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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Glass Tables | 14% | - Creating Glass Tables - Entity Drilldown - Configuring Glass Table elements |
| Topic 2: ITSI Administration | 14% | - Managing services using deep dives - ITSI searches and scheduled searches - Backup and restore ITSI configuration - KPI base searches and aggregation |
| Topic 3: ITSI Service Design | 18% | - Service dependencies and dependencies view - Creating and configuring services - Entity templates and filters - Service Health Score calculation |
| Topic 4: ITOA Administration | 10% | - Configuring ITOA - ITOA dashboard overview |
| Topic 5: ITSI Overview and Concepts | 8% | - ITSI key concepts and terminology - Entities and services - ITSI product overview |
| Topic 6: Advanced Topics | 16% | - ITSI content management - ITSI and Splunk Enterprise Security integration - Service-level availability reporting - Custom KPIs and calculations |
| Topic 7: Service Discovery and Installation | 8% | - Creating adaptive threshold templates - Service Discovery overview - Service Discovery installation |
| Topic 8: ITSI Analytics | 12% | - Notable Events and aggregation policies - Configuring aggregation policies - Glass Table thresholds |
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NEW QUESTION # 65
Which anomaly detection algorithm fulfills the paired monitoring requirement?
Answer: A
Explanation:
Splunk ITSI offers two built#in anomaly detection algorithms:TrendingandEntity Cohesion. TheTrending algorithm 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, the Entity 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.
NEW QUESTION # 66
For which ITSI function is it a best practice to use a 15-30 minute time buffer?
Answer: D
Explanation:
B is the correct answer because adaptive thresholding is a feature of ITSI that allows you to dynamically adjust KPI thresholds based on historical patterns and trends. Adaptive thresholding requires a time buffer of at least 15 minutes to calculate the thresholds based on the previous data points. The time buffer ensures that there is enough data to perform the calculations and avoid false positives or negatives. Reference: Configure adaptive thresholding for a KPI in ITSI
NEW QUESTION # 67
Which of the following describes a way to delete multiple duplicate entities in ITSI?
Answer: D
Explanation:
D is the correct answer because ITSI provides multiple ways to delete multiple duplicate entities. You can use a CSV upload to overwrite existing entities with new or updated information, or delete them by setting the action field to delete. You can also use the entity lister page to select multiple entities and delete them in bulk. Alternatively, you can use a search command called | deleteentity to delete entities that match certain criteria. Reference: Create and update entities using a CSV file in ITSI, Delete entities in bulk in ITSI, Delete entities using the | deleteentity command in ITSI
NEW QUESTION # 68
Which of the following describes default deep dives?
Answer: D
Explanation:
In Splunk IT Service Intelligence (ITSI), default deep dives are auto-generated and can be accessed via the Service Analyzer. Deep dives are an essential feature of ITSI that provide an in-depth, granular view into the health and performance of services and their associated KPIs. These default deep dives are automatically created for each service, allowing users to quickly drill down into the detailed operational metrics and performance data of their services. By accessing these deep dives through the Service Analyzer, ITSI users can efficiently investigate issues, understand service dependencies, and make informed decisions to maintain optimal service health. The auto-generated nature of these default deep dives simplifies the monitoring and analysis process, providing immediate insights into service performance without the need for manual setup or configuration.
NEW QUESTION # 69
Which step is required to install ITSI on a single Search Head?
Answer: B
Explanation:
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.
NEW QUESTION # 70
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