IIBA-CCA Exam Dumps - Certificate in Cybersecurity Analysis (CCA)

In cybersecurity risk management,impactrefers to theseverity of adverse consequencesif a threat event occurs and successfully affects information or systems. It is the “so what” of a risk scenario: how much damage the organization, its customers, or other stakeholders could experience when confidentiality, integrity, or availability is compromised. Impact commonly includes multiple dimensions such as operational disruption, loss of critical services, harm to customers, legal or regulatory exposure, reputational damage, and direct and indirect financial loss. Because these consequences can extend beyond money, impact is broader than just costs and also includes mission failure, safety implications, loss of competitive advantage, and degradation of trust.

Option D captures this correctly by describing impact as the magnitude of harm expected from unauthorized use of information. Option C describes likelihood, not impact, because it focuses on probability over time. Option B is only one component of impact, since financial cost is important but does not fully represent business, legal, and operational consequences. Option A is also a possible consequence but is narrower than the full impact concept. Cybersecurity risk scoring typically combines likelihood and impact to prioritize treatment, ensuring high-impact scenarios receive attention even when probabilities vary.

In a data warehouse, information from multiple operational sources is consolidated, transformed, and related through keys, joins, and business rules. When a Business Analyst defines processes and procedures that describehow data sets interrelate, they are primarily controlling the risk created bydata aggregation. Aggregation risk arises when combining multiple datasets produces a new, richer dataset that can change the meaning, sensitivity, or trustworthiness of the information. If relationships and transformation rules are poorly defined or inconsistently applied, the warehouse can generate misleading analytics, incorrect roll-ups, duplicated records, or invalid correlations—directly harminginformation integritybecause decisions are made on inaccurate or improperly combined data.

Well-defined interrelation procedures specify authoritative sources, master data rules, key management, referential integrity expectations, transformation and reconciliation steps, and data lineage. These controls help ensure the warehouse preserves correctness when data is integrated across systems with different formats, definitions, and update cycles. They also support governance by enabling validation checks (for example, balancing totals to source systems, exception handling, and data-quality thresholds) and by making it clear which dataset should be trusted for specific attributes.

Unauthorized access and confidentiality are important warehouse risks, but they are addressed mainly through access controls and encryption. Cross-site scripting is a web application vulnerability and is not the core issue in describing dataset relationships. Therefore, the correct answer isData Aggregation.

Multi-factor authentication requires a user to prove identity usingtwo or more different factor types. Cybersecurity standards describe the main factor categories assomething you know(for example, a password or PIN),something you have(for example, a hardware token, smart card, or authenticator app producing a one-time code), andsomething you are(biometrics such as fingerprint, face, or iris). A valid MFA pair must come fromdifferent categories, not just two items from the same category or a mix of authentication with non-authentication concepts.

OptionBis correct because it explicitly combines two distinct factor types: a knowledge factor and an inherence factor. This pairing is widely recognized as MFA because compromising one factor does not automatically compromise the other: an attacker who steals a password still needs the biometric, and spoofing a biometric does not provide the secret knowledge factor.

OptionAis incorrect because “encryption” is not an authentication factor; it is a protection mechanism for confidentiality and integrity of data. OptionDhas the same problem: encryption is not a user factor. OptionCcan represent MFA in many real implementations if “token” is truly a possession factor; however, training materials and exam items often prefer the clearest, unambiguous factor-language pairing, which is why “Something You Know and Something You Are” is the best single answer here.

Security categorization (commonly based on confidentiality, integrity, and availability impact levels) is meant to reflect the level of harm that would occur if an information type or system is compromised. A business impact analysis, on the other hand, examines the operational and organizational consequences of disruptions or failures—such as loss of revenue, inability to deliver critical services, legal or regulatory exposure, reputational harm, and impacts to customers or individuals. Because these two activities look at impact from different but related perspectives, categorization information should be used during the BIA to confirm that the stated security categorization truly matches real business consequences.

Using categorization as an input helps analysts validate assumptions about criticality, sensitivity, and tolerance for downtime. If the BIA shows that outages or data compromise would produce greater harm than the existing categorization implies, that discrepancy signals under-classification and insufficient controls. Conversely, if the BIA demonstrates limited impact, it may indicate over-classification, potentially driving unnecessary cost and operational burden. Identifying these mismatches early supports better risk decisions, prioritization of recovery objectives, and selection of controls proportionate to actual impact.

The other options describe activities that may occur in architecture, governance, or project planning, but they are not the primary purpose of using categorization information in a BIA. The key value is reconciliation: aligning security impact levels with verified business impact.

Social engineeringis the broad technique attackers use when they manipulate human psychology—such as trust, fear, urgency, curiosity, sympathy, authority, or the desire to be helpful—to persuade someone to take an action that benefits the attacker. The key idea in the question is “exploit human emotions and connection,” which is the defining characteristic of social engineering. Rather than breaking a system through purely technical means, the attacker targets the person as the easiest path to access, credentials, sensitive information, or physical entry.

Phishing is aspecific subtypeof social engineering that typically uses email, text messages, or fake websites to trick users into clicking links, opening attachments, or entering credentials. Tailgating is another subtype focused onphysical access, where an attacker follows an authorized person into a restricted area by leveraging politeness or social pressure. Malware is malicious software used to compromise systems; it can be delivered through social engineering, but malware itself is not the human-manipulation technique.

Cybersecurity control guidance treats social engineering as a major risk because it can bypass technical protections by causing legitimate users to unintentionally grant access. Common defenses include awareness training, verification procedures (call-back and out-of-band confirmation), least privilege, multi-factor authentication, strong email and web filtering, and clear reporting channels so suspicious requests can be escalated quickly.