Splunk IT Service Intelligence Certified Admin Exam - SPLK-3002 Exam Dumps

Explanation:

In the context of Splunk IT Service Intelligence (ITSI), service dependencies allow for the modeling of relationships between services, where the health of one service (dependent) can affect the health of another (primary).

B.It is best practice to use the dependent service's built-in 'ServiceHealthScore' KPI to reflect impact to the primary service:Utilizing the 'ServiceHealthScore' KPI of a dependent service as part of the primary service's health calculation is a recommended practice. This approach ensures that changes in the health of the dependent service directly influence the primary service's overall health score, providing a more holistic view of service health within the IT environment.

C.Setting the dependent service KPI importance level will be treated as any other KPI in the primary service's health score:When a dependent service's KPI is incorporated into a primary service, the importance level assigned to this KPI is factored into the primary service's overall health score calculation just like any other KPI. This means that the impact of the dependent service on the primary service can be weighted according to the business significance of the relationship between the services.

The other options are not accurate representations of ITSI service dependencies. Changes in KPI importance levels do not break dependencies, and there is no restriction on configuring impactful dependent services to only one primary service, as dependencies can be complex and multi-layered across various services.

Explanation:

In Splunk ITSI, a Deep Dive is the primary visual workspace used for root-cause analysis and investigation. The power of a custom deep dive lies in its ability to overlay completely different types of data onto parallel timelines (swimlanes) so an analyst can spot correlations at a glance.

Why C is correct

A custom deep dive is incredibly flexible. You are not restricted to just looking at a single metric. In the same view, you can stack:

KPI lanes
To see calculated metrics (e.g., CPU load or Success Rate).

Service Health Score lanes
To see the overall health percentage of an entire service.

Metric lanes
Direct performance metrics from the underlying infrastructure.

Event lanes
Overlaid logs, errors, or notable events (e.g., tracking a spike in CPU exactly when a "Deploy Script Started" event occurred).

Why A is incorrect (Allows itoa_analyst roles to add comments)

By default, the itoa_analyst role can view and interact with deep dives, but adding comments or saving changes to a shared custom deep dive typically requires higher administrative or analyst write privileges (itoa_admin or specific write permissions assigned to the object).

Why B is incorrect (Requires at least 7 days' data to show anomalies)

ITSI's anomaly detection algorithms can run on much shorter timeframes depending on the configuration. While more data improves the baseline machine learning model, a hard requirement of "at least 7 days" is not a structural characteristic of a custom deep dive view.

Why D is incorrect (Uses drilldown to generate notable events via anomaly detection)

This reverses the workflow. You use Multi-KPI Search or Correlation Searches to generate notable events via anomaly detection. You then use the deep dive to investigate those notable events. Drilldowns within a deep dive are used to take you out to a raw search or another dashboard, not to generate new alerts.

Exam Tip 💡

Remember the difference between a Service Deep Dive and a Custom Deep Dive:

A Service Deep Dive
Is auto-generated by ITSI based on the KPIs inside a specific service.

A Custom Deep Dive
Is built from scratch by a user, allowing them to cherry-pick lanes from completely different services, ad-hoc searches, and event logs to create a unified troubleshooting dashboard.

Explanation:

In ITSI Deep Dives, the Lane Overlay Options allow you to overlay individual entity metrics (e.g., CPU usage per server) on a KPI lane.

For overlays to be available, the KPI must be split by entity during its configuration. This ensures that ITSI tracks metrics per entity (like each web server) instead of aggregating them into a single average.

Since Fritz only sees the average CPU usage across all servers, the KPI was not split by entity. That’s why no entity overlays are available.

Why the other options are incorrect

B. Did not enable entity filtering
Entity filtering controls which entities are included, but overlays depend on splitting, not filtering.

C. Configured KPI to split by pseudo#entity
Pseudo entities are placeholders; this wouldn’t prevent overlays if properly split.

D. Configured service with only three entities
Even with fewer entities, overlays would still appear if split by entity. The number of entities isn’t the issue.

Reference
Splunk Docs — Configure KPIs in ITSI: explains KPI splitting by entity and its impact on Deep Dive overlays.
Splunk Docs — Deep Dive Lane Overlay Options: details how entity overlays are generated when KPIs are split by entity.

Explanation:

D is the correct answer because teams allow you to restrict access to service content in UI views such as service analyzers, glass tables, deep dives, and episode review. Teams alsocontrol access to services and KPIs for editing and viewing purposes. Teams do not affect the ability to search against the itsi_summary index, restrict notable event alert actions, or restrict searches against the itsi_notable_audit index. References: Overview of teams in ITSI

Explanation:

SA-IndexCreation is a supporting add-on included with Splunk ITSI that is responsible for creating and configuring the custom indexes that ITSI requires (e.g., itsi_summary, itsi_tracked_alerts, itsi_grouped_alerts, etc.).

