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Qlik Sense Business Analyst Certification Exam - 2024 Sample Questions (Q42-Q47):
NEW QUESTION # 42
A business analyst receives multiple requests for a variety of different filter panes to be placed on a dashboard. Users need to filter on many different values across different fields.
Which Qlik Sense feature do the users need to learn about to meet their needs?
- A. Smart search
- B. Insight Advisor
- C. Data model viewer
- D. Governed self-service
Answer: A
Explanation:
When users need to filter across many different fields and values in a Qlik Sense dashboard, the most efficient feature they can use is Smart Search. Smart Search allows users to quickly search across all fields within the data model, enabling them to find relevant information and apply filters in a streamlined manner.
A . Smart search
This is the correct option. Smart Search enables users to enter search terms and find matches across all fields in the data model, allowing for quick and intuitive filtering. It helps users locate specific data points or filter across multiple fields at once, making it highly efficient when multiple filter panes are needed.
B . Data model viewer
The Data Model Viewer provides a visual representation of the relationships between data tables in the model. While it's useful for understanding the data structure, it's not a tool for filtering or user interaction with data.
C . Insight Advisor
The Insight Advisor is designed for guided analytics, providing suggestions and generating visualizations based on user queries. It does not offer the comprehensive filtering capabilities that Smart Search does.
D . Governed self-service
Governed self-service refers to the balance between providing users with flexibility in creating their own visualizations while maintaining control over data governance. It's not related to filtering or searching data in the same way as Smart Search.
Key Qlik Sense Business Analyst References:
Smart Search in Qlik Sense is designed to provide fast, interactive search capabilities that span across all fields, enabling complex filtering in an easy-to-use interface.
This feature allows users to filter multiple fields simultaneously, saving time and effort when analyzing diverse data sets.
Thus, the correct feature for filtering on multiple values across different fields is Smart Search.
NEW QUESTION # 43
A banking organization needs a dashboard that shows the following:
* A KPI indicator for current year (CY) versus last year (LY) revenue
* A scatter plot that shows the cost versus revenue of product categories
* A treemap that shows average customer satisfaction rating based on regions
* A table that shows CY cost by record number, financial instrument, and bank In addition to the customer satisfaction rating, which fields should be used to create the required measures?
- A. CY Revenue, LY Revenue, Cost
- B. CY Revenue, LY Revenue, CY Cost
- C. Revenue and CY Cost
- D. Revenue and Cost
Answer: B
Explanation:
The banking dashboard requires several KPIs and visualizations based on current year (CY) and last year (LY) revenue, as well as cost and customer satisfaction. To build the required measures, the fields CY Revenue, LY Revenue, and CY Cost are necessary because they provide the comparison between the current and previous year, as well as cost-related data for the specific calculations.
Key Concepts:
KPI for CY vs LY Revenue: This requires both CY Revenue and LY Revenue fields.
Scatter Plot for Cost vs Revenue: This comparison requires both CY Revenue and CY Cost to create the necessary relationship in the scatter plot.
Treemap for Customer Satisfaction: In addition to the customer satisfaction rating, the field CY Revenue would likely be used for additional insights by region.
Table with CY Cost: The table requires CY Cost data along with other relevant fields like record number, financial instrument, and bank.
Why the Other Options Are Less Suitable:
B . CY Revenue, LY Revenue, Cost: While this covers the revenue fields, it does not specifically include CY Cost, which is needed for the table and scatter plot.
C . Revenue and Cost: This option is too general and does not distinguish between current year and last year revenue, which is essential for the KPI.
D . Revenue and CY Cost: This does not include LY Revenue, which is required for the KPI.
References for Qlik Sense Business Analyst:
Comparison Measures in Qlik Sense: In Qlik Sense, comparisons across time periods like CY and LY require specific fields to calculate accurate insights, and it's essential to ensure the appropriate fields are included for each visualization.
Thus, A provides the correct set of fields for the required dashboard, making it the verified answer.
NEW QUESTION # 44
The business analyst creates one table by concatenating and joining several source tables. This has resulted in a table of several thousand rows that may have several columns containing between 30% and 70% null values. The business analyst needs to understand the level of null values in each field of this table to determine if this is an issue.
Which capability should the business analyst use?
- A. Select each field in the Data model viewer and use the Density value to determine the level of nulls
- B. Look at the tags fields for any indication that $null is associated to this field
- C. Enable the Preview Panel in the Data model viewer and inspect the data table visually to determine the level of null values
- D. Inspect each field in the Data model viewer and use the Subset ratio to determine the level of null values
Answer: A
Explanation:
The Density value in the Data Model Viewer provides a measure of how "dense" or "sparse" a field is in terms of data completeness. A higher density value means fewer nulls, while a lower value indicates more nulls. By checking the density value for each field, the business analyst can determine the percentage of non-null values, which is critical for understanding data quality and completeness.
Key Concepts:
Density Value: This is a measure in Qlik Sense that indicates the proportion of non-null values in a field. A field with a high density is mostly populated, while a lower density indicates a high proportion of null values.
Data Model Viewer: This tool allows analysts to inspect the structure and quality of data fields, including metrics such as density.
