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| Section | Weight | Objectives |
|---|---|---|
| Services and KPIs | 15% | - Use entities in KPI searches - Configure KPI thresholds and alerts - Define services and KPIs - Manage service dependencies |
| Event Analytics | 5% | - Describe Event Analytics features - Configure and use Event Analytics |
| Anomaly Detection | 5% | - Work with anomaly events - Enable anomaly detection |
| Glass Tables | 5% | - Describe glass tables - Design glass tables - Use glass tables - Configure glass tables |
| Correlation and Multi-KPI Searches | 5% | - Create multi-KPI alerts - Manage notable event storage - Define correlation searches |
| Troubleshooting ITSI | 10% | - Diagnose common issues - Resolve configuration and operational problems - Monitor ITSI performance |
| Managing Notable Events | 10% | - Define key notable events terms and relationships - Describe multi-KPI alerts - Describe notable events workflow - Work with notable events - Customize notable event views |
| Deep Dives | 10% | - Use default deep dives - Describe deep dive concepts - Create and customize deep dives |
| Aggregation Policies | 5% | - Create aggregation policies - Use smart mode aggregation |
| Introducing ITSI | 5% | - Identify what ITSI does - Examine the ITSI user interface - Describe reasons for using ITSI |
| Access Control and Security | 5% | - Create service-level teams - Configure user roles and permissions |
| ITSI Architecture and Deployment | 10% | - Describe ITSI architecture - Manage ITSI modules - Plan and design deployment |
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NEW QUESTION # 31
Which of the following is a characteristic of custom deep dives?
Answer: B
Explanation:
Custom deep dives in Splunk IT Service Intelligence (ITSI) are versatile and highly customizable dashboards that allow users to analyze various types of data in a unified view. One of the key characteristics of custom deep dives is their ability to combine lanes of different data types, such as metrics, events, Key Performance Indicators (KPIs), and service health scores. This multifaceted approach provides a comprehensive and layered view of the IT environment, enabling analysts and operators to correlate different data types and gain deeper insights into the health and performance of services. By incorporating these diverse data lanes, custom deep dives facilitate a more holistic understanding of the operational landscape, aiding in more effective troubleshooting and decision-making.
NEW QUESTION # 32
Which of the following items describe ITSI teams? (select all that apply)
Answer: A,C,D
Explanation:
In Splunk IT Service Intelligence (ITSI), teams are used to organize services, KPIs, and other objects within ITSI to facilitate access control and management:
B) Services should be assigned to the 'global' team if all users need access to it: The 'global' team in ITSI is a built-in concept that denotes universal accessibility. Assigning services to the 'global' team makes them accessible to all ITSI users, irrespective of their specific team memberships. This is useful for services that are relevant across the entire organization.
C) By default, all services are owned by the built-in 'global' team and administered by the 'itoa_admin' role: This default setting ensures that upon creation, services are accessible to administrators and can be further re-assigned or refined for access by specific teams as needed.
D) A new team admin role should be created for each team. The new role should inherit the 'itoa_team_admin' role: This best practice allows for granular access control and management within teams. Each team can have its own administrators with the appropriate level of access and permissions tailored to the needs of that team, derived from the capabilities of the 'itoa_team_admin' role.
The concept of adding 'itoa admin roles' with read-only permissions contradicts the typical use case for administrative roles, which usually require more than read-only access to manage services and entities effectively.
NEW QUESTION # 33
Which scenario would benefit most by implementing ITSI?
Answer: B
Explanation:
Reference:
Splunk IT Service Intelligence (ITSI) is a monitoring and analytics solution that uses artificial intelligence and machine learning to provide insights into the health and performance of IT services. ITSI lets you create services that represent the critical components of your IT infrastructure, such as applications, databases, servers, networks, and so on. You can then monitor the status and performance of these services using key performance indicators (KPIs), which are metrics that measure aspects of service health, such as availability, latency, error rate, and so on. ITSI also provides tools for visualizing, investigating, and alerting on service issues, such as service analyzers, glass tables, deep dives, episode review, and so on. The scenario that would benefit most by implementing ITSI is monitoring of business service functionality, because ITSI enables you to measure and improve the quality and reliability of your IT services and align them with your business objectives. Reference: What is Splunk IT Service Intelligence?
NEW QUESTION # 34
Besides creating notable events, what are the default alert actions a correlation search can execute? (Choose all that apply.)
Answer: A,B,D
Explanation:
Explanation
Throttling applies to any correlation search alert type, including notable events and actions (RSS feed, email, run script, and ticketing).
NEW QUESTION # 35
To use Adaptive Threshholding, what is the minimum requirement for a set of KPI data?
Answer: A
Explanation:
To utilize Adaptive Thresholding in Splunk IT Service Intelligence (ITSI), the minimum requirement for a set of Key Performance Indicator (KPI) data is that it must be at least 7 days old. Adaptive Thresholding uses historical data to dynamically adjust thresholds based on observed patterns and trends. Having a minimum of
7 days worth of data allows the system to analyze a sufficient amount of information to identify normal ranges and variances in KPI behavior, thereby setting more accurate and contextually relevant thresholds. This requirement ensures that the adaptive thresholds are based on a meaningful data set that reflects the typical operational conditions of the monitored services.
NEW QUESTION # 36
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