Qlik QSBA2024 Exam Dumps

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.

In Qlik Sense, when dealing with inconsistent naming conventions across different systems or divisions (like the variation in country names), the best practice is to standardize the data during the loading process. Using a lookup table is the most efficient approach to achieve this. This involves loading a separate table that contains all variations of a country name along with the standardized version. During the load process, Qlik Sense can then map the varying names to a common value.

Key Concepts:

Lookup Table: A lookup table contains key-value pairs where different versions of a data element (like country names) are mapped to a single standard value. In this case, the lookup table could have entries like USA, U.S.A., US all mapped to United States of America.

Data Standardization: This is crucial in ensuring consistent analysis across datasets. By converting variations of country names into a single consistent value, the business analyst ensures that all data visualizations and analysis will treat 'USA', 'US', etc., as the same entity.

Why the Other Options Are Less Suitable:

A . Create a calculated master dimension expression: While this could theoretically work by creating a calculated expression to handle variations, it's not scalable or maintainable, especially as new variations in country names could appear in future data loads.

C . Cleanse the source text file prior to loading: This option would require modifying the raw data files manually, which is time-consuming and not sustainable if data is frequently updated or if the number of variations is extensive.

D . Use the Replace option in Data manager: The Replace option in the Data Manager could work on a small scale, but it requires manual intervention each time, which is not efficient or sustainable when new data is loaded. Also, it's more useful for one-off corrections than for handling systemic issues across multiple data loads.

References for Qlik Sense Business Analyst:

Data Modeling Best Practices: Lookup tables are a common approach to resolve issues of inconsistent data across multiple sources. They ensure that data is consistently represented in visualizations and reduce the need for manual intervention.

Data Cleansing During Loading: Qlik Sense allows for transformation and data cleansing during the data load process. A lookup table is part of this capability and ensures that the data loaded into the app is clean and consistent.

Using a lookup table is the most scalable and maintainable approach to standardizing country names in this scenario, which is why option B is the verified solution.

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.

For this scenario, using a bar chart, scatter plot, and histogram provides the best coverage of the requirements. The bar chart is ideal for comparing the sales performance of the top 10 sales representatives. The scatter plot is used to analyze the relationship between margin percentage and total sales by product category. The histogram is excellent for visualizing the distribution of sales amounts.

Key Concepts:

Bar Chart: Perfect for comparing categorical data, such as sales amounts across different sales representatives.

Scatter Plot: Ideal for exploring relationships between two variables, such as margin percentage and total sales.

Histogram: Provides a clear visualization of the distribution of a continuous variable, such as sales amounts.

Why the Other Options Are Less Suitable:

B . Treemap, Container, and Distribution plot: A treemap is less effective for comparing individual sales reps, and a container does not provide a clear visualization on its own.

C . Bar chart, Line chart, and Scatter plot: A line chart is not needed in this case, as it is best for showing trends over time, which is not required here.

D . Treemap, Box plot, and Histogram: A box plot is more suited for showing statistical distributions (e.g., quartiles), which is unnecessary in this case.

References for Qlik Sense Business Analyst:

Data Exploration: Bar charts, scatter plots, and histograms are among the most commonly recommended visualizations for comparing performance, analyzing relationships, and viewing distributions in Qlik Sense.

Thus, the combination of a bar chart, scatter plot, and histogram offers the most comprehensive solution, making A the correct answer.

To recreate the dashboard image provided by the HR Director, the following charts are needed:

Map chart: To show the geographical representation of office capacity.

Org chart: To show the company employee structure.

Line chart: To show company retention over time.

Bar chart: To show average employee salary by region.

Key Concepts:

Map Chart: Used to visualize geographical data, such as office capacity across different locations.

Org Chart: Ideal for displaying hierarchical structures, such as the employee structure of a company.

Line Chart: Best suited for showing trends over time, such as employee retention.

Bar Chart: A good choice for comparing salaries across regions.

Why the Other Options Are Less Suitable:

A . Sankey chart: This chart is used for flow or process analysis, not employee structure.

B . Network chart: Network charts show relationships but are not ideal for hierarchical structures like an org chart.

C . Grid chart and KPI chart: These charts are not well-suited for the types of data required in this scenario.

References for Qlik Sense Business Analyst:

Chart Selection for HR Dashboards: Qlik Sense provides various visualization options, and selecting the correct chart for each type of data is essential for accurate and clear representation.

Thus, the correct combination of charts is D---Map chart, org chart, line chart, and bar chart---making it the verified answer.