IIBA CBDA Certification in Business Data Analytics (IIBA - CBDA) Exam Practice Test

A narrative is a written or spoken explanation of the data analysis results that tells a story with the data, provides additional context and background information, highlights the key insights and findings, and draws correlations and implications for the decision makers12. The report is missing the narrative, meaning that it does not communicate the meaning and value of the data analysis effectively, and it leaves the interpretation and action to the senior management without any guidance or recommendation34. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 672: Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 93: Data Storytelling: The Essential Data Science Skill Everyone Needs, Brent Dykes, 2016, 14: The Power of Data Storytelling, Harvard Business Review, 2018, 2.

The data scientist is encouraging using the Gestalt Principle of Similarity with regards to data visualization. This principle states that the brain groups objects together that are similar in appearance, such as color, shape, size, or orientation. By using color to group objects needing to be associated, the data scientist is suggesting a way to reduce the complexity in the model and make it easier for the viewers to perceive the patterns and relationships among the data12 References: 1: Gestalt Principles For Data Visualization - Topcoder 2: Introduction to Data Visualization: Gestalt Principles

Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an overview of what has happened or is happening, such as the attrition rates and the personality test scores of the employees12. The team has been asked to perform descriptive analytics to explore possible relationships between the data variables, without making any predictions or prescriptions for the future. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making, S. Christian Albright and Wayne L. Winston, 2015, p. 5.

The question “Given that you’re a customer, would you work at our company?” is unclear, because it is a hypothetical and subjective question that does not specify the purpose, scope, or context of the analysis. The question also does not define what constitutes a customer, or how the customer’s experience or satisfaction relates to the employee’s motivation or performance. The question needs to be refined and clarified to make it more focused, relevant, and feasible for the analytics team to answer. For example, the question could be rephrased as “How does the customer satisfaction score affect the employee retention rate in our company?” References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions

•Understanding the Guide to Business Data Analytics, page 10-11

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 16

A logical database model is a data model of a specific problem domain expressed independently of a particular database management product or storage technology. It describes data using notation that corresponds to a data organization used by a database management system, such as relational tables and columns. It also includes rules of normalization, which are the process of converting complex data structures into simple, stable data structures12 References: 1: Logical schema - Wikipedia 2: What Is a Data Model? | Coursera

Acceptance and evaluation criteria are the techniques that the BA professional on the team should recommend they complete, because they are the standards or measures that are used to evaluate the suitability and value of each option. Acceptance and evaluation criteria can help the team compare the benefits, costs, risks, and impacts of each option, and determine which one best meets the needs and expectations of the stakeholders. Acceptance and evaluation criteria can also help the team communicate the rationale and evidence behind their recommendation, and ensure that the recommendation is aligned with the business goals and objectives. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making

•Understanding the Guide to Business Data Analytics, page 9

•Acceptance and Evaluation Criteria | Business Analysis

According to the Business Data Analytics (IIBA®- CBDA) principles, extreme outliers in a dataset can be identified visually on a scatter plot as points that are distinctly separate from the bulk of the data. In this visual, there are three points that are significantly higher on the y-axis (credit card expense) relative to their position on the x-axis (household income), indicating unusual credit card purchases. References: The identification and interpretation of outliers is a standard practice in data analytics and is covered under the Business Data Analytics (IIBA®- CBDA) learning resources.

A pie chart is a circular chart that shows the proportion of each category in a whole by dividing the circle into slices. A pie chart would result in confusion when comparing the number of sales for a specific model of electric car across all 50 US states, because it is difficult to compare the angles and areas of the slices, especially when there are many categories with similar values. A pie chart also does not show the absolute values of each category, unless they are labeled or annotated12. A better alternative would be a bar chart, which can show the number of sales for each state along a common axis, making it easier to compare and rank the values3. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 652: Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 673: The Visual Display of Quantitative Information, Edward R. Tufte, 2001, p. 178.

Validity is the extent to which a measure or a question accurately captures the intended concept or construct1. The question “What is your salary?” is not a valid measure of job satisfaction, as it does not reflect the various aspects of job satisfaction, such as work environment, recognition, autonomy, growth, etc. Salary is only one possible factor that may influence job satisfaction, but it is not a direct or comprehensive indicator of it23. Therefore, the question is not valid for measuring job satisfaction. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Job Satisfaction: Application, Assessment, Causes, and Consequences, Paul E. Spector, 1997, p. 23: Job Satisfaction Survey, 1.

Data governance is the practice of establishing and enforcing policies, standards, roles, and responsibilities for the management and use of data across the organization. Data governance helps to address the problem of inconsistent data definitions across various data sources by ensuring that data is properly defined, documented, classified, and aligned with the business objectives and requirements12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 292: Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, John Ladley, 2012, p. 3.

The percentile score provides value by ranking the attendee’s score among all the scores of the exam takers. A percentile score of 90 means that the attendee scored higher than 90% of the exam takers, and only 10% scored higher than the attendee. This gives a relative measure of how the attendee performed in comparison to other attendees, and how competitive or exceptional the score is. The percentile score does not depend on the average or the highest possible score of the exam, but only on the distribution of the scores of the exam takers. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results

•Understanding the Guide to Business Data Analytics, page 9

•What is a Percentile? - Statistics By Jim

Prescriptive analytics is the type of analytics that the retailer is using to maintain their pricing structure, because it is a technique that uses data and models to recommend the best course of action for a given situation. Prescriptive analytics can help the retailer optimize their prices based on the data collected from the competitors, the market conditions, and the customer preferences, and thus achieve their strategic goal of becoming the market leader. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data

•Understanding the Guide to Business Data Analytics, page 17

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 11

One of the steps in identifying the research questions for business data analytics is to assess the feasibility and desirability of the proposed solution or change1. This involves understanding the needs, preferences, and satisfaction of the existing and potential customers. Therefore, asking whether the existing customers really like the product is a relevant research question for the analytics team. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 22.