Why the Other Options Are Less Suitable:
B . Preview Panel: While the Preview Panel shows sample data, it does not provide a comprehensive measure of null values and is more suited for a quick glance rather than detailed analysis.
C . Tags fields with $null: This would show if the field contains any nulls, but it wouldn't quantify the level of nulls.
D . Subset Ratio: The subset ratio compares values across related tables, not null values within individual fields.
References for Qlik Sense Business Analyst:
Data Quality in Qlik Sense: Using the Density value is the best way to assess the proportion of null values in a field, making it ideal for the business analyst to understand the completeness of the data.
Thus, A is the correct answer because the density value provides the required insight into the level of nulls in each field.
NEW QUESTION # 45
A business analyst has access to all of a company's data for the past 10 years. The source table consists of the following fields: TransactionID, TransactionTime, Transaction Date, Transaction Year, Cardholder, Cardholder address, Cardissuer, and Amount.
Users request to create an app based on this source with the following requirements:
* Users only review the data for the past 2 years
* Data must be updated daily
* Users should not view cardholder info
Which steps should the business analyst complete to improve the app performance?
- A. * Deselect Cardholder and time fields in Data manager
* Apply a filter to extract data based on transaction year
* Request a daily reload task from the system admin - B. * Deselect Cardholder and time fields in Data manager
* Use a filter pane based on auto-calendar fields
* Perform a daily reload via the Data manager - C. * Delete Cardholder and time fields in Data manager
* Use set analysis to show data based on transaction year
* Use an API to perform a daily reload task - D. * Delete Cardholder and time fields in Data manager
* Use a bookmark based on auto-calendar fields
* Use the reload function in the sheet Editor asset panel
Answer: A
Explanation:
The business analyst needs to optimize the app for performance and ensure that users only see data from the past two years, without cardholder information, and that the data is updated daily. By deselecting the Cardholder and time fields in the Data Manager, the analyst ensures that sensitive information is not loaded. Applying a filter to extract data based on transaction year ensures that only relevant data (the last two years) is included in the app, improving performance. Lastly, requesting a daily reload task from the system administrator ensures that the app stays up to date.
Key Concepts:
Deselecting Fields: This removes unnecessary fields, such as Cardholder information, from the data model, which improves performance and ensures privacy.
Filtering Data: Applying a filter to limit data to the last two years reduces the dataset size and improves app responsiveness.
Daily Reload Task: Requesting a daily reload ensures that the app's data stays current, meeting the requirement for daily updates.
Why the Other Options Are Less Suitable:
A . Delete Cardholder and time fields, use bookmark: A bookmark is not an efficient solution for filtering by transaction year.
B . Set analysis and API reload: Set analysis works within the app but does not optimize the data load itself. Using an API for reload tasks is unnecessarily complex.
C . Use filter pane and auto-calendar: While auto-calendar fields can be useful, they don't optimize the data loading process for performance.
References for Qlik Sense Business Analyst:
Efficient Data Loading: Qlik Sense recommends filtering data at the load stage to improve performance, especially when dealing with large datasets.
Thus, D is the correct solution, making it the verified answer.
NEW QUESTION # 46
Refer to the exhibit.
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 RodentlD AS RodentlD Counter in the Rodent table.
* Use Count (Distinct RodentID Counter) as the KPI expression. - B. * Create 1 AS RodentID_Counter in the Rodent table.
* Use Sum (RodentlD Counter) as the KPI expression. - C. Create 1 AS RodentID_Counter in the MazeEscapes table.
Use Sum (RodentlD Counter) as the KPI expression. - D. * Create RodentlD AS RodentID_Counter in the MazeEscapes table.
* Use Count (Distinct RodentlD Counter) as the KPI expression.
Answer: D
Explanation:
In this case, we need to count how many unique rodents have made at least one attempt at any maze. Since the relationship between the Rodent table and the MazeEscapes table is a one-to-many relationship (each rodent may attempt several mazes), the best approach is to base the calculation on the MazeEscapes table, as it holds the actual records of rodent attempts.
By creating a new field RodentID_Counter in the MazeEscapes table, the analyst ensures that each rodent's attempt is captured. Using the Count (Distinct RodentID_Counter) as the KPI expression then accurately counts each unique rodent that has made at least one attempt, without counting multiple attempts by the same rodent.
Key Concepts:
Distinct Count: The distinct count ensures that rodents who have made multiple maze attempts are only counted once.
MazeEscapes Table: This table holds the records of each attempt, making it the correct source for this KPI.
Why the Other Options Are Less Suitable:
B . Sum (RodentID_Counter) in the Rodent table: This would incorrectly sum the number of rodents rather than counting unique attempts, leading to inaccurate results.
C . Sum (RodentID_Counter) in the MazeEscapes table: Summing the counter would not account for the distinct rodents, leading to incorrect counting.
D . RodentID_Counter in the Rodent table: This would not correctly reflect the rodents that have made an attempt, as not every rodent in the Rodent table may have attempted a maze.
References for Qlik Sense Business Analyst:
Distinct Count in KPI: It is best practice to use Count(Distinct ...) when working with KPIs that require unique counts, such as counting distinct rodents in this scenario.
Thus, A is the verified answer because it ensures that each rodent is counted once, based on their attempts in the MazeEscapes table.
NEW QUESTION # 47
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