Splunk SPLK-3002 Practice Test - Questions Answers, Page 7

In Splunk IT Service Intelligence (ITSI), glass tables are fully customizable dashboards that provide a visual representation of an organization's IT environment, along with the health and status of services and KPIs. Unlike some pre-configured views or dashboards that might come with default setups in various platforms, ITSI does not provide default glass tables out of the box. Instead, users are encouraged to create their own glass tables tailored to their specific monitoring needs and operational views. This approach ensures that each organization can design glass tables that best represent their unique infrastructure, applications, and service landscapes, providing a more personalized and relevant operational overview.

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.

One of the recommended best practices for Splunk IT Service Intelligence (ITSI) installation is to avoid installing ITSI on search heads that already have Splunk Enterprise Security (ES) installed. This recommendation stems from potential resource conflicts and performance issues that can arise when both resource-intensive applications are deployed on the same instance. Both ITSI and ES are complex applications that require significant system resources to function effectively, and running them concurrently on the same search head can lead to degraded performance, conflicts in resource allocation, and potential stability issues. It's generally advised to segregate these applications onto separate Splunk instances to ensure optimal performance and stability for both platforms.

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.

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.

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.

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.

To install Splunk IT Service Intelligence (ITSI) on a single Search Head, one of the straightforward methods is to use the Splunk Web interface, specifically the 'Manage Apps' dashboard, to download and install ITSI. This method is user-friendly and does not require manual file handling or command-line operations. By navigating to 'Manage Apps' in the Splunk Web interface, users can find ITSI in the app repository or upload the ITSI installation package if it has been downloaded previously. From there, the installation process is initiated through the Splunk Web interface, simplifying the setup process. This approach ensures that the installation follows Splunk's standard app installation procedures, helping to avoid common installation errors and ensuring that ITSI is correctly integrated into the Splunk environment.

When an anomaly is detected in Splunk IT Service Intelligence (ITSI), it typically generates a notable event that can be reviewed and managed in the Episode Review dashboard. The Episode Review is part of ITSI's Event Analytics framework and serves as a centralized location for reviewing, annotating, and managing notable events, including those generated by anomaly detection. This process enables IT operators and analysts to efficiently identify, prioritize, and respond to potential issues highlighted by the anomaly alerts. The integration of anomaly alerts into the Episode Review dashboard streamlines the workflow for managing and investigating these alerts within the broader context of IT service management and operational intelligence.