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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 Nov. 15, 2024 (see below)
The research study is complete, the data has been analyzed and the team has created the necessary high impact visuals. The business analysis professional urges the team to:
An analyst is working through data on comparing performance scores in different schools across the state, for ranking purposes. Since there is a lot of data and some extreme outliers, the analyst is trying to determine which type of statistical average would best represent the results. Which of the following is a concern when relying too heavily on summary statistics during data analysis?
Summary statistics are numerical measures that describe certain characteristics of a data set, such as the mean, median, mode, standard deviation, range, or quartiles. Summary statistics can help simplify and communicate complex data, but they can also obscure or distort important information, such as the distribution, shape, outliers, or trends of the data. Contextualization is the process of providing relevant background information, assumptions, limitations, or explanations for the data analysis and its results. Contextualization can help avoid misinterpretation, confusion, or bias when using summary statistics. Contextualization can also help connect the data analysis to the business problem, objectives, and stakeholders.
An operations manager for a new hotel is in need of determining the optimum number of vans to purchase to shuttle guests to/from the airport. It will be necessary to determine the most efficient routes and schedule to follow to ensure guests do not experience excessive delays. Which business analytics technique would lend itself to supporting these types of business decisions?
An analyst supporting the Marketing department for a specialty retailer has been asked to look through past sales data to help guide product decisions. The business sponsor for this initiative would first like to know 'What is the most profitable product line?'. What type of analytics is the analyst going to perform to address this question?
A data dictionary is being developed for a dataset describing a company's customer base. Within the data dictionary, which of the following represents a composite data element?
A composite data element is a data element that is made up of smaller units called sub-elements, which are separated by a sub-element separator character, such as a colon (:). For example, ITEMNO is a composite data element that consists of three sub-elements: part number, aisle number, and bin number. A street address is also a composite data element that can consist of sub-elements such as street number, street name, city, state, and zip code. First name, total sale, and birthdate are simple data elements that do not have sub-elements.
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