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

To identify that a memory usage KPI is going critical, an analyst can leverage multiple views within Splunk IT Service Intelligence (ITSI), each offering a different perspective or level of detail:

A.Memory KPI in a glass table:A glass table can display the current status of the memory usage KPI, along with other related KPIs and services, providing a high-level overview of system health.

B.Memory panel of the OS Host Details view in the Operating System module:This specific panel within the OS Host Details view offers detailed metrics and trends related to memory usage, allowing for in-depth analysis.

C.Memory swim lane in a Deep Dive:Deep Dives allow analysts to visually track the performance and status of KPIs over time. A swim lane dedicated to memory usage can highlight periods where the KPI goes critical, along with the context of other related KPIs.

D.Service & KPI tiles in the Service Analyzer:The Service Analyzer provides a comprehensive overview of all services and their KPIs. The tiles related to memory usage can quickly alert analysts to critical conditions through color-coded indicators.

Each of these views contributes to a comprehensive monitoring strategy, enabling analysts to detect and respond to critical memory usage conditions from various analytical perspectives.

In Splunk IT Service Intelligence (ITSI), notable events are created and managed within the context of its Event Analytics framework. These notable events are stored in theitsi_tracked_alertsindex. This index is specifically designed to hold the active notable events that are generated by ITSI's correlation searches, which are based on the conditions defined for various services and their KPIs. Notable events are essentially alerts or issues that need to be investigated and resolved. Theitsi_tracked_alertsindex enables efficient storage, querying, and management of these events, facilitating the ITSI's event management and review process. The other options, such asitsi_notable_archiveanditsi_notable_audit, serve different purposes, such as archiving resolved notable events and auditing changes to notable event configurations, respectively. Therefore, the correct answer for where active notable events are stored is theitsi_tracked_alertsindex.

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.

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.

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.

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

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.

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.

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.

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 isbeneficial in complex environments where the interplay between different performance metrics needs to be considered to accurately detect and diagnose issues.

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.

A KPI lane is a type of deep dive swim lane that does not require writing SPL. You can simply select a service and a KPI from a drop-down list and ITSI will automatically populate the lane with the corresponding data. You can also adjust the threshold settings and time range for the KPI lane. References: [KPI Lanes]

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

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.

Install SA-ITSI-Licensechecker and SA-UserAccess on any license master in a distributed or search head cluster environment. If a search head in your environment is also a license master, the license master components are installed when you install ITSI on the search heads.

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

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

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.

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 orattributes. 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.

In the context of Splunk IT Service Intelligence (ITSI), a Business Service often has Key Performance Indicators (KPIs) but might not have directly associated entities. Business Services represent high-level aggregations of organizational functions or processes and are typically measured by KPIs that reflect the performance of underlying technical services or components rather than direct infrastructure entities. For example, a Business Service might monitor overall transaction completion times or customer satisfaction scores, which are abstracted from the specific technical entities that underlie these metrics. This abstraction allows Business Services to provide a business-centric view of IT health and performance, focusing on outcomes rather than specific technical components.