Online IIA-CIA-Part3 Questions Video

Internal audit methodologies are determined by the CAE and should be aligned with the IIA’s principles of confidentiality, integrity, objectivity, and competency. This ensures methodology design is consistent with professional standards.

Option A misstates the objective—methodologies are not for client validation. Option B is incorrect because methodologies are not required to be publicly posted. Option C mischaracterizes the objective: methodology ensures audit consistency, not execution of organizational strategy directly.

When management disagrees with audit findings, the auditor should escalate the matter to the CAE. The CAE can determine whether to include both perspectives in the report or escalate further if unresolved. This ensures objectivity and fair representation.

Option A (escalation to CEO) is premature. Option B ignores management’s input, reducing objectivity. Option D (reperforming work) is only necessary if there is evidence the work was flawed, not simply because of disagreement.

The QAIP (Quality Assurance and Improvement Program) requires both ongoing monitoring and periodic assessments. Among these, ongoing monitoring is the mechanism that ensures continuous evaluation of whether engagements are being performed with quality and in conformance with the Standards.

Option A (periodic assessments) review effectiveness but are not continuous. Option C (external assessments) and Option D (SAIV) are broader and periodic, not engagement-level consistency checks.

When an internal auditor calculates the mean (average), median (middle value), and range (difference between highest and lowest values) of a data population, the primary purpose is to assess the distribution of data and detect anomalies. Let’s analyze the answer choices:

Option A: To inform the classification of the data population.

Incorrect. Classification typically involves categorizing data into specific groups, which requires different statistical or analytical techniques like clustering or decision trees. Mean, median, and range are more useful for identifying distribution patterns.

Option B: To determine the completeness and accuracy of the data.

Incorrect. While summary statistics can highlight extreme values, completeness and accuracy are usually assessed through data reconciliation, validation checks, and comparison with source records.

Option C: To identify whether the population contains outliers.

Correct.

The range (difference between the largest and smallest values) helps to detect extreme values.

The mean and median can show whether the data is symmetrical or skewed (which may indicate outliers).

If the mean is significantly different from the median, it suggests potential outliers pulling the average in one direction.

IIA Reference: Internal auditors use data analytics to detect anomalies and potential fraud by identifying outliers. (IIA GTAG: Auditing with Data Analytics)

Option D: To determine whether duplicates in the data inflate the range.

Incorrect. Duplicates may affect the data set, but range calculations alone do not determine whether duplicates exist. Duplicate identification usually involves checking for repeated entries, not just extreme values.