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The SPLK-3002 exam is a performance-based exam that evaluates the candidate’s ability to solve real-world problems using Splunk ITSI. SPLK-3002 exam consists of 60 multiple-choice questions and the candidate needs to score at least 70% to pass the exam. SPLK-3002 Exam Duration is 90 minutes, and candidates are required to complete the exam within the given time frame. SPLK-3002 exam is available in English and Japanese languages.
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The SPLK-3002 exam covers a range of topics related to ITSI, including configuring and managing ITSI components, creating and managing service models, utilizing machine learning to improve alerts and incident management, and using ITSI to monitor and troubleshoot IT services. SPLK-3002 exam is made up of 65 multiple-choice questions and must be completed within 90 minutes. A passing score of 70% or higher is required to earn the certification.
Splunk SPLK-3002 certification exam is designed to validate the skills and knowledge of IT professionals in managing and administering Splunk IT Service Intelligence (ITSI) environments. Splunk IT Service Intelligence Certified Admin certification is intended for individuals who have a good understanding of Splunk, ITSI, and the use of data analytics to deliver business value. SPLK-3002 Exam is aimed at professionals who work with data analytics, IT operations, and business stakeholders to improve service delivery and customer satisfaction.
NEW QUESTION # 21
Which of the following describes entities? (Choose all that apply.)
Answer: D
NEW QUESTION # 22
In which index are active notable events stored?
Answer: C
Explanation:
In Splunk IT Service Intelligence (ITSI), notable events are created and managed within the context of its Event Analytics framework. These notable events are stored in the itsi_tracked_alerts index. This index is specifically designed to hold the active notable events that are generated by ITSI's correlation searches, which are based on the conditions defined for various services and their KPIs. Notable events are essentially alerts or issues that need to be investigated and resolved. The itsi_tracked_alerts index enables efficient storage, querying, and management of these events, facilitating the ITSI's event management and review process. The other options, such as itsi_notable_archive and itsi_notable_audit, serve different purposes, such as archiving resolved notable events and auditing changes to notable event configurations, respectively. Therefore, the correct answer for where active notable events are stored is the itsi_tracked_alerts index.
NEW QUESTION # 23
In maintenance mode, which features of KPIs still function?
Answer: C
Explanation:
It's a best practice to schedule maintenance windows with a 15- to 30-minute time buffer before and after you start and stop your maintenance work. This gives the system an opportunity to catch up with the maintenance state and reduces the chances of ITSI generating false positives during maintenance operations.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/AboutMW A is the correct answer because KPI searches still run during maintenance mode, but the results are buffered until the maintenance window is over. This means that no alerts are triggered during maintenance mode, but once it ends, the buffered results are processed and alerts are generated if necessary. You cannot create new KPIs or modify existing KPIs during maintenance mode. References: [Overview of maintenance windows in ITSI]
NEW QUESTION # 24
Which of the following are deployment recommendations for ITSI? (Choose all that apply.)
Answer: A,B,D
Explanation:
You might need to increase the hardware specifications of your own Enterprise Security deployment above the minimum hardware requirements depending on your environment.
Install Splunk Enterprise Security on a dedicated search head or search head cluster.
The Splunk platform uses indexers to scale horizontally. The number of indexers required in an Enterprise Security deployment varies based on the data volume, data type, retention requirements, search type, and search concurrency.
Reference: https://docs.splunk.com/Documentation/ES/latest/Install/DeploymentPlanning A, B, and C are correct answers because ITSI deployments often require more hardware resources than base Splunk requirements due to the high volume of data ingestion and processing. ITSI deployments also require a dedicated search head that runs the ITSI app and handles all ITSI-related searches and dashboards. ITSI deployments may also increase the number of required indexers based on the number and frequency of KPI searches, which can generate a large amount of summary data. References: ITSI deployment overview, ITSI deployment planning
NEW QUESTION # 25
Which of the following is an advantage of using adaptive time thresholds?
Answer: D
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/TimePolicies 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.
References: Create adaptive KPI thresholds in ITSI
NEW QUESTION # 26
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