SPLK-3002 Splunk IT Service Intelligence Certified Admin Exam Question 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.

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

In Splunk IT Service Intelligence (ITSI), when using multiple event management policies, it is important to understand that policy processing is applied in a defined order. This order is crucial because it determines how events are processed and aggregated, and which rules are applied to events first. The order of policies can be customized, allowing administrators to prioritize certain policies over others based on the specific needs and operational logic of their IT environment. This feature provides flexibility in event management, enabling more precise control over event processing and ensuring that the most critical events are handled according to the desired precedence. This structured approach to policy processing helps in maintaining the efficiency and effectiveness of event management within ITSI.

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.

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.

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.

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.

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.

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), 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.

Splunk ITSI offers two built‑in anomaly detection algorithms:TrendingandEntity Cohesion. TheTrendingalgorithm works on theaggregate KPI series, comparing recent KPI behavior with its historical pattern to detect unusual trending patterns over time. It doesnotevaluate behavior across separate entities within the KPI split — it simply looks at deviations from historical trends in the combined KPI values. On the other hand, theEntity Cohesionalgorithm is specifically designed to detect when entities that are expected to behave similarly begin to diverge in behavior. When a KPI is split by entity (for example, multiple servers, locations, or service tiers), Entity Cohesion normalizes each entity’s time series and compares them against each other. If one entity’s pattern differs significantly from the group’s patterns, it is flagged as an anomaly. This matches the “paired monitoring requirement” of producing an alert whenone entity in the KPI is not behaving similarly to the other entities. The option describing entity cohesion paired with that requirement reflects the correct use case for this algorithm in ITSI. Neither trending anomaly detection nor entity cohesion anomaly detection is intended to detect multiple KPIs deviating at the service level — such cross‑KPI alerts are handled by other alerting constructs like multi‑KPI alerts or correlation searches, not these specific anomaly algorithms.

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

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.

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.

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.

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.

Splunk IT Service Intelligence (ITSI) is designed to align IT monitoring with the business outcomes that matter to stakeholders — especially executives who are focused on service performance and its impact on revenue, customer experience, and operational goals. In ITSI,entitiesrepresent the individual components that make up services and contribute to Key Performance Indicators (KPIs). Selecting the right entities for service modeling is critical: technical entities (like hosts or network interfaces) are useful for IT operations troubleshooting, but they don’t inherently represent business outcomes. Executive leadership cares about how incidents affect business capabilities and outcomes — such as sales performance, customer transactions, and channel health. Therefore, entities that reflectbusiness context(for example, store locations, product categories, or payment types) map IT issues directly to business performance indicators. When executives can see service health and incidents broken down by these business‑centric entities, they gainimpactful visibilityinto how issues affect revenue, customer interactions, and overall business operations. In contrast, purely technical entities such as hosts or network interfaces do not provide that business impact perspective, and demographic slices like season or customer age — while potentially valuable for marketing — don’t directly connect IT service health to business service performance in ITSI modeling.