Splunk SPLK-3002 Exam With Confidence Using Practice Dumps

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/DeepDives, A deep dive is a dashboard that allows you to analyze the historical trends and anomalies of your KPIs and metrics in ITSI. A deep dive displays a timeline of events and swim lanes of data that you can customize and filter to investigate issues and perform root cause analysis. Some of the capabilities of deep dives are:, B. Visualizing one or more service KPIs values by time. This is true because you can add KPI swim lanes to a deep dive to show the values and severity levels of one or more KPIs over time. You can also compare KPIs from different services or entities using service swapping or entity splitting., C. Examining and comparing alert levels for KPIs in a service over time. This is true because you can add alert swim lanes to a deep dive to show the alert levels and counts for one or more KPIs over time. You can also drill down into the alert details and view the notable events associated with each alert., D. Comparing swim lane values for a slice of time. This is true because you can use the time range selector to zoom in or out of a specific time range in a deep dive. You can also use the time brush to select a slice of time and compare the swim lane values for that time period., The other option is not a capability of deep dives because:, A. Comparing a service’s notable events over a time period. This is not true because deep dives do not display notable events, which are alerts generated by ITSI based on certain conditions or correlations. Notable events are displayed in other dashboards, such as episode review or glass tables., References: [Overview of deep dives in ITSI], [Add swim lanes to a deep dive in ITSI], ]

[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], ]