QSBA2024 Qlik Sense Business Analyst Certification Exam-2024 Questions and Answers

The app is experiencing performance issues due to inefficient calculations in a master measure that processes the field TransactionName, which has a complex structure (e.g., "1_ABCDEFGHI_23454"). Let’s analyze the available options and why Option B is the best solution.

A. Ask the developer of the underlying database to change the structure of the field TransactionName. While modifying the data structure in the underlying database might improve performance, this approach is not ideal. It’s a time-consuming process that might not be feasible, especially when working with large datasets that have already been integrated into the Qlik Sense app. The performance improvement should focus on optimizing the Qlik app itself.

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 is the most efficient approach. By using the Split function in the Data Manager to break down the TransactionName field based on the underscore separator, the data becomes more accessible for calculations. You can then create a calculated field that multiplies the first and third components of the split data (corresponding to the 1st part and the numeric identifier at the end). This reduces the need for complex string manipulation functions (e.g., Left, Right, Trim) within the master measure, which can be resource-intensive when applied to large datasets.

C. Change the master measure definition as follows: subfield( TransactionName, '',!) * subfield( TransactionName, '', 3) This option suggests using the subfield() function to split the string within the master measure itself. While this approach is valid, it doesn’t provide as significant a performance improvement compared to pre-processing the data in the Data Manager . Calculating fields directly within the visualizations is more computationally expensive compared to handling it during the data load phase.

D. In the Data manager, use the Replace function to remove the middle part of the field TransactionName. The Replace function would remove the middle section of the transaction name, but it doesn’t address the need to split the field for efficient multiplication. It would also result in a loss of important data that may be required for other analyses.

Key Qlik Sense Business Analyst References:

The Data Manager provides powerful tools for transforming and optimizing data before it is used in visualizations. Pre-processing the data using functions like Split significantly reduces the load on front-end visualizations.

Splitting fields during the data load rather than in the master measures improves performance, especially in large datasets where string manipulation functions in visualizations can degrade performance.

Calculated fields allow analysts to create new expressions based on transformed data, ensuring that the app remains efficient while meeting analytical needs.

Thus, the correct solution is to use the Split function to separate the field values and then use a calculated field to multiply the required components , which enhances app performance.

Storytelling in Qlik Sense allows business users to create dynamic presentations based on data insights. This feature is ideal for executives like the VP of Finance who need to share financial insights in meetings. Storytelling allows users to create guided stories from data visualizations, offering a customizable solution that can be tailored to the monthly meeting agendas.

Key Concepts:

Storytelling : This feature enables users to create data-driven stories with snapshots from Qlik Sense visualizations, allowing for dynamic, customized presentations that can be updated as data changes.

Customizable : The VP of Finance can customize the presentation each month to focus on relevant financial metrics and insights.

Why the Other Options Are Less Suitable:

B. Insight Advisor Chat : While helpful for querying data interactively, this option is not suited for presenting data in a structured, presentable format to executives.

C. Action Buttons : Action buttons are used for navigating or interacting within apps, but they are not relevant for creating presentations.

D. Bookmarks : Bookmarks save specific selections, but they don’t provide the dynamic, presentable format needed for meetings.

References for Qlik Sense Business Analyst:

Storytelling in Qlik Sense : This feature is often recommended for creating interactive, data-driven presentations, especially for executive-level meetings.

Thus, Storytelling offers the most effective solution for presenting financial data in a customizable format, making A the correct answer.

To meet the CEO's requirements for a dynamic, interactive, and visually impactful presentation that can also be shared, the Storytelling feature in Qlik Sense is the best option. The Storytelling feature allows the business analyst to create a narrative combining static snapshots and live Qlik Sense sheets. This is perfect for large presentations as it lets the CEO present critical information and answer audience questions dynamically by embedding live sheets into the story, making the presentation interactive.

Key Concepts:

Storytelling : This feature allows the creation of a sequence of slides that include embedded live Qlik Sense visualizations, enabling dynamic responses to audience questions.

Shared Content : Once the app is published, it can be shared with a broader audience, allowing stakeholders to interact with the embedded sheets.

Why the Other Options Are Less Suitable:

A. Embed a video player : While videos can be useful, they do not allow for dynamic interaction, which is a requirement.

B. Colorful and layered visualizations exported as PDF : PDFs are static, and exporting as a PDF would not allow for dynamic interaction during the presentation.

C. NPrinting slideshow-style report : NPrinting is useful for generating reports, but it does not meet the need for live, dynamic presentations.

References for Qlik Sense Business Analyst:

Qlik Sense Storytelling for Presentations : Storytelling allows users to blend narrative and interactive elements, making it ideal for presenting to large audiences.

Thus, D is the best choice because it meets all the requirements for interactive and dynamic presentations, making it the verified answer.

