SPLK-3002 Practice Test

Explanation:
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.

Explanation: 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.

Explanation: 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.

Reference: [Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/AboutMW, A is the correct answer because KPI searches still run during maintenance mode, but the results are buffered until the maintenance window is over. This means that no alerts are triggered during maintenance mode, but once it ends, the buffered results are processed and alerts are generated if necessary. You cannot create new KPIs or modify existing KPIs during maintenance mode.

Reference: [Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/MKA, A best practice for identifying the most effective services with which to start an iterative ITSI deployment is to analyze the business to determine the most critical services that have the most impact on revenue, customer satisfaction, or other key performance indicators. You can use the Service Analyzer to prioritize and monitor these services. References: Service Analyzer, , ]

Explanation:
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.