SPLK-3002 Exam Dumps

Explanation: In the context of Smart Mode configuration within Splunk IT Service Intelligence (ITSI), the two settings that control how fields affect grouping are "Text similarity" and "Category similarity." Smart Mode is a feature used in event grouping that leverages machine learning to automatically group related events. "Text similarity" refers to how closely the textual content of event fields must match for those events to be grouped together, taking into account commonalities in strings or narratives within the event data. "Category similarity," on the other hand, relates to the similarity in the categorical attributes of events, such as event types or source types, which helps in clustering events that are similar in nature or origin. Both of these settings are crucial in determining how events are grouped in ITSI, influencing the granularity and relevance of the event groupings based on textual and categorical similarities.

Explanation:

When creating a custom deep dive, services or KPIs that are in maintenance mode are shown in gray color in the topology view. This indicates that they are not actively monitored and do not generate alerts or notable events.

References:

Deep Dives

Explanation: B is the correct answer because adaptive thresholding is a feature of ITSI that allows you to dynamically adjust KPI thresholds based on historical patterns and trends. Adaptive thresholding requires a time buffer of at least 15 minutes to calculate the thresholds based on the previous data points. The time buffer ensures that there is enough data to perform the calculations and avoid false positives or negatives.

Explanation:
KPI base searches let you share a search definition across multiple KPIs in IT Service Intelligence (ITSI). Create base searches to consolidate multiple similar KPIs, reduce search load, and improve search performance.
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. The statement that accurately describes base searches used for KPIs in a service is:
A. Base searches can be used for multiple services. This means that you can create a base search for a service and use it for other services that have similar data sources and KPIs. For example, if you have multiple services that monitor web server performance, you can create a base search that queries the web server logs and use it for all the services that need to calculate KPIs based on those logs.

Explanation: Time policies are user-defined threshold values to be used at different times of the day or week to account for changing KPI workloads. Time policies accommodate normal variations in usage across your services and improve the accuracy of KPI and service health scores. For example, if your organization’s peak activity is during the standard work week, you might create a KPI threshold time policy that accounts for higher levels of usage during work hours, and lower levels of usage during off-hours and weekends. The statement that applies when configuring time policies for KPI thresholds is:
B. They are great if you expect normal behavior at 1:00 to be different than normal behavior at 5:00. This is true because time policies allow you to define different thresholdvalues for different time blocks, such as AM/PM, work hours/off hours, weekdays/weekends, and so on. This way, you can account for the expected variations in your KPI data based on the time of day or week.
The other statements do not apply because:
A. A person can only configure 24 policies, one for each hour of the day. This is not true because you can configure more than 24 policies using different time block combinations, such as 3 hour block, 2 hour block, 1 hour block, and so on.
C. If a person expects a KPI to change significantly through a cycle on a daily basis, don’t use it. This is not true because time policies are designed to handle KPIs that change significantly through a cycle on a daily basis, such as web traffic volume or CPU load percent.
D. It is possible for multiple time policies to overlap. This is not true because you can only have one active time policy at any given time. When you create a new time policy, the previous time policy is overwritten and cannot be recovered.

Explanation: When an anomaly is detected in Splunk IT Service Intelligence (ITSI), it typically generates a notable event that can be reviewed and managed in the Episode Review dashboard. The Episode Review is part of ITSI's Event Analytics framework and serves as a centralized location for reviewing, annotating, and managing notable events, including those generated by anomaly detection. This process enables IT operators and analysts to efficiently identify, prioritize, and respond to potential issues highlighted by the anomaly alerts. The integration of anomaly alerts into the Episode Review dashboard streamlines the workflow for managing and investigating these alerts within the broader context of IT service management and operational intelligence.