In a distributed deployment, SA-IndexCreation must be installed on:

Instance Type
Reason

Indexers
They need the index definitions (indexes.conf) to physically create and store the ITSI indexes where data will be written.

Search Heads
They need the index definitions to be aware of and search those indexes when running ITSI queries and dashboards.

❌ Why the Other Options Are Wrong:

A — Indexers and Forwarders
Forwarders (universal or heavy) do not need index definitions — they only ship data forward and don't store or search it. This is incomplete.

B — Search heads, indexers, and heavy forwarders
Heavy forwarders may parse data but still do not need ITSI index definitions from SA-IndexCreation. Including them is incorrect.

C — Search heads, indexers, and universal forwarders
Universal forwarders are lightweight data shippers with no indexing or searching capability. They have no need for SA-IndexCreation whatsoever.

📌 Key Takeaway:
SA-IndexCreation only needs to go where indexes are created (indexers) and where indexes are searched (search heads). Forwarders of any type are excluded.

📖 Reference:
Splunk Docs: Install ITSI in a distributed environment

Explanation:

Reference:

[Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/GTOverview, A is the correct answer because it is recommended to plan and implement services first, then build detailed glass tables that reflect the service hierarchy and dependencies. This way, you can ensure that your glass tables provide accurate and meaningful service-level insights. Building glass tables first might lead to unnecessary or irrelevant KPIs that do not align with your service goals. References: Splunk IT Service Intelligence Service Design Best Practices]

Explanation:

🟢 Option A is correct because Splunk IT Service Intelligence (ITSI) maintenance windows are designed to temporarily pause operational monitoring during scheduled downtime. While a maintenance window is active, KPI calculations and notable event generations are suppressed or muted for the targeted services/entities to prevent false alerts. Once the maintenance window expires, the system resumes normal operations, and KPI generation and notable event creation automatically start back up.

Explanation of Incorrect Options
❌ Option B is incorrect: While in maintenance mode, affected services and KPIs are typically designated by a dark grey color or a specific maintenance icon in the Service Analyzer and Topology views, not blue.

❌ Option C is incorrect: Maintenance mode windows are highly granular and flexible. They are scheduled based on exact start and end timestamps (dates and times) down to the minute, not restricted to fixed per-hour slots.

❌ Option D is incorrect: Service health scores and KPI data points are not deleted. Instead, the generation of health scores is either paused or ignored for the duration of the window so they do not negatively impact your historical trends or baselines.

📚Reference
According to the official Splunk ITSI Administration Guide, utilizing maintenance windows is a best practice to stop "alert fatigue" during planned outages. It is highly recommended to schedule maintenance windows with a 15- to 30-minute buffer before and after the actual work to accommodate system lag and prevent false positives.

Explanation:

When planning and designing a service tree in Splunk ITSI, the goal is to map the logical and technical dependencies of an application or IT service — not the human reporting structure. A service tree represents how services and their underlying components (like application tiers, databases, load balancers, etc.) relate to each other for monitoring, KPI aggregation, and root cause analysis.

A. A technical diagram of the application and its interconnections
Useful — directly shows how services and components interact.

B. An organizational chart
Least useful — org charts show reporting lines and teams, not service dependencies. ITSI cares about service topology, not who reports to whom.

C. A report of historical incidents and RCA
Useful — helps identify critical dependencies, common failure points, and meaningful KPIs.

D. A service topology from an ITSM tool
Useful — often contains CMDB relationships and service mapping that can inform the ITSI service tree.

Reference:
Splunk ITSI Service Designer documentation emphasizes using service and dependency mapping from technical documentation, CMDB, or discovery tools. Organizational charts are not mentioned as a planning input.

Best practices for service tree design focus on component relationships, data flows, and user journeys, not corporate structure.

Explanation:

In Splunk IT Service Intelligence (ITSI), a KPI widget on a glass table can be configured to drill down into a variety of destinations based on the needs of the user and the design of the glass table. This flexibility allows users to dive deeper into the data or analysis represented by the KPI widget, providing context and additional insights. The destinations for drill-downs from a KPI widget can include:

A. Another glass table, offering a different perspective or more detailed view related to the KPI.

B. A Splunk dashboard that provides broader analysis or incorporates data frommultiple sources.

C. A custom deep dive for in-depth, time-series analysis of the KPI and related metrics.

This versatility makes KPI widgets powerful tools for navigating through the wealth of operational data and insights available in ITSI, facilitating effective monitoring and decision-making.

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 twocommon 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.