CISA Exam Dumps - Certified Information Systems Auditor

Clustering is a technique that groups multiple servers or nodes together to act as one system, providing high availability, scalability, and load balancing for applications or services. Clustering can improve system resiliency, which is the ability of a system to withstand or recover from failures or disruptions without compromising its functionality or performance. Clustering can achieve this by providing redundancy and fault tolerance for critical components or processes, enabling automatic failover and recovery in case of node failures, distributing workload among multiple nodes to avoid overloading or bottlenecks, and allowing dynamic addition or removal of nodes to meet changingdemand or capacity needs. Clustering may also decrease system response time by improving performance and efficiency through load balancing and parallel processing, but this is not its primary purpose. Clustering may facilitate faster backups by enabling concurrent backup operations across multiple nodes, but this is not its main benefit. Clustering may improve the recovery time objective (RTO), which is the maximum acceptable time for restoring a system or service after a disruption, by reducing the downtime and data loss caused by failures, but this is not the best reason for using clustering, as there may be other factors that affect the RTO, such as backup frequency, recovery procedures, and testing methods. 

The sampling method in which the entire sample is considered to be irregular if a single error is found is discovery sampling. Discovery sampling is a type of statistical sampling that is used to test for the existence of at least one occurrence of a specific characteristic or condition in a population. Discovery sampling is often used when the auditor expects the characteristic or condition to be very rare or nonexistent, and when any occurrence would have a significant impact on the audit objective. For example, discovery sampling can be used to test for fraud, noncompliance, or material misstatement.

Discovery sampling works by setting a very low tolerable error rate (the maximum rate of occurrence of the characteristic or condition that the auditor is willing to accept) and a high confidence level (the degree of assurance that the auditor wants to obtain). The auditor then selects a sample from the population using a random or systematic method, and examines each item in the sample for the presence or absence of the characteristic or condition. If no error is found in the sample, the auditor can conclude with a high level of confidence that the characteristic or condition does not exist or is very rare in the population. However, if one or more errors are found in the sample, the auditor cannot draw any conclusion about the population and must either expand the sample size or perform alternative procedures.

Discovery sampling differs from other sampling methods in that it does not allow for any errors in the sample. Other sampling methods, such as variable sampling, stop-or-go sampling, or judgmental sampling, can tolerate some errors in the sample and use them to estimate the error rate or amount in the population. However, discovery sampling is designed to test for zero-tolerance situations, where any error would be unacceptable or material. Therefore, discovery sampling considers the entire sample to be irregular if a single error is found.

References:

Audit Sampling - Overview, Purpose, Importance, and Types1

Audit Sampling - What Is It, Methods, Example, Advantage, Reason2

ISA 530: Audit sampling | ICAEW3

Audit Sampling - AICPA4

The best way to identify whether the IT help desk is meeting service level agreements (SLAs) is A. Review exception reports. Exception reports are documents that highlight any deviations from the agreed service levels, such as breaches, delays, or failures. They can help the IT help desk to monitor their performance, identify root causes, and implement corrective actions. Reviewing exception reports can also help the IT help desk to communicate with the end users and stakeholders about any service issues and their resolution.

Substantive testing © provides the best evidence of the validity and integrity of logs in an organization’s security information and event management (SIEM) system, because it is a type of audit testing that directly examines the accuracy, completeness, and reliability of the data and transactions recorded in the logs. Substantive testing can involve various methods, such as re-performance, inspection, observation, inquiry, or computer-assisted audit techniques (CAATs), to verify the existence, occurrence, valuation, ownership, presentation, and disclosure of the log data1. Substantive testing canalso detect any errors, omissions, alterations, or manipulations of the log data that may indicate fraud or misstatement2.

Compliance testing (A) is not the best evidence of the validity and integrity of logs in an organization’s SIEM system, because it is a type of audit testing that evaluates the design and effectiveness of the internal controls that are implemented to ensure compliance with laws, regulations, policies, and procedures. Compliance testing can involve various methods, such as walkthroughs, questionnaires, checklists, or flowcharts, to assess the adequacy, consistency, and operation of the internal controls1. Compliance testing can provide assurance that the log data are generated and processed in accordance with the established rules and standards, but it does not directly verify the accuracy and reliability of the log data itself2.

Stop-or-go sampling (B) is not a type of audit testing, but a type of sampling technique that auditors use to select a sample from a population for testing. Stop-or-go sampling is a sequential sampling technique that allows auditors to stop testing before reaching the predetermined sample size if the results are satisfactory or conclusive. Stop-or-go sampling can reduce the audit cost and time by avoiding unnecessary testing, but it can also increase the sampling risk and uncertainty by relying on a smaller sample3. Stop-or-go sampling does not provide any evidence of the validity and integrity of logs in an organization’s SIEM system by itself; it depends on the type and quality of the audit tests performed on the selected sample.