The analytics team found a correlation between the age of the customer and the price of the neckties purchased, meaning that as one variable changes, the other tends to change in the same direction. However, this correlation does not imply causation, meaning that one variable does not necessarily cause the other to change. There could be other factors, such as income, preference, or quality, that affect both variables and create a spurious relationship. Therefore, the team realized that they need to investigate further to determine if there is a causal link between the variables, or if the correlation is coincidental12 References: 1: Correlation vs. Causation | Difference, Designs & Examples - Scribbr 2: Correlation vs Causation: Understanding the Differences - Statistics By Jim

Querying is a technique that allows analysts to access, filter, join, aggregate, and transform data from various databases using a specific syntax and logic1. Querying can be used for different purposes, such as data exploration, data preparation, data analysis, and data visualization2. Querying is not limited to creating predictive data models, nor does it always produce tabular results. Moreover, querying languages may vary depending on the type and structure of the database, such as relational, hierarchical, or document-based3. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 552: Data Analysis Using SQL and Excel, Gordon S. Linoff, 2016, p. 33: Database Systems: Design, Implementation, and Management, Carlos Coronel and Steven Morris, 2019, p. 17.

A street address is a primitive data element, because it is a basic unit of data that cannot be further decomposed into smaller components. A primitive data element has a distinct name, definition, format, and value domain. A street address can be used to identify the location of an employee or a customer, and it can be stored as a string or a combination of numbers and characters. Options B, C, and D are not primitive data elements, because they can be further broken down into smaller components. For example, a first name can be divided into a prefix, a given name, and a suffix. A customer name can be composed of a first name and a last name. A work phone number can be split into a country code, an area code, and a local number. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data

•Business analysis data dictionary – The Functional BA

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 15

A layout is a way of arranging the nodes and links of a graph visualization to convey meaningful information about the data. A layout is determined by a dedicated algorithm that calculates the node positions based on certain criteria, such as minimizing edge crossings, maximizing node spacing, or emphasizing clusters12. A layout can also be influenced by user interaction, such as zooming, panning, or dragging3. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 642: Graph Drawing: Algorithms for the Visualization of Graphs, Giuseppe Di Battista et al., 1999, p. 33: Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et al., 2015, p. 227.

Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an overview of what has happened or is happening, such as the trend of winter-related accidents over the past three years, or the percentage of drivers using winter tires12. Diagnostic analytics is a type of analytics that explores and analyzes the data to understand why something has happened or is happening, such as the primary reasons for these accidents, or the factors that influence the drivers’ decisions13. To answer the question, both descriptive and diagnostic analytics would be needed to provide the relevant information and insights for the company. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making, S. Christian Albright and Wayne L. Winston, 2015, p. 53: Data Science for Business, Foster Provost and Tom Fawcett, 2013, p. 13.

A physical data model is the most detailed and specific type of data model, which shows how the data is stored, accessed, and manipulated in the database. It includes the table structures, column names, data types, primary and foreign keys, constraints, indexes, and other physical attributes of the data12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 542: Data Modeling Essentials, Graeme Simsion and Graham Witt, 2005, p. 15.

Factor analysis is the technique that, if used, would achieve the objective of producing a single score of sustainability, because it is a technique that reduces the dimensionality of a data set by identifying the underlying factors or latent variables that explain the variation and correlation among the observed variables. Factor analysis can help the analytics team combine the 10 measures of workplace sustainability into a smaller number of factors, and then derive a composite score of sustainability based on the factor loadings and weights. Factor analysis can also help the analytics team simplify and interpret the data, and identify the key drivers of sustainability. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data

•Understanding the Guide to Business Data Analytics, page 17

•Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 3: Analyze Data, Lecture 15: Factor Analysis

The business analyst is concerned about the validity of the survey. Validity is the extent to which a survey measures what it intends to measure. In this case, the survey is supposed to measure job satisfaction, but the two questions asked to different groups of employees are not equivalent or relevant to this construct. The question “Do you enjoy working in your workplace?” is more directly related to job satisfaction than the question “Do you like the current work benefits?”. The latter question may capture only one aspect of job satisfaction, and may not reflect the overall level of contentment or happiness with the job. Therefore, the survey results may not be valid or accurate in measuring job satisfaction12 References: 1: Survey and questionnaires in business analysis - The Functional BA 2: Job Satisfaction Survey - Paul Spector

Ensuring the result set is statistically significant is important to the team because it means that the difference or relationship observed in the data is unlikely to be due to chance or sampling error. Statistical significance helps the team to assess the validity and reliability of their findings, and to draw meaningful conclusions and recommendations from the data. Statistical significance also helps the team to communicate their results with confidence and credibility to the stakeholders and decision makers12 References: 1: An Easy Introduction to Statistical Significance (With Examples) - Scribbr 2: Statistical Significance in Experimentation and Data Analysis - All About Circuits