IIBA Certification in Business Data Analytics (IIBA - CBDA) CBDA Exam Dumps: Updated Questions & Answers (October 2025)

According to the Introduction to Business Data Analytics: A Practitioner View, the breakeven point is the point at which the total revenue equals the total cost of a product or service. The breakeven point indicates the minimum sales volume or price required to cover the fixed and variable costs and to start making a profit. The breakeven point can help the management team make a decision on pricing by showing them how sensitive the profitability is to the price changes and how much margin of safety they have. The breakeven point can also help the management team evaluate the feasibility and risk of the pricing proposal and compare it with alternative scenarios.

References: Introduction to Business Data Analytics: A Practitioner View, page 18; CBDA Exam Blueprint, page 7; [Break-Even Point (BEP) Definition - Investopedia]

Agreeing on the business problem is the first and most critical step in any analytics project, as it defines the scope, purpose, and objectives of the analysis, and aligns the expectations and interests of the stakeholders1. Agreeing on the business problem involves identifying the problem statement, the problem owner, the problem context, the problem impact, and the problem criteria2. If the team did not agree on the business problem, the solution proposed by the team may seem misaligned with the actual needs, preferences, or assumptions of the decision makers, and may not address the root cause or the main drivers of the problem. In this scenario, the team and the Director of Sales may have different views on what the business problem is, why it is important, and how it should be solved.

The other options are not correct explanations of what has gone wrong. This scenario can be addressed with analytics, as it involves using data to understand customer behavior, identify factors influencing cart abandonment, and recommend improvements to the website or the pricing strategy. The team may or may not have agreed on the root cause of the problem, but that is not the main issue, as the root cause analysis is a part of the data analysis step, not the problem definition step. The team may or may not have performed an insufficient amount of planning, but that is not the main issue, as the planning process is a subsequent step after the problem definition step, and it depends on the clarity and agreement of the business problem.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 252: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 11. : Guide to Business Data Analytics, IIBA, 2020, p. 25. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 11.

According to the Introduction to Business Data Analytics: An Organizational View, one of the critical success factors for creating an enterprise analytics program is to have a management team that thinks analytically and fosters a culture where data science thrives. This means that the management team should understand the potential value and impact of data science, promote a data-driven mindset and decision-making process, encourage innovation and experimentation, and support collaboration and learning among the data science team and other stakeholders. A management team that thinks analytically and fosters a culture where data science thrives can help create a strategic vision, align the goals and objectives, allocate the resources and investments, and overcome the challenges and barriers for the enterprise analytics program.

References: Introduction to Business Data Analytics: An Organizational View, page 8-9; CBDA Exam Blueprint, page 8; Guide to Business Data Analytics, page 85-86.

According to the Guide to Business Data Analytics, a heatmap is a type of visualization technique that uses colours to represent the values of a variable across a two-dimensional space. A heatmap can help reveal patterns, trends, and outliers in the data, as well as show the relative importance or intensity of different areas. In this situation, the analyst has decided to communicate the information about the website usage by displaying the top pages overlaid with colours denoting the volume of clicks. This is a heatmap, as it uses colours to show the distribution and magnitude of clicks across the web pages.

References:Guide to Business Data Analytics, page 61;CBDA Exam Blueprint, page 7;Heat Maps | Trendz Analytics

According to the Guide to Business Data Analytics, one of the key competencies of a business data analyst is to identify the research questions that guide the analytics work1. The research questions should be based on the business problem or opportunity, the stakeholder needs, and the data availability and quality2. By providing the focus area of the study, the Operational Manager has limited the scope of the study with their biased opinion, as they have not considered other possible factors that might affect theoperational efficiencies, such as demand, inventory, quality, labor, or customer satisfaction. The Operational Manager has also not involved other stakeholders who might have different perspectives or interests in the study. This could lead to a narrow or incomplete analysis that might miss some important insights or recommendations. The Operational Manager should instead collaborate with the analyst to define the research questions that are relevant, specific, measurable, achievable, and time-bound3.

The other options are not correct, as they do not address the issue of defining the research questions. The Operational Manager is not necessarily the expert on the operational processes, as they might have a limited or biased view of the situation. The Operational Manager has not limited the scope of the budget by providing a timeline of three weeks, as this is a reasonable time frame for an analytics study, depending on the complexity and availability of the data. The Operational Manager has not helped the analyst stay on track with time and budget by providing the focus areas, as this might actually waste time and resources if the focus areas are not aligned with the actual business problem or opportunity.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 113: Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 12.

