Splunk SPLK-3002 Exam With Confidence Using Practice Dumps

Reference: [Reference: https://newoutlook.it/download/book/splunk/advanced-splunk.pdf, When onboarding data into a Splunk index, assuming that ITSI will need to use this data, you should consider the following:, B. Check if the data could leverage pre-built KPIs from modules, then use the correct TA to onboard the data. This is true because modules are pre-packaged sets of services, KPIs, and dashboards that are designed for specific types of data sources, such as operating systems, databases, web servers, and so on. Modules help you quickly set up and monitor your IT services using best practices and industry standards. To use modules, you need to install and configure the correct technical add-ons (TAs) that extract and normalize the data fields required by the modules., The other options are not things you should consider because:, A. Use | stats functions in custom fields to prepare the data for KPI calculations. This is not true because using | stats functions in custom fields can cause performance issues and inaccurate results when calculating KPIs. You should use | stats functions only in base searches or ad hoc searches, not in custom fields., C. Make sure that all fields conform to CIM, then use the corresponding module to import related services. This is not true because not all modules require CIM-compliant data sources. Some modules have their own data models and field extractions that are specific to their data sources. You should check the documentation of each module to see what data requirements and dependencies they have., D. Plan to build as many data models as possible for ITSI to leverage. This is not true because building too many data models can cause performance issues and resource consumption in your Splunk environment. You should only build data models that are necessary and relevant for your ITSI use cases., References: Overview of modules in ITSI, [Install technical add-ons for ITSI modules], ]

Reference: [Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/AboutMW, A maintenance window is a period of time when a service or entity is undergoing maintenance operations or does not require active monitoring. It is a best practice to schedule maintenance windows with a 15- to 30-minute time buffer before and after you start and stop your maintenance work. This gives the system an opportunity to catch up with the maintenance state and reduces the chances of ITSI generating false positives during maintenance operations. For example, if a server will be shut down for maintenance at 1:00PM and restarted at 5:00PM, the ideal maintenance window is 12:30PM to 5:30PM. The 15- to 30-minute time buffer is a rough estimate based on 15 minutes being the time period over which most KPIs are configured to search data and identify alert triggers. References: Overview of maintenance windows in ITSI]

Reference: [Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/BaseSearch, A base search is a search definition that can be shared across multiple KPIs that use the same data source. Base searches can improve search performance and reduce search load by consolidating multiple similar KPIs. One of the characteristics of base searches is that it is possible to filter to entities assigned to the service for calculating the metrics for the service’s KPIs. This means that you can use entity filtering rules to specify which entities are relevant for each KPI based on the base search results. References: Create KPI base searches in ITSI, [Filter entities for KPIs based on base searches]]