Pass the Splunk IT Service Intelligence Certified Admin SPLK-3002 Questions and answers with Dumpstech

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.

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.

To automatically create ServiceNow incidents when a Multi-KPI alert triggers in Splunk IT Service Intelligence (ITSI), the following approaches can be used:

C.By creating a notable event aggregation policy with a ServiceNow (SNOW) incident action:ITSI allows the creation of notable event aggregation policies that can specify actions to be taken when certain conditions are met. One of these actions can be the creation of an incident in ServiceNow, directly linking the alerting mechanism in ITSI with incident management in ServiceNow.

D.By editing the associated correlation search and specifying an alert action:Correlation searches in ITSI are used to identify patterns or conditions that signify notable events. These searches can be configured to include alert actions, such as creating a ServiceNow incident, whenever the search conditions are met. This direct integration ensures that incidents are automatically generated in ServiceNow, based on the specific criteria defined in the correlation search.

Options A and B are not standard practices for integrating ITSI with ServiceNow for automatic incident creation. The configuration typically involves setting up actionable alert mechanisms within ITSI that are specifically designed to integrate with external systems like ServiceNow.

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.

A realistic troubleshooting workflow in ITSI is:

B. Service Analyzer –> Notable Event Review –> Deep Dive

This workflow involves using the Service Analyzer dashboard to monitor the health and performance of your services and KPIs, using the Notable Event Review dashboard to investigate and manage the notable events generated by ITSI, and using the Deep Dive dashboard to analyze the historical trends and anomalies of your KPIs and metrics.

The other workflows are not realistic because they involve components that are not part of the troubleshooting process, such as correlation search, aggregation policy, and KPI. These components are used to create and configure the alerts and episodes that ITSI generates, not to investigate and resolve them. References: [Service Analyzer dashboard in ITSI], Overview of Episode Review in ITSI, [Overview of deep dives 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 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.

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