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QSBA2024 QlikView Practice Test Questions and Exam Dumps

Question No 1:

A business analyst must add a list of temporary employees (interns) to the current sales app. The app contains an existing employees table. When the business analyst profiles the data, the association view displays possible associations as shown. 

Which action should the business analyst take in Data manager to meet the requirements?

A. Create a concatenated key to associate the Employees and InternEmp tables
B. Concatenate the InternEmp table to the Employees tables within Data manager
C. Force an association between the InternEmp and the Orders tables
D. Create an association between the EmpID and EmployeeID fields

Answer: D

Explanation:

In the context of the question, the business analyst's goal is to add a list of temporary employees (interns) to the current sales app, which already contains an existing employees table. To integrate this list of interns into the sales app correctly, it's essential to establish a relationship between the two datasets — the existing Employees table and the InternEmp (interns) table.

Let's analyze each option to understand which action is the most appropriate:

• A. Create a concatenated key to associate the Employees and InternEmp tables:
  A concatenated key refers to combining two or more fields to form a unique key. However, creating a concatenated key is generally unnecessary for simple associations between tables, especially if there are fields like EmpID or EmployeeID already available in both tables. This option could be an overcomplication and is not the best choice.
  
  

• B. Concatenate the InternEmp table to the Employees table within Data manager:
  Concatenating tables involves merging the datasets, which could be useful if both tables have identical structures and you want to combine the information into a single table. However, in this case, the goal is not to merge the two datasets but rather to associate them. Concatenation is typically used when the two datasets have complementary data with the same schema, and that is not the requirement here. The business analyst needs to associate the intern data with the existing employee data, not combine the tables.
  
  

• C. Force an association between the InternEmp and the Orders tables:
  This option is irrelevant to the business analyst's needs. The Orders table is likely related to sales transactions, and there's no mention of a relationship between interns and orders in the question. The goal is to associate the intern data with the employee data, not the orders data.
  
  

• D. Create an association between the EmpID and EmployeeID fields:
  This is the most appropriate action. By creating an association between the EmpID in the Employees table and the EmployeeID in the InternEmp table, the business analyst can link the intern data to the existing employee records without merging the tables. This approach will ensure that both datasets are properly associated and that the intern data can be accessed alongside employee data in the app.

The best approach is to establish a clear association between the EmpID (or equivalent field) in both the Employees and InternEmp tables, as this creates the relationship needed for analysis and reporting without unnecessary data manipulation. Therefore, the correct action is D. Create an association between the EmpID and EmployeeID fields.

Question No 2:

A business analyst is creating a new app with sales data. The visualizations must meet several requirements:

• A Bar chart that shows sales by product group is used in multiple sheets

• A KPI object that visualizes the total amount of sales is used once

• A Treemap that shows margin by product group is used one time 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 Qlik Sense or other similar data visualization platforms, the master items library is a collection of reusable visualizations, dimensions, and measures that can be used across multiple sheets and applications. This ensures consistency in design and functionality, as the items are centrally managed and can be easily reused.

The requirement specifies the need for the Bar chart to be used in multiple sheets, which makes the Bar chart the most appropriate candidate for inclusion in the master items library. By adding the Bar chart to the master items library, the business analyst can easily reuse it across different parts of the app, ensuring that the sales by product group visualization is consistent throughout the app.

Let's analyze the other options:

• A. Container: A container is typically used to group multiple visualizations together in a structured layout, but it is not a visualization itself. It is more of a container for holding other visual elements. Therefore, it wouldn't be added to the master items library as a reusable visualization.

• B. KPI: While the KPI is an important element to visualize the total sales amount, it is only used once in the application. Since the requirement specifies the need for the Bar chart to be used across multiple sheets, adding the KPI to the master items library isn't necessary in this case.

• D. Treemap: The Treemap is only used once inside a Container, meaning it doesn't meet the criteria for something that needs to be reused across multiple sheets. While it is an important part of the app, it is not a prime candidate for inclusion in the master items library as a reusable visualization.

In conclusion, the Bar chart is the best choice for inclusion in the master items library because it is used across multiple sheets, ensuring consistency and ease of reuse throughout the application. Therefore, the correct answer is C.

