QlikView QSBA2024 Exam Dumps & Practice Test Questions

Question 1:

A business analyst needs to incorporate a list of temporary employees (interns) into the existing sales application, which already contains an employees table. When reviewing the data profiles, possible associations are displayed between tables. 

What should the business analyst do in Data Manager to correctly link these datasets?

A. Create a concatenated key to link the Employees and InternEmp tables
B. Merge the InternEmp table with the Employees table in Data Manager
C. Force an association between the InternEmp and Orders tables
D. Establish an association between the EmpID field in Employees and the EmployeeID field in InternEmp

Correct Answer: D

Explanation:

In this scenario, the business analyst’s objective is to add a list of interns to an existing sales app that already maintains an employees table. The key challenge is to ensure the intern data is properly connected to the existing employee dataset to enable accurate analysis and reporting.

Option A suggests creating a concatenated key by combining multiple fields. While concatenated keys can uniquely identify records, this approach is unnecessarily complex here because both tables already contain single identifier fields—EmpID and EmployeeID—that logically correspond to one another. Therefore, creating a composite key is redundant and complicates the data model.

Option B involves merging (concatenating) the InternEmp table into the Employees table. Concatenation is appropriate when two tables have the exact same structure and should be combined into a single table. However, here, interns and employees are conceptually distinct datasets. The requirement is to associate them, not to merge them. Merging could also cause issues if the tables have differing columns or data nuances.

Option C proposes forcing an association between InternEmp and Orders tables. This is irrelevant, as the Orders table deals with sales transactions, and no direct connection between interns and orders is mentioned or needed.

Option D is the best choice—it recommends creating an association between the EmpID field in Employees and the EmployeeID field in InternEmp. By establishing this relationship, the data model links interns to the existing employee data without merging tables. This preserves data integrity and supports queries spanning both tables.

In summary, associating the key identifier fields between the two tables maintains a clear, efficient relationship. This is the cleanest and most effective approach for integrating temporary employee data with existing employee records, making D the correct answer.

Question 2:

A business analyst is designing a new sales app requiring several visualizations with different usage patterns: a bar chart showing sales by product group used on multiple sheets, a KPI displaying total sales used once, and a treemap showing margin by product group used once inside a container. 

Which visualization should be added to the master items library?

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

Correct Answer: C

Explanation:

In data visualization tools like Qlik Sense, the master items library stores reusable assets such as visualizations, dimensions, and measures. Items placed in this library can be used consistently across multiple sheets and apps, which helps maintain uniformity and eases maintenance.

In the given requirements, the bar chart showing sales by product group is utilized on multiple sheets. Because this chart appears repeatedly, adding it to the master items library is the best practice. This allows the analyst to manage and update the chart centrally, ensuring that all instances reflect any changes without the need to modify each one separately.

The other visualizations have different usage scopes:

• Container (Option A) is not a visualization itself but a layout object that holds other visualizations. It’s typically used to organize visuals but isn’t something you'd add as a reusable chart or KPI.

• The KPI (Option B) showing total sales is used only once. Adding it to the master items library is unnecessary since it is not reused. Master items are most beneficial for repeated use, so including a single-use KPI does not optimize resource management.

• The treemap (Option D) is also used only once and inside a container. Like the KPI, it does not qualify for master items since its usage is limited.

Thus, because the bar chart needs to be reused multiple times across sheets, adding it to the master items library ensures consistency, easier updates, and better app maintenance. Therefore, C is the correct answer.

Question 3:

A movie analyst is exploring an application that provides insights on films from the early 1900s. When examining the Length Range filter, the analyst notices a hyphen appears and selects it.

What conclusion can the analyst draw from the filtered results?

A. Six movies in the dataset have invalid characters in the Length Range field.
B. Early 20th-century movies typically had varying runtimes.
C. Every movie from the 1920s and 1930s lacks Length Range data.
D. Six movies in the dataset have missing Length Range values.

Correct Answer: D

Explanation:

To correctly interpret this scenario, it is essential to understand what the hyphen symbol represents in the context of a filter on a Length Range field. Usually, a hyphen is used to indicate a range (such as "60-90 minutes"). However, in filter interfaces, a hyphen can sometimes represent missing or null values.

Option A suggests that six movies have illegal characters in their Length Range data. However, the question doesn’t mention any corrupt or invalid data—just the presence of a hyphen. Since hyphens can be valid in ranges, this option is unlikely.

