundamental measure for the required table or scatter plot; it’s more of a derived or secondary metric.
Number of Products (D) relates to product diversity rather than purchase frequency or amount spent, which are the core focuses here.
Correlation (E) is a statistical relationship calculated from two measures; it’s not a raw measure itself, so it cannot be selected directly in this context.

In summary, selecting Number of Purchases and Purchase Amount enables the dashboard to meet all visualization needs: showing purchase counts, illustrating spending patterns, and ranking top customers by sales.

Question 4:

A client requests a bar chart displaying a single measure and breaking down data across three dimensions: Region, Product Category, and Month. The business analyst creates the bar chart using the measure Sum(Revenue).

What is the next best step to complete this chart?

A. Add Region, Product Category, and Month as three separate dimensions
B. Combine Region, Product Category, and Month into one master dimension
C. Use Region as the main dimension and add Product Category and Month as alternate dimensions
D. Create a cyclic group with Region, Product Category, and Month as dimensions

Answer: A

Explanation:

In this scenario, the bar chart is built on a single measure, Sum(Revenue), and the goal is to segment or break down this measure by three different dimensions: Region, Product Category, and Month. The question is about the best way to incorporate these dimensions into the chart.

Add Region, Product Category, and Month as three separate dimensions (A):
This approach is the most straightforward and effective. Adding each dimension independently allows the bar chart to display revenue data segmented by all three factors simultaneously. This means the chart will provide detailed insights into how revenue varies across regions, categories, and time periods. Keeping dimensions separate ensures the data is grouped and visualized in a meaningful and granular way, facilitating better analysis.

Why the Other Options Are Less Appropriate:
Combining the dimensions into a single master dimension (B) would merge all three categories into one field, which complicates analysis by losing the ability to filter or compare them individually. It reduces flexibility and clarity in understanding how each dimension impacts revenue.
Using Region as the main dimension and Product Category and Month as alternate dimensions (C) limits the display to one dimension at a time, which is not ideal when the requirement is to see all three dimensions simultaneously. Alternate dimensions are better suited for drill-down scenarios, not parallel comparison.
Creating a cyclic group (D) allows toggling between dimensions but does not show all dimensions together. This adds unnecessary complexity and doesn’t fulfill the need for simultaneous breakdowns.

In conclusion, the best practice is to add Region, Product Category, and Month as three separate dimensions, allowing the bar chart to clearly and effectively display the revenue breakdown as requested.

Question 5:

A large organization with over 100 departments plans a fundraising campaign over the next 30 days. To boost participation, leadership introduces a competition where departments raising $10,000 or more will earn two extra holidays for their team members. Leadership requires:

• A way to view the overall donation total

• A method to identify which departments have raised at least $10,000

Which two visualization types should the business analyst select, without using set analysis, to fulfill these needs? (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’s goal is to enable leadership to easily track the total funds raised and pinpoint departments that meet or exceed the $10,000 target. Two visualizations stand out as most effective for these requirements, especially without relying on set analysis.

Option C (Bar chart) is ideal because it visually displays donation amounts by department. Each bar represents a department, and the length corresponds to the amount raised. This format allows quick comparison across departments and immediate identification of those surpassing the $10,000 goal, especially if bars are colored or highlighted to emphasize qualifying departments. Bar charts are straightforward, scalable, and handle numerous categories well, making them perfect for organizations with many departments.

Option D (KPI) is perfect for showcasing the overall donation total. KPIs display critical metrics in a concise and prominent way, offering leadership an at-a-glance snapshot of fundraising progress. A well-designed KPI can also include supplementary info, such as the count of qualifying departments, helping leadership monitor both aggregate and qualifying performance without clutter.

Options A (Pie chart) and E (Treemap) are less suited here. Pie charts struggle with many categories and don’t clearly reveal individual thresholds, while treemaps emphasize relative proportions but make it harder to pinpoint specific values or departments reaching $10,000. Option B (Box plot) focuses on statistical distributions and outliers, which is not relevant for identifying total amounts or departments meeting set goals.

Overall, combining a bar chart and KPI visualization provides clear, actionable insights aligned with leadership’s needs, without complicating the design with advanced set analysis.

