SPLK-3002 Questions and Answers

During the data audit phase of requirements gathering for Splunk IT Service Intelligence (ITSI), it's crucial to identify and record the meta-data for entities, focusing on information (info) and aliases. This step involves understanding and documenting the key attributes and identifiers that describe each entity, such as host names, IP addresses, device types, or other relevant characteristics. These attributes are used to categorize and uniquely identify entities within ITSI, enabling more effective mapping of data to services and KPIs. By meticulously recording this meta-data, organizations ensure that their ITSI implementation is aligned with their specific monitoring needs and infrastructure, facilitating accurate service modeling and event management. This practice is foundational for setting up ITSI to reflect the actual IT environment, enhancing the relevance and effectiveness of the monitoring and analysis capabilities.

For Entity Cohesion anomaly detection in Splunk IT Service Intelligence (ITSI), the minimum number of entities a KPI must be split by is 2. Entity Cohesion as a method of anomaly detection focuses on identifying anomalies based on the deviation of an entity's behavior in comparison to other entities within the same group or cohort. By requiring a minimum of only two entities, ITSI allows for the comparison of entities to detect significant deviations in one entity's performance or behavior, which could indicate potential issues. This method leverages the idea that entities performing similar functions or within the same service should exhibit similar patterns of behavior, and significant deviations could be indicative of anomalies. The low minimum requirement of two entities ensures that this powerful anomaly detection feature can be utilized even in smaller environments.

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. 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.

In Splunk IT Service Intelligence (ITSI), default deep dives are auto-generated and can be accessed via the Service Analyzer. Deep dives are an essential feature of ITSI that provide an in-depth, granular view into the health and performance of services and their associated KPIs. These default deep dives are automatically created for each service, allowing users to quickly drill down into the detailed operational metrics and performance data of their services. By accessing these deep dives through the Service Analyzer, ITSI users can efficiently investigate issues, understand service dependencies, and make informed decisions to maintain optimal service health. The auto-generated nature of these default deep dives simplifies the monitoring and analysis process, providing immediate insights into service performance without the need for manual setup or configuration.

In Splunk IT Service Intelligence (ITSI), teams are used to organize services, KPIs, and other objects within ITSI to facilitate access control and management:

B.Services should be assigned to the 'global' team if all users need access to it:The 'global' team in ITSI is a built-in concept that denotes universal accessibility. Assigning services to the 'global' team makes them accessible to all ITSI users, irrespective of their specific team memberships. This is useful for services that are relevant across the entire organization.

C.By default, all services are owned by the built-in 'global' team and administered by the 'itoa_admin' role:This default setting ensures that upon creation, services are accessible to administrators and can be further re-assigned or refined for access by specific teams as needed.

D.A new team admin role should be created for each team. The new role should inherit the 'itoa_team_admin' role:This best practice allows for granular access control and management within teams. Each team can have its own administrators with the appropriate level of access and permissions tailored to the needs of that team, derived from the capabilities of the 'itoa_team_admin' role.

The concept of adding 'itoa admin roles' with read-only permissions contradicts the typical use case for administrative roles, which usually require more than read-only access to manage services and entities effectively.

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.

In Splunk IT Service Intelligence (ITSI), administrators can manually control the grouping of notable events using aggregation policies. Aggregation policies allow for the definition of criteria based on which notable events are grouped together. This includes configuring rules based on event fields, severity, source, or other event attributes. Through these policies, administrators can tailor the event grouping logic to meet the specific needs of their environment, ensuring that related events are grouped in a manner that facilitates efficient analysis and response. This feature is crucial for managing the volume of events and focusing on the most critical issues by effectively organizing related events into manageable groups.

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

In the Notable Events Review dashboard within Splunk IT Service Intelligence (ITSI), when working with a notable event group, users can set or adjust certain attributes at the individual event level or at the group level. These attributes include:

Severity:The importance or impact level of the notable event or group, which can be adjusted to reflect the current assessment of the situation.

Status:The current state of the notable event or group, such as "New," "In Progress," or "Resolved," indicating the progress in addressing the event or group.

Owner:The user or team responsible for managing and resolving the notable event or group.

These settings allow for effective management and tracking of notable events, ensuring that they are appropriately prioritized, acted upon, and resolved by the responsible parties.

In the context of troubleshooting KPI search performance in Splunk IT Service Intelligence (ITSI), the search names in the job activity that identify base searches typically follow the pattern "Indicator - Shared - xxxx - ITSI Search." These base searches are fundamental components of the KPI calculation process, aggregating and preparing data for further analysis by KPIs. Identifying these base searches in the job activity is crucial for diagnosing performance issues, as these searches can be resource-intensive and impact overall system performance. Understanding the naming convention helps administrators and analysts quickly pinpoint the base searches related to specific KPIs, facilitating more effective troubleshooting and optimization of search performance within the ITSI environment.

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 also control 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

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 and analyzing 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.

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

One of the recommended best practices for Splunk IT Service Intelligence (ITSI) installation is to avoid installing ITSI on search heads that already have Splunk Enterprise Security (ES) installed. This recommendation stems from potential resource conflicts and performance issues that can arise when both resource-intensive applications are deployed on the same instance. Both ITSI and ES are complex applications that require significant system resources to function effectively, and running them concurrently on the same search head can lead to degraded performance, conflicts in resource allocation, and potential stability issues. It's generally advised to segregate these applications onto separate Splunk instances to ensure optimal performance and stability for both platforms.

1. From the ITSI main menu, click Configuration > Notable Event Aggregation Policies.

2. Select a custom policy or the Default Policy.

3. Under Smart Mode grouping, enable Smart Mode.

4. Click Select fields. A dialog displays the fields found in your notable events from the last 24 hours.

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 that specific entity. This method leverages the concept of cohesion among similar entities to enhance the accuracy and relevance of anomaly detection within ITSI environments.

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

To utilize Adaptive Thresholding in Splunk IT Service Intelligence (ITSI), the minimum requirement for a set of Key Performance Indicator (KPI) data is that it must be at least 7 days old. Adaptive Thresholding uses historical data to dynamically adjust thresholds based on observed patterns and trends. Having a minimum of 7 days worth of data allows the system to analyze a sufficient amount of information to identify normal ranges and variances in KPI behavior, thereby setting more accurate and contextually relevant thresholds. This requirement ensures that the adaptive thresholds are based on a meaningful data set that reflects the typical operational conditions of the monitored services.