Exin CITM Exam Dumps

In ITIL (Information Technology Infrastructure Library), an emergency change is implemented to address urgent issues that significantly impact business operations, such as a processing error during financial year closing. Emergency changes are fast-tracked to restore service or prevent further disruption, bypassing some standard change management processes while still requiring approval.

Normal changes (A) follow the full change management process, standard changes (B) are pre-approved and routine, and exceptional (C) is not a standard ITIL term. Emergency change (D) fits the scenario of urgent action to avoid business delays.

A white box test involves testing the internal structure and code of an application, requiring access to its source code. The stakeholders' demand for an independent white box test indicates their primary concern is the quality of the source code (C). This type of testing, conducted by an independent party, ensures the code is well-structured, secure, and free of defects that could lead to vulnerabilities or inefficiencies.

Capacity (A): Refers to the system's ability to handle load, typically tested via performance or stress testing, not white box testing.

Performance (B): Focuses on speed and responsiveness, evaluated through performance testing, not white box testing.

Functionality (D): Is tested via black box testing, which focuses on inputs and outputs without examining the code.

White box testing is a technical process often aligned with SDLC quality assurance practices, ensuring code reliability and maintainability, which is critical for stakeholders concerned about long-term system integrity.

In risk management, the Annual Loss Expectancy (ALE) is calculated as:

ALE = Single Loss Expectancy (SLE) Annualized Rate of Occurrence (ARO), where SLE = Asset Value Exposure Factor (EF).

Given:

Asset Value = $200,000

Exposure Factor (EF) = 25% = 0.25

ALE = $100,000

Calculate SLE:

SLE = Asset Value EF = $200,000 0.25 = $50,000

Calculate ARO:

ALE = SLE ARO

$100,000 = $50,000 ARO

ARO = $100,000 $50,000 = 2

Thus, the Annualized Rate of Occurrence (ARO) is 2 (C), meaning the incident is expected to occur twice per year.

0.4 (A): Incorrect; implies a lower frequency (0.4 times per year).

1 (B): Incorrect; would yield an ALE of $50,000, not $100,000.

Security awareness programs require ongoing engagement to remain effective. If security incidents decrease initially but increase after eight months, the most likely cause is that message materials are few and static, and renewal is not taking place (C). Static content becomes outdated or ignored over time, reducing its impact. Regular updates, new campaigns, and varied delivery methods (e.g., videos, quizzes) are essential to maintain employee awareness and adapt to evolving threats, as per ISO/IEC 27001 or NIST security awareness guidelines.

Insufficient budget (A): While budget constraints could limit program scope, there's no evidence in the scenario to suggest this is the primary issue.

Scope too narrow (B): A narrow scope might limit effectiveness initially, but the initial success suggests the scope was adequate; the issue is sustaining engagement.

Lack of resources for instructor-led sessions (D): Instructor-led sessions are one delivery method, but the core issue is likely outdated content rather than delivery format.

To predict future customer purchases, the marketing department requires advanced analytics (B), which involves sophisticated data analysis techniques, such as predictive modeling, machine learning, and data mining. These technologies enable the department to analyze customer behavior, identify patterns, and forecast purchasing trends, supporting targeted advertising campaigns.

Records Management System (RMS) (A): Focuses on managing and storing records, not predictive analysis.

Ad hoc analysis (C): Allows for on-demand, one-off queries but lacks the predictive capabilities of advanced analytics.

Business Intelligence (BI) (D): Provides reporting and historical data analysis but is less focused on predictive modeling compared to advanced analytics.

Advanced analytics aligns with IT strategy goals of leveraging data for competitive advantage, as it supports predictive insights critical for marketing decisions.