Question No 3:

A movie analyst is using an app to gain insights into films created in the early 20th century. The analyst reviews the filter for Length Range, notices a hyphen and selects it. 

What can the analyst determine from the resulting filter panes?

A. Six movies in the source data contain illegal characters for the Length Range field.
B. Movies at the start of the 20th century often varied in length.
C. All movies from the 1920s or 1930s contain no data for Length Range.
D. The source data for six movies is missing a Length Range.

Answer: D

Explanation:

To answer this question, let’s first interpret the scenario: The movie analyst is using a filter for Length Range and notices a hyphen. The hyphen is commonly used as a separator or to indicate a range (e.g., 60-90 minutes), but in this context, the presence of the hyphen in the filter suggests an issue with the data or how it is being displayed.

A. Six movies in the source data contain illegal characters for the Length Range field:

• The question doesn’t suggest that the movies contain illegal characters for the Length Range field. A hyphen typically indicates a valid range of values. Therefore, this answer is not correct because there’s no evidence suggesting illegal characters are involved.

• A is incorrect.

B. Movies at the start of the 20th century often varied in length:

• While it is true that movies in the early 20th century could vary in length, this answer is not directly related to the filter issue with the hyphen. The question focuses on a technical issue regarding the filter and its data, not the historical variation in movie lengths.

• B is not the best choice.

C. All movies from the 1920s or 1930s contain no data for Length Range:

• The filter problem regarding the hyphen suggests a data issue but does not specifically indicate that all movies from these decades are missing data for Length Range. Instead, the presence of the hyphen is more likely to suggest an incomplete or missing data entry for some of the movies.

• C is not supported by the scenario described.

D. The source data for six movies is missing a Length Range:

• D is the most plausible answer. If the analyst notices a hyphen when using the filter, this might indicate that some movies have missing data for the Length Range field. A hyphen could appear when there’s a missing or null value for that field, and selecting the hyphen in the filter could reveal that six movies have incomplete or missing length information.

• D is the correct choice because it logically explains the data problem described in the question.

The most likely explanation for the hyphen appearing in the filter is that six movies in the source data are missing a value for Length Range. Therefore, D is the correct answer.

Question No 4:

The VP of Sales asks a business analyst to include a KPI object on the sales dashboard that shows total sales value for the year 2022, regardless of selections. Existing fields in the data model include Sales and Year. 

How should the business analyst write the measure for the KPI object?

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

Answer: C

Explanation:

To create a KPI object on the sales dashboard that consistently shows the total sales for the year 2022 regardless of any selections made by the user in the dashboard, the business analyst must use a measure that disregards any selections or filters except for the year condition (i.e., the year should always be 2022).

Step 1: Review the set analysis syntax

The syntax for set analysis in QlikView or Qlik Sense, which is used for filtering data based on specific conditions, is crucial here. The basic structure of a set analysis expression is:

Where:

• Sum() calculates the sum of the Sales field.

• { <condition> } specifies the filtering criteria to be applied. This allows for filtering data based on specific conditions, such as a fixed year.

Step 2: Understand the options

• A. Sum( { < year="{'2022'}" /> } Sales): This expression would work if we are directly specifying the year condition inside the set analysis, but it would respect any selections already applied to the data model, which is not the intended behavior here. It is important that this KPI ignores other selections and only shows the sales for the year 2022.

• B. Sum( { $ < year="{'2022'}" /> } Sales): The $ in this case indicates that the condition should respect the current selections and would result in a dynamic filter. This is not what is needed because the goal is to show the sales for 2022 regardless of the user's current selections.

• C. Sum( { 1 < year="{'2022'}" /> } Sales): This is the correct syntax. The 1 in the set analysis tells Qlik to ignore any selections made in the application, ensuring that the calculation of the total sales for 2022 disregards all other filters. This forces the KPI to always show the total sales for the year 2022, regardless of what the user selects.

• D. Sum( 1 { < year="{'2022'}" /> } Sales): This expression is syntactically incorrect. The 1 should appear outside of the {} to correctly ignore selections, not as part of the Sum() function.

