IIA Updated IIA-CIA-Part3 Exam Questions and Answers by clark

Understanding Organizational Structures:

Organizations structure their workforce based on functions, products, or a combination of both.

A matrix organization combines functional and project-based structures, where employees report to both a functional manager and a project manager.

Why Option C (Matrix Organization) Is Correct?

The software development firm uses employees from multiple departments who report to a single project manager, which is a defining characteristic of a matrix structure.

Employees maintain their departmental roles while contributing to project-based work.

IIA Standard 2110 – Governance supports evaluating flexible organizational structures like matrix organizations to ensure accountability and risk management.

Why Other Options Are Incorrect?

Option A (Functional departmentalization):

In functional structures, employees report to one department head, not a project manager.

Option B (Product departmentalization):

In product-based structures, employees are grouped based on specific product lines, not cross-functional projects.

Option D (Divisional organization):

A divisional structure separates business units based on markets, regions, or customer segments, not cross-functional teams.

A matrix organization allows employees to work across departments under a project manager, making option C the best choice.

IIA Standard 2110 supports assessing governance structures that involve cross-functional teams.

Final Justification:IIA References:

IPPF Standard 2110 – Governance (Organizational Structures & Reporting Lines)

COSO ERM – Risk Management in Matrix Organizations

Project Management Institute (PMI) – Matrix Management Best Practices

Understanding Matrix Organizations:

A matrix organization is a hybrid structure that combines functional and project-based structures, where employees report to multiple managers (e.g., a functional manager and a project manager).

These organizations adapt to projects by adjusting authority, responsibility, and accountability based on the project's stage or the organization's culture.

Why Option C Is Correct?

In a matrix organization, roles and decision-making authority evolve based on the project's phase, size, or complexity.

Employees might report to different managers at different times, and accountability structures may change.

This aligns with IIA Standard 2110 – Governance, which emphasizes clear roles and responsibilities in dynamic organizational structures.

Why Other Options Are Incorrect?

Option A (Unity-of-command concept):

The unity-of-command principle states that employees should report to only one superior, which contradicts the nature of a matrix organization, where dual reporting exists.

Option B (Combination of product and functional departments allows management to utilize personnel from various functions):

While matrix organizations integrate product and functional departments, the key defining feature is the variable authority, responsibility, and accountability, making option C a better fit.

Option D (Best suited for firms with scattered locations or large-scale firms):

While matrix structures can be used in large firms, they are not limited to them and are often found in project-based industries (e.g., engineering, IT, consulting).

Matrix organizations adapt their authority structures based on project needs, making option C the best choice.

IIA Standard 2110 supports governance structures that evolve with organizational needs.

Final Justification:IIA References:

IPPF Standard 2110 – Governance (Organizational Structure & Accountability)

COSO ERM – Governance & Decision-Making in Matrix Organizations

Analytical procedures involve evaluating financial and operational data by examining plausible relationships between numbers, trends, and industry benchmarks. These procedures assume that data relationships exist and will continue unless there is evidence to the contrary.

(A) Data relationships are assumed to exist and to continue where no known conflicting conditions exist. ✅

Correct. Analytical procedures rely on historical trends and logical relationships between data (e.g., revenue vs. expenses, payroll vs. employee count). If no unusual variations or red flags are observed, auditors assume continuity.

IIA GTAG "Auditing Business Intelligence" supports the assumption that data relationships persist unless evidence suggests otherwise.

(B) Analytical procedures are intended primarily to ensure the accuracy of the information being examined.

Incorrect. The primary goal of analytical procedures is not absolute accuracy but rather identifying trends, anomalies, and risks that require further investigation.

(C) Data relationships cannot include comparisons between operational and statistical data.

Incorrect. Operational and statistical data are commonly used in analytical procedures (e.g., comparing production output with raw material consumption, or customer transactions with website visits).

IIA GTAG "Data Analytics: Elevating Internal Audit Performance" highlights the importance of using both financial and operational data in analytical testing.

(D) Analytical procedures can be used to identify unexpected differences, but cannot be used to identify the absence of differences.

Incorrect. Analytical procedures can identify both unexpected variances and expected consistency. Auditors analyze trends, seasonal fluctuations, and relationships, detecting both errors and missing anomalies.

IIA GTAG – "Auditing Business Intelligence"

IIA GTAG – "Data Analytics: Elevating Internal Audit Performance"

IIA Standard 2320 – Analysis and Evaluation

Analysis of Answer Choices:IIA References:Thus, the correct answer is A, as analytical procedures assume data relationships exist and continue unless conflicting conditions arise.

Comprehensive and Detailed In-Depth Explanation:

In data analytics, data cleaning involves identifying and correcting errors, inconsistencies, and redundancies in the dataset to ensure accuracy and reliability. By eliminating duplicate or irrelevant data, the internal auditor enhances the quality of the dataset, which is crucial for accurate analysis and risk assessment. This process is a preparatory step before analyzing the data to identify high-risk areas. Normalization (option A) refers to organizing data to reduce redundancy but is more specific to database design. Analyzing data (option B) and reviewing data prior to defining the question (option D) are steps that occur before and after data cleaning, respectively.