IIBA CBDA Practice Test - Questions Answers, Page 14

Expert judgement and experience are valuable sources of knowledge and insight for business data analytics, but they can also introduce bias in the decision making process. Bias is a tendency to favor or reject a certain perspective, outcome, or solution based on personal or subjective preferences, beliefs, or expectations. Bias can affect the quality, validity, and reliability of the data analysis and the resulting decisions. Some examples of bias that can affect expert judgement and experience are confirmation bias, availability bias, anchoring bias, and overconfidence bias. To avoid or minimize bias, business data analysts should apply critical thinking, data literacy, and ethical principles throughout the data analysis process. They should also seek diverse perspectives, challenge assumptions, validate findings, and communicate uncertainties and limitations.

Explanation: Linear bias is a type of cognitive bias that assumes a linear relationship between two variables, when in fact the relationship may be more complex or nonlinear. In this case, the analytics team assumed that the higher the discount, the higher the sales and profit, without considering other factors that may affect customer behavior, such as price elasticity, perceived quality, or competition. By changing their recommendation, the team can avoid making a suboptimal decision that may result in lower profit or customer satisfaction.

Explanation: A Treemap chart is a type of chart that displays hierarchical data as a set of nested rectangles, where the size and color of each rectangle represent a quantitative value and a categorical variable, respectively1.A Sunburst chart is a type of chart that displays hierarchical data as a set of concentric circles, where the size and color of each slice represent a quantitative value and a categorical variable, respectively2. Both charts are useful for visualizing hierarchical data structures, but they have different advantages and disadvantages.One advantage of using a Treemap chart over a Sunburst chart is that a Treemap chart is optimized to include more data points, as it uses a Cartesian coordinate system that fills the entire rectangular space of the chart area, whereas a Sunburst chart uses a polar coordinate system that leaves empty spaces in the corners of the chart area3. This means that a Treemap chart can display more levels of hierarchy, more categories, and more details than a Sunburst chart, without compromising readability or clarity.Therefore, the correct answer is B, as a Treemap chart is optimized to include more data than a Sunburst chart.

Explanation: Normalization is a technique that rescales the values of the variables in a data set to a common range, such as [0,1] or [-1,1]. Normalization can help reduce the effect of outliers, improve the performance of some algorithms, and make the interpretation of the regression coefficients easier and more consistent. Normalization can be done using different methods, such as min-max scaling, z-score scaling, or unit vector scaling.

Explanation: According to the IIBA Guide to Business Data Analytics, communication is a key skill for analysts, as it involves conveying the results, methods, and limitations of the data analysis to various stakeholders in a clear, concise, and meaningful way. To communicate effectively, analysts need to understand the communication needs of stakeholders, such as their level of interest, knowledge, and influence, their preferred format and frequency of communication, and their expectations and objectives. By understanding the communication needs of stakeholders, analysts can tailor their messages, choose the appropriate language and tone, and select the most suitable communication channels and medi a. Therefore, the correct answer is B, as understanding the communication needs of stakeholders is a recommended practice for analysts while performing the steps of drawing out the results, explaining the methods and processes used, and identifying any limitations or weaknesses in the data or methods applied.

Explanation: In the context of Business Data Analytics (IIBA- CBDA), zero covariance between two variables indicates that there is no linear relationship between those variables. When the covariance is zero, it means the variables are independent of each other. In the provided options, graph 4 shows a random scatter of data points without any apparent trend or pattern, indicating zero covariance.

Explanation: According to the Guide to Business Data Analytics, data security is the protection of data from unauthorized access, use, modification, or destruction. Data security includes the policies, procedures, and technologies that ensure the confidentiality, integrity, and availability of dat a. Data security is an important aspect of data management, which is the planning, execution, and oversight of the data lifecycle. Data security is also related to data governance, which is the establishment and enforcement of rules, roles, and responsibilities for data quality, access, and usage. Data security is not the same as content management, which is the creation, storage, distribution, and maintenance of digital content. One of the ways to enhance data security is to provision access to executive dashboards and visualizations based on the roles and permissions of the users. This can help prevent unauthorized or inappropriate access to sensitive or confidential data, as well as ensure compliance with data privacy and ethical standards. By establishing standards and implementing role based logins to executive dashboards, the organization can address the data security needs of its analytics practices.

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.

Explanation: According to the Guide to Business Data Analytics, prescriptive analytics is a type of analytics that provides recommendations or suggestions for optimal actions or decisions based on data analysis. Prescriptive analytics uses techniques such as optimization, simulation, and decision analysis to evaluate various scenarios and trade-offs and to determine the best course of action for a given objective and constraint. Prescriptive analytics can help organizations achieve their goals, improve their performance, and increase their efficiency and effectiveness. In this situation, Freedom Insurance wants to determine how to optimally price a new type of insurance policy that would produce the maximum profit in the coming year. This is a prescriptive analytics problem, as it involves finding the optimal solution for a complex and uncertain decision problem.