The correct choice is C. By using the 1 < year="{'2022'}" />, we ensure that the KPI reflects the total sales for the year 2022, ignoring any other selections in the dashboard or other filters that may be applied by the user. This approach provides the static result needed by the VP of Sales.

Question No 5:

Two customers in an organization want to use an app that contains a finance data set. With different analysis objectives, each customer will only use a subset of that data. 

Which procedure should the business analyst follow?

A. Apply Section Access to manage the data for each customer
B. Create multiple visualizations using set analysis
C. Duplicate and rename the apps for each customer
D. Unpivot, then re-associate the data tables for each customer

Answer: A

Explanation:

When two customers need to access a shared finance dataset but only use different subsets of the data, the business analyst must ensure that each customer only sees the data relevant to their analysis objectives, while maintaining a secure and efficient workflow.

Let's examine the options:

1. A. Apply Section Access to manage the data for each customer: This is the correct approach. Section Access is a feature in data visualization tools like QlikView or Qlik Sense that allows the business analyst to restrict access to specific portions of the data based on user roles or other criteria. This ensures that each customer only has access to the data they are authorized to see, and it helps in managing data security and privacy. By applying Section Access, the business analyst can dynamically filter the data visible to each user based on their specific needs.
   
   

2. B. Create multiple visualizations using set analysis: Set analysis allows for defining specific subsets of data for use in visualizations, but it does not control data access at the user level. It is useful for creating comparative or aggregated visualizations for different groups of data, but it does not prevent unauthorized users from accessing the full dataset. In this case, set analysis would help in visualizing subsets of the data, but it won't restrict access to data based on customer needs, making it less suitable for managing data access between customers.
   
   

3. C. Duplicate and rename the apps for each customer: Duplicating the app and renaming it for each customer might seem like a solution, but it can lead to inefficiency and maintenance challenges. Managing multiple versions of the app for each customer increases complexity and doesn't scale well, especially if the data set changes or needs to be updated regularly. A better solution would be to use Section Access within a single app to manage customer-specific data access, rather than duplicating the app.
   
   

4. D. Unpivot, then re-associate the data tables for each customer: Unpivoting and re-associating data tables would change the structure of the data but would not address the issue of user-specific data access. This approach might complicate the data model unnecessarily and does not offer a direct method of managing data access for individual users. It would also potentially make the app more complex and harder to maintain.

In conclusion, the best solution is A. Apply Section Access, as it allows the business analyst to securely manage data access based on customer roles, ensuring that each customer only sees the data they are authorized to use while maintaining a single app for all users.

Question No 6:

An app is being developed at a university to monitor student exam attempts. Three core tables are loaded into the app for Students, Exams, and Attempts. Students can attempt the same exam multiple times.Before building any visualizations, the business analyst needs to know:

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

Which metadata should the analyst focus on to answer these questions?

A. Total distinct values and Subset ratio for the StudentID field in the Attempts table
B. Non-null values and Subset ratio for the StudentID field in the Students table
C. Subset ratio and Present distinct values for the ExamID field in the Attempts table
D. Present distinct values and Density% for the ExamID field in the Exams table

Correct Answer: B

Explanation:

In this scenario, the business analyst needs to determine two things:

1. The total number of students in the system.

2. What percentage of students have not yet attempted an exam.

To answer these questions effectively, the analyst needs to understand the relationship between the Students table and the Attempts table.

Option A: Total distinct values and Subset ratio for the StudentID field in the Attempts table

This option focuses on the Attempts table, which contains data about student exam attempts. However, although it can give you the number of distinct student IDs that have made attempts, it does not help determine how many students have not attempted an exam at all. Thus, it's not ideal for answering both questions.

Option B: Non-null values and Subset ratio for the StudentID field in the Students table

This option is the best choice. The Students table holds the full list of students, so it is necessary to focus on the StudentID field in this table. The Non-null values give the total number of students in the system, which answers the first question. The Subset ratio in the Students table will allow the analyst to calculate the percentage of students who have not made any exam attempts (i.e., students whose IDs do not appear in the Attempts table). This option directly supports both of the business analyst's needs.