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.

For analyzing the relationship between Sales and Margin, a scatter plot is ideal, as it allows you to visualize the relationship between two measures (Sales and Margin) across various dimensions such as Customer or Product Category. The funnel chart is perfect for visualizing stages in a sales process, as it shows how sales progress from the initial stage to the final (Won) stage, with the width of each segment representing the total sales for each stage.

Key Concepts:

Scatter Plot : This type of chart is specifically designed to visualize the correlation or relationship between two measures, making it ideal for analyzing Sales versus Margin across different dimensions.

Funnel Chart : This chart is particularly suited for visualizing the sales stages, as it visually demonstrates the proportion of sales moving through each stage of the sales funnel.

Why the Other Options Are Less Suitable:

A. Scatter plot and Bar chart : While a scatter plot is correct for analyzing Sales and Margin, a bar chart won’t adequately represent the different stages of the sales process as effectively as a funnel chart.

C. Combo chart and Pie chart : A combo chart could potentially work, but it would not show the relationship between Sales and Margin as clearly as a scatter plot. A pie chart is also less effective for representing stages in a sales funnel.

D. Distribution plot and Bar chart : A distribution plot does not effectively show the relationship between two measures, and a bar chart isn’t the best choice for visualizing the stages of a sales process.

References for Qlik Sense Business Analyst:

Scatter Plot for Relationships : This chart type is highly recommended when exploring relationships between two continuous variables, such as Sales and Margin.

Funnel Charts : These are ideal for visualizing how data moves through various stages of a process, such as sales stages, from initial engagement to final sale.

Therefore, the combination of a scatter plot and a funnel chart provides the best solution, making B the correct answer.

When working with large datasets, such as the one containing over a billion rows in this scenario, loading the entire dataset can be time-consuming, especially during the development phase. Qlik Sense provides a Filter data option in the Data Manager, which allows business analysts to work with a subset of the data during development. This is particularly useful for rapidly prototyping the application design.

Key Concepts:

Filter Data Option : This feature in Qlik Sense allows the analyst to load a smaller sample of the dataset for analysis and development purposes. By filtering out unnecessary rows, the business analyst can quickly build and prototype the application without waiting for the full dataset to load. Once the design is finalized, the full dataset can be reloaded.

Prototyping with Reduced Data : It’s often more efficient to work with a smaller dataset during the design phase. This allows for faster iterations and design cycles, especially when working with large datasets.

Why the Other Options Are Less Suitable:

A. Split the dataset and create a normalized star schema of associated tables : This would involve complex data modeling that is not necessarily related to the immediate need of reducing the size of the dataset for prototyping. While star schemas can optimize data models, it’s not the quickest way to reduce the number of rows for initial testing.

B. Deselect text columns with unique data values to reduce the memory footprint : This may reduce the memory usage but won't necessarily address the issue of reducing the number of rows. Also, the text columns might be important for the analysis and should not be removed without careful consideration.

D. Truncate text fields longer than 256 characters to create preview fields : Truncating text fields will not significantly reduce the dataset size or the load time. It may also result in losing critical information, which is not ideal for prototyping.

References for Qlik Sense Business Analyst:

Rapid Prototyping : Qlik Sense encourages rapid development of applications by allowing business analysts to work with subsets of the data. The Filter Data option is an important tool for managing large datasets efficiently.

Data Manager Tools : The Data Manager in Qlik Sense provides several tools for reducing the dataset size, and filtering is one of the key options for improving performance during development.

Using the Filter data option allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes option C the most effective solution.

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.

To dynamically highlight a specific product bar in a bar chart based on conditions, the best approach is to use the Color By Expression feature under the Appearance settings in Qlik Sense. This feature allows you to apply conditional formatting to bars, changing their color dynamically based on expression logic.

A. A Color By Expression property under Appearance This is the correct answer. The Color By Expression property allows the business analyst to dynamically color bars in the chart based on specific conditions. The expression can be set to highlight a specific product bar only when certain conditions are met, and the color can be customized to make it stand out.

B. A Dimension reference line add-on Dimension reference lines are used to show thresholds or important values along the axes, but they do not dynamically color the bars or add conditional labels to them. This would not achieve the desired effect.

C. An Alternative dimension with a calculation Alternative dimensions allow users to switch between different dimensions in the same chart, but they do not provide dynamic highlighting or conditional visibility for labels.

D. A reference line add-on under Properties Reference lines are used to mark specific values or thresholds in a chart, but they do not interact with the dynamic coloring or visibility of labels on individual bars.

Key Qlik Sense Business Analyst References:

Color By Expression is a powerful feature in Qlik Sense that allows dynamic customization of chart colors based on expressions, making it ideal for highlighting specific data points or conditions.