Option B implies that movies early in the 20th century were inconsistent in length, which might be historically true but is irrelevant here. The question focuses on what the hyphen in the filter signifies, not on film history.

Option C states that all movies from the 1920s and 1930s have missing length data. The question does not support this blanket statement; the hyphen does not imply a decade-wide data absence.

Option D is the most plausible. In many filter tools, a hyphen may appear as a placeholder for missing or null values in a dataset. Selecting this hyphen filter often reveals which records have no valid data for that field. Therefore, the presence of a hyphen indicates that six movies lack Length Range values.

Summarizing, the hyphen in the Length Range filter likely identifies missing data entries. The analyst’s selection reveals that six movies have no Length Range information in the source data, making D the correct answer.

Question 4:

The VP of Sales requests a KPI on the sales dashboard that displays the total sales amount for the year 2022, unaffected by any user filters or selections. The data model includes fields named Sales and Year.

Which expression should the business analyst use to define this KPI?

A. Sum({ < year={"2022"} >} Sales)
B. Sum({ $ < year={"2022"} >} Sales)
C. Sum({ 1 < year={"2022"} >} Sales)
D. Sum(1 { < year={"2022"} >} Sales)

Correct Answer: C

Explanation:

To meet the VP of Sales’ requirement of displaying the total sales for 2022 independently of any other dashboard filters, the measure must ignore all user selections except for the fixed year 2022.

This is accomplished using set analysis in Qlik, a powerful syntax that allows filtering data independently of dashboard selections. The set analysis expression works inside aggregation functions like Sum().

Option A applies a filter to only the year 2022 but respects other user selections. If the dashboard user filters the data, this measure will reflect those changes, which is not desired.

Option B includes the $ sign which means the calculation uses the current selection state as the baseline and then filters on 2022. This again respects other filters and is thus incorrect.

Option C uses 1 inside the set analysis, which tells Qlik to ignore all current selections and consider the full dataset, but still applies the condition to restrict results to the year 2022. This ensures the KPI always shows total sales for 2022 regardless of user filters.

Option D is syntactically incorrect; the 1 is misplaced inside the Sum() function and set analysis brackets.

Hence, option C is the best choice because it uses the correct syntax to ignore user filters but still focus on sales data for 2022, exactly fulfilling the VP’s requirement.

Question 5:

Two clients want to use an application containing financial data, but each requires access to only a specific subset of that data for their distinct analysis goals. 

What is the recommended approach for a business analyst to handle this scenario?

A. Implement Section Access to control data visibility for each client
B. Build multiple visualizations using set analysis
C. Create duplicate versions of the app for each client
D. Unpivot and re-link data tables separately for each client

Correct Answer: A

Explanation:

When multiple users or clients need access to a shared dataset but must see different subsets of that data, the challenge is to ensure data security and relevance without duplicating efforts or compromising the data model. The best practice in such situations, particularly when using platforms like QlikView or Qlik Sense, is to leverage Section Access.

Section Access is a built-in security feature designed to restrict data at the row level based on the user's identity or role. This means the business analyst can configure the application so that each client only sees the specific portion of the financial dataset relevant to them. It dynamically applies filters based on user credentials, thus protecting sensitive data and minimizing maintenance overhead because only a single app needs to be managed.

Option B, creating multiple visualizations using set analysis, helps define subsets for display purposes but does not prevent unauthorized users from accessing the entire data. Set analysis works at the visualization layer, so it’s insufficient for security or restricting data access.

Option C suggests duplicating the app for each customer, which is inefficient and hard to maintain. This approach increases workload as every update or data change would require repeated modifications across all versions, leading to inconsistency and scalability issues.

Option D involves altering the data structure by unpivoting and re-associating tables, which complicates the data model without addressing the core problem of secure, user-specific data access.

In summary, Section Access (A) is the recommended method because it enables secure, user-based data restriction while maintaining a single app and data model, ensuring efficient maintenance and compliance with data privacy requirements.

Question 6:

An application is being developed to track student exam attempts with three main tables: Students, Exams, and Attempts. Since students can retake exams, the analyst needs to find out how many students are in the system and what percentage have not attempted any exam. 

Which metadata should the analyst examine?

A. Total distinct values and subset ratio of StudentID in the Attempts table
B. Non-null values and subset ratio of StudentID in the Students table
C. Subset ratio and present distinct values of ExamID in the Attempts table
D. Present distinct values and density percentage of ExamID in the Exams table

Correct Answer: B

Explanation:

To answer the business analyst’s questions—how many students exist in total and how many have not yet attempted an exam—it's crucial to understand the roles of each table and the metadata fields available.

