CPA Quiz: Apply Data Analytics To Identify Anomalies

What this quiz covers

This quiz focuses on Apply Data Analytics To Identify Anomalies, giving you a quick way to practice the rules, question types, and explanations that matter most for CPA.

How to use this quiz

Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.

Question 1

1. To eliminate duplicate records from a data population before analysis.
2. To divide a data population into homogeneous subgroups based on defined characteristics so that each subgroup can be analyzed separately. (correct answer)
3. To calculate the statistical correlation between two variables in the dataset.
4. To convert unstructured data into a structured format suitable for analysis.

Question 2

1. Matching and deduplication analysis comparing invoice number, vendor ID, amount, and date. (correct answer)
2. Regression analysis correlating invoice amounts to vendor size.
3. Time-series analysis of monthly payment totals.
4. Stratification of invoices by dollar amount range.

Question 3

1. Regression analysis to predict future payment trends.
2. Data normalization to prepare data for statistical analysis.
3. Anomaly detection to identify transactions that deviate from expected patterns. (correct answer)
4. Data visualization to present payment trends to management.

Question 4

1. The expense data is accurate and normally distributed.
2. Employees may be fabricating or manipulating expense amounts, potentially clustering just below a review threshold. (correct answer)
3. The expense system contains data entry errors from incorrect date fields.
4. Employees are rounding expenses to the nearest dollar.

Question 5

1. An audit conducted once per year using a complete population of transactions.
2. An approach in which auditors perform manual testing of controls on a sample basis.
3. An automated process that analyzes transactions on an ongoing basis and generates alerts when anomalies or exceptions are detected. (correct answer)
4. A method of preparing financial statements using real-time accounting data.

Question 6

1. Purchase order splitting - breaking a large purchase into smaller amounts to avoid authorization controls. (correct answer)
2. Vendor master file manipulation to add fictitious vendors.
3. Duplicate payments to legitimate vendors.
4. Unauthorized changes to inventory quantities in the ERP system.

Question 7

1. Applying statistical models to predict future transaction patterns.
2. Visualizing data in charts to present findings to audit committees.
3. Examining the structure, content, and quality of a dataset to understand its characteristics and identify potential data issues before analysis. (correct answer)
4. Encrypting sensitive data fields before analysis to protect privacy.

Question 8

1. Regression analysis using journal entry amounts as the dependent variable.
2. Full population analysis with multiple filter criteria applied simultaneously to the complete journal entry file. (correct answer)
3. Statistical sampling of journal entries using systematic selection.
4. Cluster analysis to group journal entries by posting date.

Question 9

1. Trend analysis of total payroll costs over time.
2. Benford's Law analysis of payroll transaction amounts.
3. Exception reporting to identify transactions outside defined parameters. (correct answer)
4. Regression analysis to predict future payroll costs.

Question 10

1. Time-series analysis of payment amounts by vendor.
2. Stratification of vendors by payment frequency.
3. Cross-reference matching of vendor bank account numbers across all vendor master records. (correct answer)
4. Benford's Law analysis of vendor payment amounts.

Question 11

1. Completeness - ensuring all sales transactions are recorded.
2. Accuracy - identifying whether recorded revenue amounts are consistent with expectations based on underlying business activity. (correct answer)
3. Existence - confirming that recorded sales relate to actual transactions.
4. Cutoff - ensuring transactions are recorded in the correct period.

Question 12

1. Ratio analysis to identify outliers relative to a benchmark. (correct answer)
2. Benford's Law analysis to examine leading digit frequencies.
3. Regression analysis to predict expected expense levels.
4. Stratification to group employees by expense category.

Question 13

1. Duplicate vendor payments processed during normal business hours.
2. Payroll fraud involving ghost employees.
3. Inventory theft by warehouse employees.
4. Unauthorized journal entries posted outside normal business hours by individuals attempting to conceal fraudulent activity. (correct answer)

Question 14

1. Normal seasonal fluctuation in inventory levels.
2. Potential manipulation of period-end inventory to manage reported financial results. (correct answer)
3. Routine system recalibration performed by IT operations staff.
4. Data entry errors caused by inadequate training of warehouse staff.

Question 15

1. Duplicate posting of the same invoice to multiple customers.
2. Unauthorized changes to customer credit limits.
3. Systematic rounding errors in the billing system.
4. Potentially fictitious or uncollectible balances that have not been written off or investigated. (correct answer)

Question 16

1. Audit command language (ACL) / Galvanize or IDEA (Interactive Data Extraction and Analysis). (correct answer)
2. Python scripts for unstructured web data scraping.
3. Business intelligence dashboards for executive reporting.
4. Database normalization tools for schema design.

Question 17

1. Identify invoices with amounts that deviate from the average transaction value.
2. Detect duplicate invoice numbers that have been used more than once.
3. Identify missing invoice numbers that may indicate unrecorded sales transactions. (correct answer)
4. Calculate the statistical distribution of invoice amounts across customers.

Question 18

1. Duplicate employee records in the HR system.
2. Unauthorized changes to the vendor master file.
3. Journal entries posted to incorrect general ledger accounts.
4. Payment of invoices that do not have a matching purchase order and goods receipt. (correct answer)

Question 19

1. Data analytics always produces definitive evidence of fraud.
2. Data analytics eliminates the need for auditor judgment.
3. Data analytics is less expensive than traditional audit procedures.
4. Data analytics can test 100% of a transaction population, eliminating sampling risk. (correct answer)

Question 20

1. Incomplete or inaccurate data in the source system, leading to false positives or missed anomalies. (correct answer)
2. The use of SQL queries rather than specialized audit software.
3. Analyzing data from multiple fiscal years rather than a single period.
4. The presence of large transaction amounts in the dataset.