This feature provides great flexibility in creating visually engaging and interactive charts that respond to changes in the underlying data or user selections.

Thus, the correct way to highlight a specific product bar with a dynamic label is to use Color By Expression under Appearance .

In the provided exhibit, the movie analyst filters by the Length Range field and notices a hyphen (“-”), which typically indicates missing data in Qlik Sense. When the analyst selects this hyphen, it filters out the rows where no data exists for the Length Range field.

Let’s review the options:

A. Six movies in the source data contain illegal characters for the Length Range field. This statement is incorrect. The hyphen (“-”) is not an indicator of illegal characters. In Qlik Sense, it usually represents null values or missing data , not improperly formatted data.

B. Movies at the start of the 20th century often varied in length. This answer is not relevant to the specific observation of the hyphen in the Length Range field. The hyphen does not provide insights into the variation of movie lengths; it indicates missing data.

C. All movies from the 1920s or 1930s contain no data for Length Range. While the screenshot shows some movies from different decades, we cannot definitively conclude that all movies from a particular era are missing the Length Range data. The hyphen indicates that some movies across various years are missing data, but this does not apply to all movies from the 1920s or 1930s.

D. The source data for six movies is missing a Length Range. This is the correct interpretation. The hyphen in the Length Range column indicates that these six movies lack data for this field. Selecting the hyphen allows the analyst to identify movies with missing values in the Length Range field.

Key Qlik Sense Business Analyst References:

Hyphen (“-”) in Qlik Sense typically represents null or missing values in a field.

Filter panes in Qlik Sense allow users to quickly see where data may be incomplete or missing, and selecting these null values provides immediate insight into which records are affected.

Thus, the correct determination is that the source data for six movies is missing a Length Range .

To answer the two questions:

How many students are in the system?

What percentage of students have not yet attempted an exam?

The analyst needs to focus on the StudentID field, specifically in relation to the Attempts table. This is because the Attempts table captures all exam attempts made by students, and we can deduce which students have and have not made an attempt by examining the presence of StudentID values in this table.

Key Concepts:

Total Distinct Values : This provides the total number of unique students who have attempted exams. It helps identify how many students have made at least one attempt.

Subset Ratio : This compares the values of StudentID between the Students table and the Attempts table. The subset ratio shows how many students in the Students table are represented in the Attempts table. This ratio helps determine the percentage of students who have not yet attempted any exams.

Why the Other Options Are Less Suitable:

B. Non-null values and Subset ratio for the StudentID field in the Students table : The non-null values in the Students table are not relevant to the question about exam attempts. The focus should be on whether the StudentID is present in the Attempts table.

C. Subset ratio and Present distinct values for the ExamID field in the Attempts table : This focuses on exams, not students. The question specifically relates to how many students have attempted exams.

D. Present distinct values and Density% for the ExamID field in the Exams table : This focuses on the number of exams and their density, which does not help in determining how many students have attempted or not attempted an exam.

References for Qlik Sense Business Analyst:

Subset Ratio and Distinct Counts : Qlik Sense’s data model viewer provides valuable metadata like the distinct count of a field and its subset ratio when compared to related fields in other tables. This is particularly useful for understanding relationships and gaps in the data, such as identifying students who have not yet made an exam attempt.

By focusing on the Total distinct values and Subset ratio for the StudentID field in the Attempts table, the business analyst can easily determine the total number of students and the percentage who have not yet attempted an exam, making A the verified answer.

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.

The Multi KPI object is designed to display multiple key measures in a compact and visually appealing format, making it ideal for a dashboard that requires a minimal number of visualizations but must present multiple key metrics. By using master visualizations , the business analyst ensures consistency and reusability across different sheets. This setup also allows for easy navigation through the app, as users can drill down into more detailed sheets when needed.

Key Concepts:

Multi KPI : This visualization allows multiple key performance indicators (KPIs) to be displayed in a single object, reducing the need for multiple charts or visualizations while ensuring that critical metrics are visible.

Master Visualizations : These ensure that KPIs are consistent across the app and can be reused as needed, helping to maintain a clean and efficient dashboard.

Why the Other Options Are Less Suitable:

B. Dynamic text and image linked objects : This could work for navigation, but it wouldn’t meet the requirement of displaying multiple key measures in a minimal number of visualizations.

C. Bookmark : A bookmark is used to save a particular state of selections but isn’t useful for showing multiple KPIs simultaneously.

D. Container : While containers can hold multiple visualizations, the Multi KPI object is more suited to displaying multiple key measures in a compact space.

References for Qlik Sense Business Analyst:

Multi KPI for Compact Dashboards : Qlik Sense recommends using Multi KPI objects when there is a need to display multiple key metrics in a minimalistic format.

Thus, A is the best choice to meet the CEO's requirements, making it the correct answer.