Data sampling is the process of selecting a subset of data from a larger population to represent the characteristics of the whole population. Data sampling is often used when the population is too large or costly to collect data from every individual. Data sampling can help reduce the time, cost, and complexity of data analysis, while maintaining the validity and reliability of the results. Data sampling can also help avoid biases and errors that may arise from collecting data from the entire population. Data sampling can be done using various methods, such as random sampling, stratified sampling,cluster sampling, or convenience sampling, depending on the research objectives and the availability of data. In this example, the analytics team develops a survey to send to randomly selected customers, which is a form of data sampling. The survey aims to collect data from a representative sample of customers that can reflect the preferences and opinions of the entire customer population. The survey data can then be used to analyze the performance and demand of different products, and provide recommendations to management. References:

[Business Data Analytics: A Practitioner’s Guide], Chapter 4: Data Analysis, Section 4.2: Data Sampling, pp. 69-72.

[A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide)], Version 3, Chapter 6: Solution Evaluation, Section 6.2: Analyze Performance Measures, pp. 152-153.

Unstructured data is data that does not have a predefined format, structure, or schema, and that cannot be easily stored, processed, or analyzed by traditional databases or tools1. Unstructured data may include text, images, audio, video, or other types of data that are rich in information but complex and diverse in nature2. In the Customerissues entity, the ComplaintNotes attribute would be categorized as unstructured data, as it may contain free-form text that captures the details, sentiments, or emotions of the customers’ complaints, and that may vary in length, language, tone, or style. The ComplaintNotes attribute would require special techniques, such as natural language processing, text mining, or sentiment analysis, to extract meaningful insights from the unstructured data3.

The other attributes in the Customerissues entity would be categorized as structured data, as they have a predefined format, structure, or schema, and that can be easily stored, processed, or analyzed by traditional databases or tools4. Structured data may include numbers, dates, codes, categories, or other types of data that are simple and consistent in nature5. In the Customerissues entity, the CustomerlD, ConcernCategory, ConcernSubCategory, AgentID, and lssueResolved(Y/N) attributes would be categorized as structured data, as they may contain numeric, alphanumeric, or binary values that represent the identifiers, classifications, or statuses of the customers’ issues, and that may have fixed lengths, ranges, or domains.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 412: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 83: Data Analysis: The Definitive Guide, Tableau, 4: Guide to Business Data Analytics, IIBA, 2020, p. 415: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 8. : Data Analysis: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 41. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 8.

According to the Guide to Business Data Analytics, PERT (Program Evaluation and Review Technique) is a type of estimation that uses three values: optimistic, pessimistic, and most likely. The PERT estimate is calculated as the weighted average of these three values, with more weight given to the most likely value. PERT can be used to estimate the duration, cost, or other variables of a project or activity, taking into account the uncertainty and variability of the data. PERT can help provide a realistic and reliable estimate based on the available information.

References: Guide to Business Data Analytics, page 54-55; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 16.

Logistic regression is a technique that can be used to model the probability of a binary outcome, such as choosing to watch or not watch a movie, based on one or more predictor variables, such as the characteristics of the movie and the customer’s viewing history. Logistic regression can help the business analysis professional to identify the factors that influence the customer’s decision and to estimate the likelihood of each customer’s preference. Logistic regression can also be used to test hypotheses and to evaluate the performance of the predictive model. References: [Guide to Business Data Analytics], page 55; [Business Data Analytics: A Practical Guide], page 93; [Introduction to Business Data Analytics: A Practitioner View], page 14.

According to the Guide to Business Data Analytics, a study population is the subset of the population that meets the eligibility criteria for the research question. A study population should be representative of the population of interest and relevant to the business problem or opportunity. In this situation, the survey will establish a poor study population because the students from local colleges and universities within a 50km radius may not reflect the characteristics, preferences, and behaviours of the potential online customers for the fifty-year-old brick and mortar business. The students may have different demographics, income levels, shopping habits, and needs than the target market for the business. Therefore, the survey results may not be generalizable or applicable to the population of interest and may not provide valid and reliable insights for predicting future sales.

References: Guide to Business Data Analytics, page 48-49; CBDA Exam Blueprint, page 7; Population vs. Sample | Definitions, Differences & Examples - Scribbr