Isaca Updated CRISC Exam Questions and Answers by tymoteusz

 The risk of users circumventing application controls is the most significant exposure when an application uses individual user accounts to access the underlying database. This is because users may have direct access to the data and bypass the validation, authorization, and logging mechanisms that are implemented at the application level. Users may also be able to modify or delete data without proper authorization or audit trail. The other options are less significant exposures, as they do not directly affect the integrity or confidentiality of the data. References = Risk IT Framework, ISACA, 2009, page 35; CRISC Review Manual, 6th Edition, ISACA, 2015, page 214.

The drawback in the use of quantitative risk analysis is that it requires more resources than other methods. Quantitative risk analysis is a method of risk analysis that assigns numeric values to the exposures of assets, the impact and likelihood of risk events, and the cost and benefit of risk responses. Quantitative risk analysis can provide more precise and objective results, and support the risk-based decision making process. However, quantitative risk analysis also requires more resources than other methods, such as data, time, expertise, and tools, to collect, validate, and analyze the quantitative information, and to perform the complex calculations and simulations. Quantitative risk analysis may also be limited by the availability, reliability, and accuracy of thedata, and the assumptions and models used. Assigning numeric values to exposures of assets, producing the results in numeric form, and being based on impact analysis of information assets are not drawbacks, but characteristics of quantitative risk analysis. References = CRISC Review Manual, 6th Edition, ISACA, 2015, page 49.

Log modification undermines data integrity, which is critical for accurate risk monitoring. Ensuring log integrity supports reliable KRI assessments, a key focus within theRisk Monitoring and Reportingframework.

The primary concern for a risk practitioner in adopting innovative big data analytics is the potential re-identification of individuals from previously anonymized data. Advanced analytics techniques can inadvertently combine datasets in ways that reveal personal identities, leading to privacy breaches and regulatory non-compliance. This risk is heightened when data from multiple sources are aggregated, increasing the chance of re-identification.