Question 6:

A business analyst is developing an app for a client who needs to:

• Display detailed, row-level transaction data

• Provide an overview of key metrics

• Enable data analysis 

What is the optimal order to organize the app’s sheets to meet these requirements?

A. 1) Dashboard: overview 2) Report: row-level details 3) Analysis: in-depth exploration
B. 1) Analysis 2) Dashboard 3) Report
C. 1) Report 2) Dashboard 3) Analysis
D. 1) Dashboard 2) Analysis 3) Report

Correct answer: A

Explanation:

The best approach to organize the app sheets aligns with a logical user journey: start broad with an overview, then provide detailed data, and finally enable deeper analysis.

Starting with the Dashboard (overview) sheet satisfies the client’s requirement to present the most important numbers upfront. Dashboards offer a high-level view of key performance indicators (KPIs) and metrics, designed for quick comprehension and decision-making. Users expect to see this summary first, as it sets the context for further exploration.

Next, the Report (row-level details) sheet allows users to drill down into specific transaction data underlying the high-level metrics. Providing access to detailed rows here lets users validate, investigate, or audit individual transactions. This step bridges the gap between summary insights and granular data.

Finally, the Analysis sheet supports advanced data examination, such as trend spotting, pattern recognition, and what-if scenarios. Positioned last, this sheet is for users who want to manipulate data further or perform complex evaluations after understanding the overall and detailed data.

Option B is not ideal because it starts with analysis, which is counterintuitive; users generally want an overview before delving into detailed or analytical content. Option C begins with row-level data, which can overwhelm users lacking initial context. Option D places detailed reports last, making it harder for users to verify insights from the overview and analysis.

Thus, sequence A ensures an intuitive, user-friendly flow that meets the client’s objectives effectively, supporting typical user behavior in business intelligence applications.

Question 7:

A retailer operating 300 locations globally needs to prepare workforce data for a board meeting, focusing primarily on total compensation and the number of employees by city. 

Which type of visualization should a business analyst choose to best meet these needs?

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

Correct answer: D

Explanation:

When tasked with presenting workforce data that includes both the total compensation and the number of employees segmented by city, it’s important to pick a visualization that clearly reflects geographical distribution along with quantitative metrics. The most fitting visualization here is a map because it inherently shows spatial relationships, allowing stakeholders to instantly recognize where employees are located worldwide and how compensation varies across cities.

Breaking down the options:

• Scatter plots are generally suited for exploring relationships between two continuous variables, such as total compensation versus number of employees, but they lack geographic context. Using a scatter plot to represent cities might confuse rather than clarify, since cities are categorical data, not numeric coordinates.

• Bar charts efficiently compare discrete categories like employee counts or compensation per city. However, they don’t visually convey geographic locations, which is a critical aspect for the board to understand the workforce distribution globally.

• Pivot tables are powerful tools for summarizing data by aggregations like total compensation or employee count. Despite their analytical strength, pivot tables are not visualizations and may not deliver the immediate clarity or impact needed for executive presentations.

• Maps integrate both location and numeric data, enabling users to see where employees are clustered and how compensation differs geographically. This dual functionality makes maps intuitive and highly effective for decision-makers to assess workforce patterns quickly.

Because the board is interested in both employee numbers and compensation by city, the visualization must show location as a key factor. A map meets this requirement perfectly by visually plotting cities and using markers or color coding to represent compensation and headcount, delivering a clear, accessible summary.

Thus, D (Map) is the best choice for communicating workforce data by city and compensation to the board effectively.

Question 8:

Which two measures should a business analyst select to build key performance indicators (KPIs) in a Qlik Sense dashboard to best reflect overall business performance? (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

Correct answer: C, D

Explanation:

When designing a Qlik Sense dashboard with KPIs, the primary goal is to include measures that offer clear, actionable insights into business health and performance. KPIs should be simple yet impactful metrics that enable decision-makers to quickly grasp essential aspects of business operations.

Among the options provided:

• Number of customers (C) is a fundamental business metric that indicates the size of the customer base. It is directly tied to growth and market reach and provides a broad view of business health. Tracking total customers helps monitor acquisition success and customer retention.

