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| Section | Weight | Objectives |
|---|---|---|
| Glass Tables | 5% | - Describe glass tables - Design glass tables - Configure glass tables - Use glass tables |
| Managing Notable Events | 10% | - Customize notable event views - Describe multi-KPI alerts - Describe notable events workflow - Define key notable events terms and relationships - Work with notable events |
| Event Analytics | 5% | - Describe Event Analytics features - Configure and use Event Analytics |
| Access Control and Security | 5% | - Configure user roles and permissions - Create service-level teams |
| Troubleshooting ITSI | 10% | - Diagnose common issues - Monitor ITSI performance - Resolve configuration and operational problems |
| Introducing ITSI | 5% | - Examine the ITSI user interface - Describe reasons for using ITSI - Identify what ITSI does |
| Anomaly Detection | 5% | - Work with anomaly events - Enable anomaly detection |
| ITSI Architecture and Deployment | 10% | - Manage ITSI modules - Plan and design deployment - Describe ITSI architecture |
| Services and KPIs | 15% | - Use entities in KPI searches - Manage service dependencies - Configure KPI thresholds and alerts - Define services and KPIs |
| Aggregation Policies | 5% | - Use smart mode aggregation - Create aggregation policies |
| Correlation and Multi-KPI Searches | 5% | - Manage notable event storage - Create multi-KPI alerts - Define correlation searches |
| Deep Dives | 10% | - Describe deep dive concepts - Create and customize deep dives - Use default deep dives |
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NEW QUESTION # 50
Which of the following items apply to anomaly detection? (Choose all that apply.)
Answer: B,C
NEW QUESTION # 51
Which of the following is an advantage of using adaptive time thresholds?
Answer: B
Explanation:
Reference:
Adaptive thresholds are thresholds calculated by machine learning algorithms that dynamically adapt and change based on the KPI's observed behavior. Adaptive thresholds are useful for monitoring KPIs that have unpredictable or seasonal patterns that are difficult to capture with static thresholds. For example, you might use adaptive thresholds for a KPI that measures web traffic volume, which can vary depending on factors such as holidays, promotions, events, and so on. The advantage of using adaptive thresholds is:
A) Automatically update thresholds daily to manage dynamic changes to KPI values. This is true because adaptive thresholds use historical data from a training window to generate threshold values for each time block in a threshold template. Each night at midnight, ITSI recalculates adaptive threshold values for a KPI by organizing the data from the training window into distinct buckets and then analyzing each bucket separately. This way, the thresholds reflect the most recent changes in the KPI data and account for any anomalies or trends.
The other options are not advantages of using adaptive thresholds because:
B) Automatically adjust KPI calculation to manage dynamic event data. This is not true because adaptive thresholds do not affect the KPI calculation, which is based on the base search and the aggregation method. Adaptive thresholds only affect the threshold values that are used to determine the KPI severity level.
C) Automatically adjust aggregation policy grouping to manage escalating severity. This is not true because adaptive thresholds do not affect the aggregation policy, which is a set of rules that determines how to group notable events into episodes. Adaptive thresholds only affect the threshold values that are used to generate notable events based on KPI severity level.
D) Automatically adjust correlation search thresholds to adjust sensitivity over time. This is not true because adaptive thresholds do not affect the correlation search, which is a search that looks for relationships between data points and generates notable events. Adaptive thresholds only affect the threshold values that are used by KPIs, which can be used as inputs for correlation searches.
NEW QUESTION # 52
In distributed search, which components need to be installed on instances other than the search head?
Answer: D
Explanation:
SA-IndexCreation is required on all indexers. For non-clustered, distributed environments, copy SA-IndexCreation to $SPLUNK_HOME/etc/apps/ on individual indexers.
Reference:
In distributed search, the components that need to be installed on instances other than the search head are SA-IndexCreation and SA-ITSI-Licensechecker on indexers. SA-IndexCreation is an add-on that creates the indexes required by ITSI, such as itsi_summary and itsi_tracked_alerts. SA-ITSI-Licensechecker is an add-on that monitors the license usage of ITSI and generates alerts when the license limit is exceeded or about to expire. These components need to be installed on indexers because they handle the data ingestion and storage functions for ITSI. The other components, such as ITSI app and SA-ITOA, need to be installed on the search head(s) because they handle the search management and presentation functions for ITSI. Reference: Install IT Service Intelligence in a distributed environment
NEW QUESTION # 53
Which of the following best describes a default deep dive?
Answer: A
NEW QUESTION # 54
Where are KPI search results stored?
Answer: C
Explanation:
Explanation
Search results are processed, created, and written to the itsi_summary index via an alert action.
NEW QUESTION # 55
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