ADA - Audit Data Analytics Predictive Analytics Models Questions and Answers

Question 1

An audit team wants to develop a model that predicts the monetary value of expected sales for the next quarter based on historical sales data, seasonality, and recent marketing expenditures. Which predictive analytics model would be most suitable for this task?

Accepted Answer

Regression Analysis

Answer

Regression analysis is the appropriate technique because it is used to model the relationship between a dependent variable (in this case, the monetary value of sales) and one or more independent variables (historical data, seasonality, marketing spend) to predict a continuous numerical outcome.

Question 2

An internal auditor is tasked with building a predictive model to identify which employee expense reports are most likely to be fraudulent. The model needs to categorize each new report as either 'high-risk' or 'low-risk'. Which of the following models is the most appropriate choice?

Accepted Answer

Classification

Answer

A classification model is designed to predict a categorical label, such as 'high-risk' or 'low-risk'. It learns from historical data where reports were already labeled as fraudulent or not, and then applies that learning to new, unlabeled data. Linear regression predicts a number, not a category, and time-series is for forecasting trends over time.

Question 3

Which of the following scenarios is the BEST application of time-series forecasting in an audit context?

Accepted Answer

Establishing an expected monthly revenue baseline to identify significant anomalies in the current year.

Answer

Time-series forecasting uses historical data points ordered in time to predict future values. In an audit, this is commonly used to create a reliable expectation or baseline (e.g., for revenue or expenses), and then compare actual results against the forecast to flag significant, unexpected deviations that warrant investigation.

Question 4

An auditor is using logistic regression to develop a model that assesses the likelihood of a company defaulting on a loan. What type of output will this predictive model primarily generate?

Accepted Answer

A probability score between 0 and 1 indicating the likelihood of default.

Answer

Logistic regression is a specific type of classification algorithm used to predict a binary outcome (e.g., default/no default). It works by calculating the probability of the event occurring, which is expressed as a value between 0 and 1.

Question 5

When an auditor develops a supervised machine learning model to predict fraudulent transactions, what is the most critical requirement for the training data?

Accepted Answer

It must contain a large and representative set of both known fraudulent and non-fraudulent transactions.

Answer

Supervised learning models require a labeled dataset to learn from. To effectively learn the patterns that distinguish fraudulent from legitimate transactions, the model must be trained on a comprehensive dataset that includes clear examples of both categories.

ADA - Audit Data Analytics Certification Practice Test

ADA - Audit Data Analytics Certification Practice Test

ADA - Audit Data Analytics Predictive Analytics Models Questions and Answers