Option C: Subset ratio and Present distinct values for the ExamID field in the Attempts table

This option focuses on the ExamID field in the Attempts table, which will help understand which exams students have attempted. However, this doesn't directly address the number of students or those who haven't attempted any exam, making it less useful for answering the business questions.

Option D: Present distinct values and Density% for the ExamID field in the Exams table

This option involves the Exams table and provides information on distinct exams and their density, but it doesn’t relate to the number of students or attempts. It is not helpful in answering the two questions about student attempts.

Option B is the most appropriate choice because it allows the business analyst to calculate the total number of students and the percentage who have not attempted any exams by analyzing the StudentID field in the Students table.

Question No 7:

An app that will track experiments for rodents (e.g., rats and mice) that navigate mazes (labyrinths) is being developed. Individual rodents are catalogued in the Rodent table, while the Mazes table has metadata for the mazes. The MazeEscapes table holds a record of each attempt at a maze by a rodent. A business analyst needs to build a KPI that will allow users to see how many rodents have made at least one attempt at any maze.

How should the analyst construct the KPI?

A. Create RodentID AS Rodent ID_counter in the MazeEscapes table.Use Count (Distinct RodentID_Counter) as the KPI expression.
B. Create l AS RodentID_Counter in the Rodent table.Use Sum (RodentID_Counter) as the KPI expression.
C. Create 1 AS RodentID_Counter in the MazeEscapes table.Use Sum (RodentID_Counter) as the KPI expression.
D. Create Rodent ID AS RodentID_Counter in the Rodent table.Use Count (Distinct RodentID_Counter) as the KPI expression.

Answer: A

Explanation:

The goal of this KPI is to count how many distinct rodents have made at least one attempt at any maze. Let's examine each option:

• A. Create RodentID AS Rodent ID_counter in the MazeEscapes table. Use Count (Distinct RodentID_Counter) as the KPI expression.
  This option is the best approach. The MazeEscapes table records each attempt by a rodent at a maze. By counting the distinct RodentID values in this table, we can determine how many unique rodents have participated in at least one attempt at any maze. This will give us the exact number of distinct rodents involved in maze trials, which is what the KPI is asking for.

• B. Create l AS RodentID_Counter in the Rodent table. Use Sum (RodentID_Counter) as the KPI expression.
  This option is not suitable because summing the RodentID_Counter in the Rodent table would not give an accurate count of rodents that attempted any maze. The Rodent table contains information about all rodents, but it doesn't track maze attempts. This would not help in determining how many rodents have made at least one attempt at a maze.

• C. Create 1 AS RodentID_Counter in the MazeEscapes table. Use Sum (RodentID_Counter) as the KPI expression.
  This approach is incorrect because it would sum up 1s for each record in the MazeEscapes table, which would just give the total number of attempts rather than the number of distinct rodents. This method doesn’t answer the question, as we are interested in counting distinct rodents, not attempts.

• D. Create Rodent ID AS RodentID_Counter in the Rodent table. Use Count (Distinct RodentID_Counter) as the KPI expression.
  While this option uses the Rodent table, it is not effective because it doesn’t take into account the rodent's attempts at the mazes. The Rodent table alone doesn’t track how many times a rodent has tried a maze; it only catalogs the rodents. The MazeEscapes table, which links rodents to specific maze attempts, is necessary to build this KPI.

Therefore, the best choice is A, as it directly counts distinct rodents who have made attempts at mazes, which is the KPI the analyst needs.

Question No 8:

The users of a Qlik Sense app report slow performance. The app contains approximately 10 million rows of data. The business analyst notices the following KPI master measure definition:

Left( Trim( TransactionName), 1 ) * Right ( TransactionName, 5)

Which steps should the business analyst complete to improve app performance?

A. Ask the developer of the underlying database to change the structure of the field TransactionName.
B. - In the Data manager, use the Split function to split the field values with the underscore character as the separator.

C. Change the master measure definition as follows:subfield( TransactionName, '', 1) * subfield( TransactionName, '', 3)
In the Data manager, use the Replace function to remove the middle part of the field TransactionName.