The Students table holds the master list of all registered students. Examining the StudentID field here, especially the Non-null values, provides the total count of students in the system, which directly addresses the first question.

The Subset ratio of the StudentID field within the Students table indicates what portion of students have made entries related to exam attempts elsewhere in the data model. By comparing this ratio to the total student population, the analyst can calculate the percentage of students who have not attempted any exam yet. This capability directly meets the second question's requirements.

Option A focuses only on the Attempts table. While the number of distinct StudentIDs in Attempts shows how many students have tried exams, it doesn’t reveal the total number of students or those who have never attempted an exam. It’s insufficient for complete analysis.

Option C considers ExamID data in Attempts, which helps understand which exams were attempted but does not provide information about the student count or non-attempt rates.

Option D relates to the Exams table, providing insights on exam diversity and density, but is unrelated to student counts or their exam participation.

Therefore, by analyzing the Non-null values and subset ratio of StudentID in the Students table (Option B), the analyst gains both the total student count and the ability to identify how many students have yet to attempt any exams. This makes B the best choice.

Question 7:

An application is being developed to monitor experiments involving rodents navigating mazes. The Rodent table contains individual rodent details, the Mazes table holds maze metadata, and the MazeEscapes table records each maze attempt by a rodent. A business analyst wants to create a KPI that shows the number of unique rodents who have attempted at least one maze.

How should the KPI be constructed?

A. Define RodentID as RodentID_Counter in the MazeEscapes table and use Count (Distinct RodentID_Counter) as the KPI expression.
B. Define 1 as RodentID_Counter in the Rodent table and use Sum (RodentID_Counter) as the KPI expression.
C. Define 1 as RodentID_Counter in the MazeEscapes table and use Sum (RodentID_Counter) as the KPI expression.
D. Define RodentID as RodentID_Counter in the Rodent table and use Count (Distinct RodentID_Counter) as the KPI expression.

Correct Answer: A

Explanation:

The goal of this KPI is to determine how many distinct rodents have made at least one attempt in any maze. To achieve this, the analyst needs to count unique rodents recorded in the attempts, which are stored in the MazeEscapes table. Each record in MazeEscapes corresponds to one rodent’s attempt, so counting distinct RodentID values here directly indicates how many rodents participated.

Option A is correct because it assigns RodentID as a counter in the MazeEscapes table and uses a Count (Distinct RodentID_Counter) to count unique rodents who have made attempts. This method reflects exactly what the KPI requires — a count of distinct rodents linked to maze attempts.

Option B is incorrect because it assigns a counter in the Rodent table, which lists all rodents but does not track attempts. Summing this would just sum all rodents irrespective of whether they tried a maze or not, which doesn’t fulfill the KPI’s purpose.

Option C attempts to sum 1 for every MazeEscapes record, resulting in counting total attempts rather than unique rodents. This inflates the number and does not represent distinct rodents.

Option D counts distinct rodents in the Rodent table, ignoring attempts altogether. Since not all rodents may have attempted a maze, this will overstate the number.

Thus, counting distinct RodentIDs within the MazeEscapes table (Option A) is the accurate way to measure how many rodents have attempted at least one maze, fulfilling the KPI requirement precisely.

Question 8:

Users of a Qlik Sense application with roughly 10 million rows report slow performance. The current KPI measure uses the expression:
Left(Trim(TransactionName), 1) * Right(TransactionName, 5)

What should the analyst do to improve performance?

A. Request the database developer to modify the structure of the TransactionName field.
B. Use the Split function in the Data Manager to divide the field by underscore and calculate with the new parts.
C. Modify the master measure to use subfield(TransactionName, '', 1) * subfield(TransactionName, '', 3) and use the Replace function in Data Manager to remove the middle part of the field.

Correct Answer: C

Explanation:

When dealing with large datasets (like 10 million rows), efficiency in data transformations is critical. The original expression uses string functions Left(), Trim(), and Right(), which perform multiple operations per row. This complexity slows the app significantly.

Option C is the best approach because it uses the subfield() function, which is designed for splitting delimited strings more efficiently in Qlik Sense. This function extracts specific parts of the string without the overhead of trimming or manually slicing strings. By focusing on the 1st and 3rd parts of the string (assuming a delimiter), the expression simplifies calculations.

Additionally, using the Replace() function in the Data Manager to remove unnecessary middle parts of the field reduces the dataset size and complexity, further improving performance.

