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| Section | Weight | Objectives |
|---|---|---|
| ITSI Administration | 14% | - KPI base searches and aggregation - Backup and restore ITSI configuration - ITSI searches and scheduled searches - Managing services using deep dives |
| ITOA Administration | 10% | - Configuring ITOA - ITOA dashboard overview |
| ITSI Overview and Concepts | 8% | - Entities and services - ITSI product overview - ITSI key concepts and terminology |
| Glass Tables | 14% | - Configuring Glass Table elements - Creating Glass Tables - Entity Drilldown |
| ITSI Analytics | 12% | - Notable Events and aggregation policies - Glass Table thresholds - Configuring aggregation policies |
| Advanced Topics | 16% | - ITSI content management - Service-level availability reporting - Custom KPIs and calculations - ITSI and Splunk Enterprise Security integration |
| Service Discovery and Installation | 8% | - Creating adaptive threshold templates - Service Discovery overview - Service Discovery installation |
| ITSI Service Design | 18% | - Creating and configuring services - Entity templates and filters - Service dependencies and dependencies view - Service Health Score calculation |
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NEW QUESTION # 85
Which deep dive swim lane type does not require writing SPL?
Answer: D
Explanation:
Explanation
Among all the search configurations, automatic lane doesn't need to be written in Splunk Processing language.
NEW QUESTION # 86
Which of the following is a best practice when configuring maintenance windows?
Answer: A
Explanation:
It's 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.
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
NEW QUESTION # 87
Which index contains ITSI Episodes?
Answer: D
Explanation:
Reference:
B is the correct answer because ITSI episodes are stored in the itsi_grouped_alerts index. This index contains notable events that have been grouped together based on predefined aggregation policies. Episodes help you reduce alert noise and focus on resolving incidents faster. Reference: [Overview of episodes in ITSI]
NEW QUESTION # 88
ITSI Saved Search Scheduling is configured to use realtime_schedule = 0. Which statement is accurate about this configuration?
Answer: C
Explanation:
ITSI Saved Search Scheduling is a feature that allows you to schedule searches that run periodically to populate the data for your KPIs. You can configure various settings for your scheduled searches, such as the search frequency, the time range, the cron expression, and so on. One of the settings is realtime_schedule, which controls the way the scheduler computes the next execution time of a scheduled search. The statement that is accurate about this configuration is:
* B. If this value is set to 0, the scheduler bases its determination of the next scheduled search on the last search execution time. This is called continuous scheduling. If set to 0, the scheduler never skips scheduled execution periods. However, the execution of the saved search might fall behind depending on the scheduler's load. Use continuous scheduling whenever you enable the summary index option.
The other statements are not accurate because:
* A. If this value is set to 0, the scheduler bases its determination of the next scheduled search execution time on the current time. This is not true because this is what happens when the value is set to 1, not 0.
* C. If this value is set to 0, the scheduler may skip scheduled execution periods. This is not true because this is what happens when the value is set to 1, not 0.
* D. If this value is set to 0, the scheduler might skip some execution periods to make sure that the scheduler is executing the searches running over the most recent time range. This is not true because this is what happens when the value is set to 1, not 0.
References: Create KPI base searches in ITSI, Rrealtime_schedule in SavedSearches.conf
NEW QUESTION # 89
Which of the following is a characteristic of base searches?
Answer: A
Explanation:
Reference:
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. Reference: Create KPI base searches in ITSI, [Filter entities for KPIs based on base searches]
NEW QUESTION # 90
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