Answer: C

Explanation:

To improve the performance of a Qlik Sense app, especially with large datasets, it’s important to reduce the complexity of expressions and calculations that need to be performed on the data. In this case, the current master measure expression is performing multiple string manipulations with the Left(), Trim(), and Right() functions. These operations can be computationally expensive when applied across millions of rows of data, which is likely contributing to the slow performance.

Let’s analyze each option:

• A. Ask the developer of the underlying database to change the structure of the field TransactionName.
  While modifying the underlying database structure could improve performance in some cases, it may not be the most practical or efficient solution here. Requiring changes to the database is time-consuming and may not resolve the specific performance bottleneck in the app itself, especially when the current approach of manipulating the field in Qlik Sense might be the source of the performance issue. This solution is likely unnecessary.

• B. - In the Data manager, use the Split function to split the field values with the underscore character as the separator.

• In the Data manager, use the Add calculated field function to multiply the 1st and the 3rd column of the split field.

• Reload the data.**
  This approach involves splitting the TransactionName field into multiple columns based on an underscore separator and then performing calculations on specific parts of the split field. While splitting the field could simplify the expression, this approach may still result in unnecessary columns and additional calculations that may not be the most efficient way to handle the data transformation. This solution could add more overhead instead of simplifying the calculations, and therefore, it’s not the most optimal solution for performance improvement.

• C. Change the master measure definition as follows:
  subfield( TransactionName, '', 1) * subfield( TransactionName, '', 3)
  In the Data manager, use the Replace function to remove the middle part of the field TransactionName.
  This is the best approach. The subfield() function is more efficient than using Left(), Trim(), and Right() when dealing with delimited strings. By directly using subfield() to extract specific parts of the TransactionName (the first and third sections, based on the assumption that underscores are the delimiter), the complexity of the expression is reduced. Additionally, using the Replace() function to eliminate unnecessary parts of the field (in this case, the middle portion) can reduce the amount of data and processing required. This approach optimizes the measure calculation by using more efficient Qlik Sense functions.

Option C offers the most efficient solution by utilizing the subfield() function, which is more optimized for string manipulation in Qlik Sense, reducing the complexity of the expression and improving app performance.

Thus, the correct answer is C.

Question No 9:

A clothing manufacturer has operations throughout Europe and needs to manage access to the data. There is data for the following countries under the field SACOUNTRY -> France, Spain, United Kingdom and Germany. The application has been designed with Section Access to manage the data displayed. 

What is the expected outcome of this Section Access table?

A. USER1 sees data for France and Spain, USER2 sees data for the UK. ADMIN sees data for France, Spain, Germany and United Kingdom
B. USER1 does not see data for France and Spain. USER2 does not see data for the United Kingdom. ADMIN sees data for all countries.
C. USER1 does not see data for France and Spain, USER2 does not see data for United Kingdom. ADMIN can not open the application
D. USER1 sees data for France and Spain, USER2 sees data for the UK. ADMIN sees data for France, Spain and United Kingdom

Answer: A

Explanation:

The Section Access feature in QlikView and Qlik Sense allows for controlling access to specific data based on the user's identity. In this case, the Section Access table governs which data the users are allowed to access, based on the SACOUNTRY field and the access rights provided for each user.

• USER1: This user is granted access to France and Spain, based on the data entries in the Section Access table for these two countries.

• USER2: This user has access to the United Kingdom (UK) data, as specified in the Section Access table.

• ADMIN: The ADMIN user is typically granted full access to all data across the specified countries, meaning they can see the data for France, Spain, Germany, and the United Kingdom.

Thus, based on the description of the Section Access setup, the expected outcome is that:

• USER1 sees data for France and Spain.

• USER2 sees data for the United Kingdom.

• ADMIN has access to all the countries listed, which includes France, Spain, Germany, and the United Kingdom.

This corresponds to Option A, where:

• USER1 sees data for France and Spain.

• USER2 sees data for the UK.

• ADMIN sees data for all four countries (France, Spain, Germany, and United Kingdom).

Therefore, the correct answer is A.