Variable sampling (D) is not a type of audit testing, but a type of sampling technique that auditors use to estimate a numerical characteristic of a population for testing. Variable sampling is a statistical sampling technique that allows auditors to measure the amount or rate of error or deviation in a population by using quantitative methods. Variable sampling can provide precise and objective results by using mathematical formulas and confidence intervals4. Variable sampling does not provide any evidence of the validity and integrity of logs in an organization’s SIEM system by itself; it depends on the type and quality of the audit tests performed on the selected sample.

References:

Audit Testing Procedures - 5 Types and Their Use Cases

5 Types of Testing Methods Used During Audit Procedures | I.S. Partners

Stop-or-Go Sampling Definition

Variable Sampling Definition

A data loss prevention (DLP) tool is a software application that detects and prevents data breaches by monitoring and protecting sensitive data from unauthorized access, transfer, or use1. A DLP tool can help your organization comply with regulations, prevent insider threats, and protect your intellectual property.

Before implementing a DLP tool, the most important prerequisite is to identify where existing data resides and establish a data classification matrix. This is because you need to know what data you have, where it is stored, how sensitive it is, and who can access it. A data classification matrix is a framework that defines the categories and levels of data sensitivity, such as public, internal, confidential, or restricted2. By identifying and classifying your data, you can determine the appropriate DLP policies and controls to apply to each type of data and prevent data loss or leakage.

The IS auditor’s greatest concern when reviewing a business case for a proposed implementation of a third-party system should be A. Lack of ongoing maintenance costs. This is because ongoing maintenance costs are an essential part of the total cost of ownership (TCO) of a third-party system, and they can have a significant impact on the return on investment (ROI) and the feasibility of the project. If the business case does not include ongoing maintenance costs, it may underestimate the true cost of the project and overestimate the benefits. This could lead to poor decision making and unrealistic expectations.

Lack of training materials (B), lack of plan for pilot implementation ©, and lack of detailed work breakdown structure (D) are also potential issues that could affect the quality and success of the project, but they are not as critical as lack of ongoing maintenance costs. Training materials can be developed or acquired later, pilot implementation can be planned during the project initiation or planning phase, and work breakdown structure can be refined as the project progresses. However, ongoing maintenance costs are difficult to change or estimate once the project is approved and implemented, and they can have long-term implications for the organization. Therefore, they should be included and analyzed in the business case.

When assessing the scope of privacy concerns for an IT project, the most important factor to consider is the applicable laws and regulations. These laws and regulations define the legal requirements for data privacy and protection that the project must comply with. They can vary greatly depending on the jurisdiction and the type of data being processed, and non-compliance can result in significant penalties123. While data ownership, business requirements and data flows, and end-user access rights are also important considerations, they are typically guided by these legal requirements.

References: ISACA’s Information Systems Auditor Study Materials1

 The primary factor to determine system criticality is the maximum allowable downtime (MAD), which is the maximum period of time that a system can be unavailable before causing significant damage or risk to the organization. TheMAD reflects the business impact and the recovery requirements of the system, and it can be used to prioritize the systems and allocate the resources for disaster recovery planning. The other options are not as important as the MAD, and they may vary depending on the system characteristics and the recovery strategy. The recovery point objective (RPO) is the maximum amount of data loss that is acceptable for a system. The mean time to restore (MTTR) is the average time required to restore a system after a failure. The key performance indicators (KPIs) are metrics that measure the performance and effectiveness of a system. References: CISA Review Manual (Digital Version) 1, page 468-469.

QUESTIO NO. 704

Which of the following would provide the BEST evidence that a cloud provider's change management process is effective?

A. Minutes from regular change management meetings with the vendor

B. Written assurances from the vendor's CEO and CIO

C. The results of a third-party review provided by the vendor

D. A copy of change management policies provided by the vendor

Answer: C

The results of a third-party review provided by the vendor would provide the best evidence that a cloud provider’s change management process is effective, because it would be an independent and objective assessment of the vendor’s compliance with best practices and standards for managing changes in the cloud environment. A third-party review would also include testing of the vendor’s change management controls and procedures, and provide recommendations for improvement if needed.

Minutes from regular change management meetings with the vendor would not provide sufficient evidence, because they would only reflect the vendor’s self-reported information and may not capture all the changes that occurred or their impact on the cloud services. Written assurances from the vendor’s CEO and CIO would also not provide sufficient evidence, because they would be based on the vendor’s own opinion and may not be verified by external sources. A copy of change management policies provided by the vendor would not provide sufficient evidence, because it would only show the vendor’s intended approach to change management, but not how it is implemented or monitored in practice.

References:

ISACA Cloud Computing Audit Program, Section 4.5: Change Management

Cloud Computing: Business Benefits With Security, Governance and Assurance Perspectives, Section 4.3: Change Management