• Number of products sold (D) reflects sales volume, a key driver of revenue and operational performance. This metric gives insight into market demand and effectiveness of sales efforts, making it a critical KPI for sales-focused dashboards.

Examining the other options:

• Margin by region (A) is useful for understanding profitability but can be complex and less intuitive for a general KPI dashboard. Profit margins may be better suited for detailed financial reports rather than high-level KPIs.

• Number of products by customer (B) offers granularity on customer purchasing behavior, but it’s more detailed than broad KPIs and may complicate the dashboard’s clarity.

• Number of customers by region (E) provides useful regional breakdowns but adds complexity that might be better reserved for drill-down analysis rather than primary KPIs.

In summary, selecting Number of customers and Number of products sold ensures the KPIs are straightforward, easily interpretable, and aligned with fundamental business objectives. These measures provide an effective high-level overview that supports quick decision-making and performance tracking, making C and D the best choices.

Question 9:

A transportation company wants to examine how customers travel throughout the city in order to add more services or design new routes. The business analyst must consider the following data points:

• 190 routes spanning the city
  
  

• Start and end points of each route
  
  

• Number of passengers per hour
  
  

• Complaints received about full buses

Which type of visualization would best help the analyst to understand and act on this data?

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

Correct answer: C

Explanation:

When analyzing customer travel patterns for a bus company, the key objective is to visualize not only the geographical layout of the routes but also customer volume and complaint data tied to those routes. A Line layer map is the most effective visualization for this scenario because it clearly displays the start and end locations of each route and can overlay data such as passenger volume and complaints.

An Area layer map (Option A) is better suited for showing aggregated data over broad regions by shading areas. However, it does not effectively illustrate individual bus routes or the flow of travel between specific points, which is essential for this use case. Similarly, a Treemap (Option B) is designed to represent hierarchical or categorical data as nested rectangles, reflecting proportions, but it lacks the geographic context required to visualize routes across a city. It cannot display spatial relationships or routes effectively.

A Scatter plot (Option D) shows relationships between two variables, typically on X and Y axes, but fails to represent spatial route data. It does not map geographic paths or the volume of customers traveling specific routes.

The Line layer map excels here because it visualizes each route as a line connecting start and end points, and can incorporate additional layers—such as color coding to represent passenger volumes or markers to highlight complaint hotspots. This layered approach enables the analyst to easily identify high-traffic routes and pinpoint where overcrowding complaints are frequent, which is crucial for planning service expansions or creating new routes.

In summary, a Line layer map provides the comprehensive spatial and quantitative insights the analyst needs to make informed decisions about the bus network.

Question 10:

Within Qlik Sense Enterprise, when a published app on the hub requires quick modifications or new visualizations, which two tasks can a business analyst perform without needing to republish or alter the underlying data model? (Choose two)

A. Duplicate sheets to modify visualizations
B. Add new sheets and create visualizations
C. Import new data into the app
D. Create and edit master items
E. Define variables within the app

Correct answers: A, B

Explanation:

In Qlik Sense Enterprise, once an app is published to the hub, business analysts typically have limited permissions to make non-destructive updates. They can make certain changes that don’t affect the core data or structure, allowing for rapid adjustments or additional analysis without needing to republish the entire app.

One such action is duplicating sheets (Option A). This enables an analyst to create a copy of an existing sheet to experiment with or modify visualizations without impacting the original version. It’s a safe way to test new insights or layouts while preserving the original content for other users.

Another key action is the ability to add new sheets and create visualizations (Option B). Analysts can build entirely new views or charts within the published app, providing flexibility to explore data from different angles. These new sheets can complement existing analysis and deliver fresh insights without requiring changes to the app’s data model or re-deployment.

Conversely, adding new data (Option C) is usually restricted because integrating new data sources requires data modeling and app reloads, which are typically reserved for developers or administrators with higher privileges. Similarly, creating or editing master items (Option D) — such as predefined dimensions, measures, or visualizations — often requires developer-level access, making this action unavailable to most business analysts in a published app.

Lastly, creating variables (Option E) affects app logic and calculation behavior and is typically limited to developers or power users.

Thus, the primary actions a business analyst can confidently perform in a published app environment are duplicating sheets to safely edit visualizations and creating new sheets and visualizations for additional analysis, making A and B the correct choices.