IIA IIA-CIA-Part3 Dumps Questions Answers

Remote wipe is not always 100% effective: While remote wiping can delete most user data, some residual data may remain on the device, especially in cases where:

The device has built-in storage redundancies.

Deleted data can be recovered using forensic tools.

The remote wipe command fails to execute properly due to network issues or device settings.

Security Risk: This limitation poses a risk for organizations handling sensitive or confidential data, as unauthorized individuals may recover wiped data.

IIA Standard 2110 - Governance: Internal auditors must assess how organizations manage IT security risks, including risks related to mobile devices and data protection.

IIA Practice Guide: Auditing Cybersecurity Risks highlights the need to evaluate mobile security controls and limitations of data removal techniques.

A. Encrypted data cannot be locked to prevent further access (Incorrect)

Encrypted data remains secure even if the device is lost.

Many enterprise security solutions allow organizations to revoke encryption keys remotely, making data inaccessible.

IIA Standard 2120 - Risk Management advises that effective encryption reduces the impact of data loss.

B. Default settings cannot be restored on the device. (Incorrect)

Most remote wipe solutions allow factory reset, restoring the device to default settings.

Many mobile device management (MDM) tools support full device restoration.

D. Mobile device management software is required for a successful remote wipe. (Incorrect)

While MDM enhances remote wiping capabilities, it is not strictly required.

Some consumer and enterprise mobile operating systems (e.g., iOS, Android) provide built-in remote wipe functionality without MDM.

Explanation of Answer Choice C (Correct Answer):Explanation of Incorrect Answers:Conclusion:Remote wipe has limitations, and the inability to completely remove all data from the device (Option C) is a primary concern.

IIA References:

IIA Standard 2110 - Governance

IIA Standard 2120 - Risk Management

IIA Practice Guide: Auditing Cybersecurity Risks

Earned Value Analysis (EVA) is a project management technique that integrates scope, time, and cost data to measure project performance and progress objectively. EVA allows internal auditors to assess whether a software development project is on track by comparing planned work with completed work and actual costs.

Here’s why EVA is the most appropriate choice:

Evaluates Project Progress and Performance – EVA measures how much work has been completed against the planned schedule and budget, helping auditors analyze project efficiency.

Identifies Deviations – It highlights cost overruns or delays in task completion, which is critical for software development projects.

Uses Key Metrics – EVA includes essential indicators like:

Planned Value (PV) – The budgeted cost of work scheduled.

Earned Value (EV) – The value of actual work performed.

Actual Cost (AC) – The real cost incurred for work completed.

Schedule Variance (SV) and Cost Variance (CV) – Indicators of deviations from planned performance.

Supports Risk-Based Internal Audit Approach – The IIA emphasizes risk-based auditing, and EVA helps auditors assess risks related to project cost overruns, schedule slippage, and performance gaps.

A. A Balanced Scorecard – This measures overall organizational performance across perspectives (financial, customer, internal processes, and learning & growth), but it is not specifically designed for evaluating project task completion.

B. A Quality Audit – This focuses on compliance with quality standards and does not measure project task completion efficiency.

D. Trend Analysis – This evaluates patterns over time but does not provide a structured measurement of project progress in terms of cost, time, and completion percentage.

The IIA’s GTAG (Global Technology Audit Guide) on IT Project Management – Recommends using earned value analysis for project auditing.

IIA’s International Professional Practices Framework (IPPF) – Performance Standard 2120 (Risk Management) – Emphasizes the need for internal auditors to evaluate the effectiveness of project risk management, which EVA supports.

COSO’s Enterprise Risk Management (ERM) Framework – Encourages structured performance measurement techniques like EVA to monitor projects.

Why Not the Other Options?IIA References:Thus, Earned Value Analysis (EVA) is the correct answer because it provides a precise, quantitative way to measure project performance. ✅

In data analytics, cleaning the data is a crucial step where the auditor eliminates redundancies, corrects inconsistencies, and removes errors to ensure accurate analysis. This step is taken before analyzing the data to identify high-risk areas and relevant processes.

Correct Answer (C - Cleaning the Data in Preparation for Determining Involved Processes)

Data cleaning involves:

Removing duplicate entries to prevent misinterpretation.

Standardizing data formats for consistency.

Handling missing or inaccurate values to ensure reliability.

This step prepares the data for analysis and identification of high-risk processes.

The IIA’s GTAG 16: Data Analysis Technologies emphasizes data cleaning as a critical part of internal audit analytics.

Why Other Options Are Incorrect:

Option A (Normalizing data in preparation for analyzing it):

Normalization refers to structuring data efficiently (e.g., in databases) but does not necessarily involve eliminating redundancies in the way described.

Option B (Analyzing data in preparation for communicating results):

The auditor is still in the data preparation phase, not the analysis or reporting phase.

Option D (Reviewing data prior to defining the question):

The auditor is already working with data. Defining questions typically happens before data collection.

GTAG 16: Data Analysis Technologies – Covers data preparation, cleaning, and analytics in internal auditing.

IIA Practice Guide: Data Analytics in Internal Auditing – Outlines best practices for data validation and cleaning.

Step-by-Step Explanation:IIA References for Validation:Thus, cleaning the data (C) is the correct answer, as it ensures data integrity before identifying relevant processes and risks.