Download Splunk IT Service Intelligence Certified Admin.SPLK-3002.CertDumps.2024-08-04.40q.vcex

A base search is a search definition that can be shared across multiple KPIs that use the same data source. Base searches can improve search performance and reduce search load by consolidating multiple similar KPIs. One of the characteristics of base searches is that it is possible to filter to entities assigned to the service for calculating the metrics for the service's KPIs. This means that you can use entity filtering rules to specify which entities are relevant for each KPI based on the base search results.Reference:Create KPI base searches in ITSI, [Filter entities for KPIs based on base searches]

Create a glass table to visualize and monitor the interrelationships and dependencies across your IT and business services.The service swapping settings are saved and apply the next time you open the glass table.You can add metrics like KPIs, ad hoc searches, and service health scores that update in real time against a background that you design. Glass tables show real-time data generated by KPIs and services.The glass table editor is a tool that allows you to create and edit glass tables in ITSI. Some of the capabilities of the glass table editor are:Creating glass tables from scratch or from existing templates.Configuring service swapping on widgets to toggle displaying metrics from different services.Adding KPI metric lanes to glass tables to show historical trends of KPI values.The glass table editor does not support correlation search creation, which is a separate feature in ITSI that allows you to create searches that look for relationships between data points and generate notable events.Reference:Overview of the glass table editor in ITSI, [Configure service swapping on glass tables], [Add KPI metric lanes to glass tables], [Overview of correlation searches in ITSI]

An adaptive time threshold in the context of Splunk IT Service Intelligence (ITSI) refers to the capability of dynamically adjusting threshold values for Key Performance Indicators (KPIs) based on historical data trends and patterns. This feature allows thresholds to evolve as the 'normal' behavior of KPIs changes over time, ensuring that alerts remain relevant and reduce the likelihood of false positives or negatives. The advantage of this approach is that it accommodates for natural fluctuations in KPI values that may occur due to changes in business operations, seasonality, or other factors, without requiring manual threshold adjustments. This makes the monitoring system more resilient and responsive to actual conditions, improving the overall effectiveness of IT operations management.

An ITSI Glass Table provides a customizable, high-level view that can display a system's topology overlaid with real-time Key Performance Indicator (KPI) metrics and service health scores. This visualization tool allows users to create a visual representation of their IT infrastructure, applications, and services, integrating live data to monitor the health and performance of each component in context. The ability to overlay KPI metrics on the system topology enables IT and business stakeholders to quickly understand the operational status and health of various elements within their environment, facilitating more informed decision-making and rapid response to issues.

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.

A multi-KPI alert is a type of correlation search that is based on defined trigger conditions for two or more KPIs. When trigger conditions occur simultaneously for each KPI, the search generates a notable event. For example, you might create a multi-KPI alert based on two common KPIs: CPU load percent and web requests. A sudden simultaneous spike in both CPU load percent and web request KPIs might indicate a DDOS (Distributed Denial of Service) attack. Multi-KPI alerts can bring such trending behaviors to your attention early, so that you can take action to minimize any impact on performance. Multi-KPI alerts are useful for correlating the status of multiple KPIs across multiple services. They help you identify causal relationships, investigate root cause, and provide insights into behaviors across your infrastructure. The best use case for configuring a multi-KPI alert is to raise an alert when one or more KPIs indicate an outage is occurring, such as when the service health score drops below a certain threshold or when multiple KPIs have critical severity levels.Reference:Create multi-KPI alerts in ITSI