SPLK-3002 Splunk IT Service Intelligence Certified Admin Exam Questions and Answers

Copy SA-IndexCreation to $SPLUNK_HOME/etc/apps/ on all individual indexers in your environment.

By default, impacting service health scores have an importance value of 11.

Create a glass table to visualize and monitor the interrelationships and dependencies across your IT and business services.

The service swapping settings are saved and apply the next time you open the glass table.

You can add metrics like KPIs, ad hoc searches, and service health scores that update in real time against a background that you design. Glass tables show real-time data generated by KPIs and services.

A is the correct answer because anomaly detection can be enabled on a KPI level in ITSI. Anomaly detection allows you to identify trends and outliers in KPI search results that might indicate an issue with your system. You can enable anomaly detection for a KPI by selecting one of the two anomaly detection algorithms in the KPI configuration panel. References: Apply anomaly detection to a KPI in ITSI

Among the anomaly detection algorithms included within Splunk IT Service Intelligence (ITSI), "Entity Cohesion" is a notable option. The Entity Cohesion algorithm is designed to detect anomalies by comparing the behavior of one entity against the collective behavior of a group of similar entities. This approach is particularly useful in scenarios where entities are expected to exhibit similar patterns of behavior under normal conditions. Anomalies are identified when an entity's metrics deviate significantly from the group norm, suggesting a potential issue with thatspecific entity. This method leverages the concept of cohesion among similar entities to enhance the accuracy and relevance of anomaly detection within ITSI environments.

D is the correct answer because teams allow you to restrict access to service content in UI views such as service analyzers, glass tables, deep dives, and episode review. Teams alsocontrol access to services and KPIs for editing and viewing purposes. Teams do not affect the ability to search against the itsi_summary index, restrict notable event alert actions, or restrict searches against the itsi_notable_audit index. References: Overview of teams in ITSI

ITSI provides a kvstore_to_json.py script that lets you backup/restore ITSI configuration data, perform bulk service KPI operations, apply time zone offsets for ITSI objects, and regenerate KPI search schedules.

When you run a backup job, ITSI saves your data to a set of JSON files compressed into a single ZIP file.

It's a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation.

When an episode warrants investigation, the analyst acknowledges the episode, which moves the status from New to In Progress.

Service templates in Splunk IT Service Intelligence (ITSI) are designed to streamline the creation of services by providing pre-defined configurations:

B.Service templates contain KPIs and KPI thresholds:This allows for the standardized deployment of services with predefined performance indicators and their associated thresholds, ensuring consistency across similar services.

C.Service templates can contain specific or generic entity rules:These rules define how entities are associated with services created from the template, allowing for both broad and targeted applicability.

While service templates contain configurations for KPIs, thresholds, and entity rules, the ability to modify templates after services have been instantiated from them is limited. Changes to a template do not retroactively affect services already created from that template. Moreover, service templates do not inherently contain domain-specific dashboards or deep dives; these are created separately within ITSI.

You might need to increase the hardware specifications of your own Enterprise Security deployment above the minimum hardware requirements depending on your environment.

Install Splunk Enterprise Security on a dedicated search head or search head cluster.

The Splunk platform uses indexers to scale horizontally. The number of indexers required in an Enterprise Security deployment varies based on the data volume, data type, retention requirements, search type, and search concurrency.

SA-IndexCreation is required on all indexers. For non-clustered, distributed environments, copy SA-IndexCreation to $SPLUNK_HOME/etc/apps/ on individual indexers.

The IT Service Intelligence (ITSI) metrics summary index, itsi_summary_metrics, is a metrics-based summary index that stores KPI data.

In Splunk IT Service Intelligence (ITSI), notable event groups are used to logically group related notable events, which enhances the manageability and analysis of events:

A.Notable event groups combine independent notable events:This characteristic allows for the aggregation of related events into a single group, making it easier for users to manage and investigate related issues. By grouping events, users can focus on the broader context of an issue rather than getting lost in the details of individual events.

While notable event groups play a critical role in organizing and managing events in ITSI, they do not inherently allow users to adjust threshold settings, which is typically handled at the KPI or service level. Additionally, while notable event groups are utilized within the ITSI framework, the statement that they are created in the 'itsi_tracked_alerts' index might not fully capture the complexity of how event groups are managed and stored within the ITSI architecture.

In Splunk IT Service Intelligence (ITSI), when a Key Performance Indicator (KPI) aggregate value is calculated, thetstatsfunction is often called. Thetstatsfunction in Splunk is used for rapid statistical queries over large volumes of data, which is particularly useful in ITSI for efficiently calculating aggregate values of KPIs across potentially vast datasets. This function allows for quick aggregation and summarization of indexed data, which is essential for monitoring andanalyzing the performance metrics that KPIs represent in ITSI. Unlike thestatscommand, which operates on already retrieved events,tstatsworks directly on indexed data, providing faster performance especially when dealing with high volumes of data typical in an IT environment. Thetstatscommand is therefore fundamental in the backend processing of ITSI for calculating aggregate values of KPIs, enabling real-time and historical analysis of service health and performance.

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.

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.

References: Create time-based static KPI thresholds in ITSI

In Splunk IT Service Intelligence (ITSI), creating teams for each department and assigning services to those teams is an effective way to segregate data and ensure that information remains private between different groups within an organization. Teams in ITSI provide a mechanism for role-based access control, allowing administrators to define which users or groups have access to specific services, KPIs, and dashboards. By setting up teams corresponding to each department and then assigning services to these teams, ITSI canaccommodate multi-departmental use within the same instance while maintaining strict access controls. This ensures that each department can only view and interact with the data and services relevant to their operations, preserving confidentiality and data integrity across the organization.