Free Splunk SPLK-3002 Practice Exam with Questions & Answers | Set: 2

In Splunk IT Service Intelligence (ITSI), a KPI widget on a glass table can be configured to drill down into a variety of destinations based on the needs of the user and the design of the glass table. This flexibility allows users to dive deeper into the data or analysis represented by the KPI widget, providing context and additional insights. The destinations for drill-downs from a KPI widget can include:

A. Another glass table, offering a different perspective or more detailed view related to the KPI. B. A Splunk dashboard that provides broader analysis or incorporates data from multiple sources. C. A custom deep dive for in-depth, time-series analysis of the KPI and related metrics.

This versatility makes KPI widgets powerful tools for navigating through the wealth of operational data and insights available in ITSI, facilitating effective monitoring and decision-making.

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.

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.

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.

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]

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.

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.