Option A suggests modifying the database structure, which might improve backend efficiency but is often impractical. Changing the database requires more coordination and doesn’t necessarily address performance bottlenecks in the Qlik app itself.

Option B proposes splitting the string by underscores and calculating on resulting columns. While this may help, it introduces additional fields and calculations, possibly increasing overhead rather than reducing it.

Overall, Option C is a practical, Qlik-native solution. It optimizes the KPI calculation by simplifying the expression and data shape, significantly improving app responsiveness without needing backend changes. For analysts working with large datasets, leveraging efficient native functions like subfield() and preprocessing data to reduce complexity is key to enhancing performance.

Question 9:

A clothing manufacturer operates across several European countries and needs to control user access to country-specific data. The data includes entries for France, Spain, the United Kingdom, and Germany under the field SACOUNTRY. The application employs Section Access to restrict data visibility based on users. 

What outcome should be expected from this Section Access configuration?

A. USER1 can view data for France and Spain; USER2 can access data for the United Kingdom; ADMIN has visibility over France, Spain, Germany, and the United Kingdom.
B. USER1 cannot view data for France and Spain; USER2 cannot see data for the United Kingdom; ADMIN has access to all countries.
C. USER1 cannot view France and Spain; USER2 cannot access the United Kingdom; ADMIN cannot open the application.
D. USER1 can see France and Spain; USER2 can see the United Kingdom; ADMIN sees only France, Spain, and the United Kingdom.

Answer: A

Explanation:

In QlikView and Qlik Sense, Section Access is a powerful security feature used to enforce data-level security by restricting the data a user can access within an application. This control is typically implemented by associating users with specific values in a data field — in this case, the field SACOUNTRY.

Here, the Section Access table is configured such that each user or user group is allowed access only to data for certain countries:

• USER1 has been assigned access to France and Spain. This means when USER1 logs in, the data visible to them is filtered down to only those two countries. They will not see any information related to Germany or the United Kingdom.

• USER2 is restricted to the United Kingdom data. This means USER2’s view excludes France, Spain, and Germany, ensuring data segregation based on the role.

• ADMIN typically represents a superuser or administrator account with full access permissions. Consequently, ADMIN can view data for all countries listed — France, Spain, Germany, and the United Kingdom.

The Section Access mechanism enforces this by matching the logged-in user against the Section Access table and applying data reduction based on the SACOUNTRY field values assigned to that user.

This setup ensures proper data governance, preventing unauthorized access to sensitive country-specific data while allowing necessary visibility for each user’s scope of work.

Hence, the correct expected behavior aligns with option A — USER1 sees France and Spain, USER2 accesses the United Kingdom, and ADMIN views all four countries.

This understanding of Section Access is critical for the Qlik developer role, ensuring secure, user-specific data views within enterprise applications.

Question 10:

In a QlikView application, a business analyst wants to create a dynamic chart that updates automatically based on the selection of multiple product categories. 

Which of the following approaches should the analyst use to ensure that the chart reflects the user’s current selections without requiring manual reloads?

A. Use set analysis expressions to filter the data based on the current selections.
B. Create multiple static charts for each product category and display them conditionally.
C. Apply section access to restrict data visibility in the chart.
D. Hard-code the product categories in the chart dimension properties.

Answer: A

Explanation:

In QlikView, one of the core strengths is its associative data model, which allows users to interactively explore data by making selections. To create a dynamic chart that updates automatically when multiple product categories are selected, the recommended approach is to leverage set analysis.

Set analysis enables the developer or analyst to define specific subsets of data within chart expressions, independent of current selections or in combination with them. It allows for precise control over which data is included or excluded in calculations. By writing expressions with set modifiers, the chart can dynamically respond to the user's multiple selections, reflecting changes immediately without needing a data reload.

Let’s analyze the options:

• Option A is correct because set analysis expressions dynamically filter data according to the user’s current selections, enabling responsive and interactive charts.

• Option B is inefficient and impractical; creating separate static charts for each product category is cumbersome and does not scale well, especially when users can select multiple categories.

• Option C involves section access, which is primarily a data security feature to restrict data visibility based on user roles. It does not influence how charts dynamically update based on user selections.

• Option D is incorrect because hard-coding product categories removes flexibility. It means the chart would not reflect any changes in selections, defeating the purpose of interactivity.

In summary, using set analysis (Option A) aligns with QlikView best practices for creating interactive, user-responsive visualizations. It enables analysts to build dashboards where charts immediately reflect user input, delivering a rich, exploratory analytics experience.