Splunk SPLK-3002 Dumps

Define entities before creating services. When you configure a service, you can specify entity matching rules based on entity aliases that automatically add the entities to your service.

Notable events in Splunk IT Service Intelligence (ITSI) are primarily generated through scheduled correlation searches. These searches are designed to monitor data for specific conditions or patterns defined by the ITSI administrator, and when these conditions are met, a notable event is created. These correlation searches are often linked to specific services or groups of services, allowing for targeted monitoring and alerting based on the operational needs of those services. This mechanism enables ITSI to provide timely and relevant alerts that can be further investigated and managed through the Episode Review dashboard, facilitating efficient incident response and management within the IT environment.

 D is the correct answer because ITSI provides multiple ways to delete multiple duplicate entities. You can use a CSV upload to overwrite existing entities with new or updated information, or delete them by setting the action field to delete. You can also use the entity lister page to select multiple entities and delete them in bulk. Alternatively, you can use a search command called | deleteentity to delete entities that match certain criteria. References: Create and update entities using a CSV file in ITSI, Delete entities in bulk in ITSI, Delete entities using the | deleteentity command in ITSI

When planning and designing a service tree in Splunk ITSI, the focus is onunderstanding how services, components, and dependencies relate to each otherso that ITSI can model service health, impact, and business relevance accurately. A technical diagram of the application and its interconnections provides direct insight into thecomponents and dependency relationshipsthat must be included in the service tree. A report of historical incidents and associated root causes offers valuable context for definingwhich components are critical, where failures have occurred, and how issues propagate, making it highly relevant to service modeling. A service topology from an IT Service Management (ITSM) tool also directly informs the modeling process by showingconfigured relationships between infrastructure and services, which can be imported or referenced to build accurate service trees. In contrast, anorganizational chart of the companyshows the reporting relationships and team structure within the organization — useful for human resource planning or escalation paths, but not directly useful for service tree design in ITSI. Organizational charts do not provide information about application components, their runtime connections, or how failures affect service delivery, making them theleast usefulmaterial when designing a service tree for technical and service health purposes. Therefore, company org charts are generally not relevant to ITSI service modeling.

Throttling applies to any correlation search alert type, including notable events and actions (RSS feed, email, run script, and ticketing).

Provide a value to filter the service to a specific set of entities. These entity rule values are meant to be custom for each service.

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.

C. New KPIs. This is true because when you add new KPIs to a service template, they will be automatically added to all the services that are linked to that template. This helps you keep your services consistent and up-to-date with the latest KPI definitions.

The other options will not be added to linked services by default because:

A. Thresholds. This is not true because when you change thresholds in a service template, they will not affect the existing thresholds in the linked services. You need to manually apply the threshold changes to each linked service if you want them to inherit the new thresholds from the template.

B. Entity rules. This is not true because when you change entity rules in a service template, they will not affect the existing entity rules in the linked services. You need to manually apply the entity rule changes to each linked service if you want them to inherit the new entity rules from the template.

D. Health score. This is not true because when you change health score settings in a service template, they will not affect the existing health score settings in the linked services. You need to manually apply the health score changes to each linked service if you want them to inherit the new health score settings from the template.

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.

Creating a custom module in Splunk IT Service Intelligence (ITSI) is particularly beneficial for the purpose of migrating KPI base searches and related visualizations to other ITSI installations. Custom modules can encapsulate a set of configurations, searches, and visualizations that are tailored to specific monitoring needs or environments. By packaging these elements into a module, it becomes easier to transfer, deploy, and maintain consistency across different ITSI instances. This modularity supports the reuse of developed components, simplifying the process of scaling and replicating monitoring setups in diverse operational contexts. The ability to migrate these components seamlessly enhances operational efficiency and ensures that best practices and custom configurations can be shared across an organization's ITSI deployments.

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

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. References: Configure adaptive thresholding for a KPI in ITSI

When setting up a new aggregation policy in Splunk IT Service Intelligence (ITSI), one of the crucial components is defining the filtering criteria. This aspect of the aggregation policy determines which events should be included in the aggregation based on specific conditions or attributes. The filtering criteria can be based on various event fields such as severity, source, event type, and other custom fields relevant to the organization's monitoring strategy. By specifying the filtering criteria, ITSI administrators can ensure that the aggregation policy is applied only to the pertinent events, thus facilitating more targeted and effective event management and reducing noise in the operational environment. This helps in organizing and prioritizing events more efficiently, enhancing the overall incident management process within ITSI.

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

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

Deep dives in Splunk IT Service Intelligence (ITSI) allow for an in-depth analysis of services and their KPIs over time, providing a detailed view of the operational health and performance trends. One of the powerful actions that can be performed with a deep dive is the creation of a Multi-KPI alert from the deep dive's current state. This functionality enables users to define alerts based on the complex conditions observed during the deep dive analysis, allowing for the early detection of similar situations in the future. By configuring a Multi-KPI alert directly from a deep dive, ITSI users can leverage their insights and observations to proactively monitor for patterns or conditions that may indicate potential service degradation or failure, enhancing the overall responsiveness and effectiveness of the IT monitoring strategy.

To install Splunk IT Service Intelligence (ITSI) on a single Search Head, one of the straightforward methods is to use the Splunk Web interface, specifically the "Manage Apps" dashboard, to download and install ITSI. This method is user-friendly and does not require manual file handling or command-line operations. By navigating to "Manage Apps" in the Splunk Web interface, users can find ITSI in the app repository or upload the ITSI installation package if it has been downloaded previously. From there, the installation process is initiated through the Splunk Web interface, simplifying the setup process. This approach ensures that the installation follows Splunk's standard app installation procedures, helping to avoid common installation errors and ensuring that ITSI is correctly integrated into the Splunk environment.

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.

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

A Multi-KPI alert in Splunk IT Service Intelligence (ITSI) is designed to trigger based on the conditions of multiple Key Performance Indicators (KPIs). This type of alert is particularly useful when a single KPI's state is not sufficient to indicate an issue, but the correlation between multiple KPIs can provide a clearer picture of an emerging problem. The best use case for a Multi-KPI alert is therefore when comparing the values of two or more KPIs indicates an unusual condition is occurring. This allows for more nuanced and context-rich alerting mechanisms that can identify complex issues not detectable by monitoring individual KPIs. This approach is beneficial in complex environments where the interplay between different performance metrics needs to be considered to accurately detect and diagnose issues.

By default, notable event metadata is archived after six months to keep the KV store from growing too large.

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), the Service Health Score is a metric that provides a quantifiable measure of the overall health and performance of a service. The score ranges from 0 to 100, with higher scores indicating better health. The range for a normal Service Health score category is typically from 80 to 100. Scores within this range suggest that the service is performing well, with no significant issues affecting its health. This categorization helps IT and business stakeholders quickly assess the operational status of their services, enabling them to focus on services that may require attention or intervention due to lower health scores.

In Splunk IT Service Intelligence (ITSI), entities represent infrastructure components, applications, or other elements that are monitored. Each entity is uniquely identified by its entity ID, and entities can be associated with one or more services through the concept of aliases. A problem arises when two or more entities have the same value in a single alias field because aliases are used to match events to entities in ITSI. If multiple entities share the same alias value, ITSI might incorrectly associate data with the wrong entity, leading to inaccurate monitoring and analytics. This scenario requires correction to ensure that each alias uniquely identifies a single entity, thereby maintaining the integrity of the monitoring and analysis process within ITSI. The uniqueness of service IDs, entity IDs, and entity key values in info fields is also important but does not typically present the same level of issue as duplicate values in an alias field.