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IIBA CBDA Exam Questions
- Topic 1: Financial Transactions and Fraud Schemes: This section evaluates your understanding of fraudulent financial transactions in accounting records. You'll need to showcase your knowledge of fundamental accounting and auditing principles, various fraud schemes, fraud deterrence through internal controls, additional relevant accounting and auditing topics.
- Topic 2: Law: This portion examines your grasp of legal frameworks used in prosecuting fraud cases. It also assesses your awareness of the legal implications involved in conducting fraud examinations such as elements of criminal and civil law, evidentiary rules, and legal rights of defendants and plaintiffs.
- Topic 3: Investigations: This domain assesses your proficiency in essential investigative techniques for gathering evidence and identifying fraud perpetrators and it covers effective interviewing strategies, methods for obtaining statements, accessing and utilizing public records, techniques for tracking illicit financial activities Recognizing deceptive behavior, and crafting comprehensive investigation reports.
- Topic 4: Fraud Prevention and Deterrence: In this segment, candidates are tested for their knowledge of fraud motivations and preventive measures such as theories of criminal behavior, characteristics of white-collar crime, patterns in occupational fraud, strategies for fraud prevention, conducting fraud risk assessments, ethical standards as outlined in the ACFE Code of Professional Ethics.
Free IIBA CBDA Exam Actual Questions
Note: Premium Questions for CBDA were last updated On Sep. 14, 2026 (see below)
A research marketer is interested in collecting information about the spending habits of families in North Americ
a. Concerned about the volume of data required to conduct the research, they choose to use sampling. The dataset is sourced using all credit card transactions from a leading North American credit card company for Quarter 1 of the prior year. The sample used is:
The sample used in this case is biased, meaning that it is not representative of the population of interest. The population of interest is the families in North America, but the sample is drawn from only one source of data: the credit card transactions from a leading North American credit card company. This sample excludes the families who do not use credit cards, or who use other credit card companies, or who use other payment methods. Therefore, the sample is not random or fair, and it may introduce sampling bias into the research results12 Reference: 1: Sampling Methods | Types, Techniques & Examples 2: Sampling Bias - an overview | ScienceDirect Topics
An analyst is looking at a particular dataset that includes the scores across all 8th grade students, across three schools. The analyst is trying to determine which type of statistics average to use to best represent the results. On looking through the dataset, the analyst has identified a few extreme outliers. As a result, the analyst was led to use the following type of average:
The median is the type of statistics average that the analyst should use to best represent the results, because it is a measure of central tendency that divides the data set into two equal halves. The median is the middle value of the data set when it is arranged in ascending or descending order. The median is not affected by extreme outliers, unlike the mean, which is the arithmetic average of the data set. The median can give a more accurate representation of the typical score of the 8th grade students across the three schools. Options B, C, and D are not types of statistics average, but types of statistics measures that describe other aspects of the data set. The range is a measure of dispersion that shows the difference between the highest and the lowest values of the data set. The mean is a measure of central tendency that shows the sum of the values of the data set divided by the number of values. The mode is a measure of central tendency that shows the most frequent value of the data set. Reference:
* 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 13: Descriptive Statistics
A database analyst is modelling a database for a large toy manufacturer. Which statement describes a logical database model?
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 Reference: 1: Logical schema - Wikipedia 2: What Is a Data Model? | Coursera
The team has completed their analysis on a vast amount of collected data and agree on their recommendations for action.
However, they are having difficulty in developing the appropriate messages to support their recommendations. The business analysis professional suggests which technique to assist the team?
Storyboarding is a technique that helps the team to develop the appropriate messages to support their recommendations by creating a visual sequence of the main points, evidence, and actions. Storyboarding helps the team to organize their thoughts, identify gaps, and communicate their findings in a clear and compelling way12 Reference: 1: Developing Key Messages for Effective Communication - MSKTC 2: 11 Ways Highly Successful Leaders Support Their Team - Redbooth
While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values. Which activity would the analyst perform in order to use this dataset?
Weighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process. Reference:
* Understanding the Guide to Business Data Analytics, page 16
* Business Analysis Certification in Data Analytics, CBDA | IIBA, CBDA Competencies, Domain 3: Analyze Data
* CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4
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