Splunk SPLK-4001 : Splunk O11y Cloud Certified Metrics User

Exam Code: SPLK-4001
Exam Name: Splunk O11y Cloud Certified Metrics User
Updated: Sep 26, 2026
Q & A: 128 Questions and Answers

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Splunk SPLK-4001 Exam Syllabus Topics:

Topic	Details
Topic 1	Get Metrics In with OpenTelemetry: This section of the exam measures the skills of Observability Engineers and focuses on configuring and managing OpenTelemetry (OTel) for data collection. It covers deploying the OTel Collector on Linux, editing configurations, troubleshooting common issues, and understanding general OpenTelemetry concepts necessary for integrating metrics efficiently.
Topic 2	Introduction to Visualizing Metrics: This section of the exam measures the skills of Data Visualization Analysts and emphasizes creating, managing, and interpreting charts and dashboards. It includes searching for metrics, visualizing data with different chart types, applying rollups and analytic functions, and effectively understanding metric data within dashboards.
Topic 3	Detectors for Common Use Cases: This section of the exam measures the skills of Monitoring Engineers and focuses on building and troubleshooting detectors for various real-world use cases. It includes creating detectors to monitor populations, prevent alert flapping, track cyclic patterns, handle large-scale monitoring, and ensure visibility in ephemeral infrastructure environments.
Topic 4	Finding Insights Using Analytics: This section of the exam measures the skills of Data Analysts and covers analytical methods for uncovering insights from metrics. It includes finding total values across sources, combining plots, performing time-based comparisons, analyzing trends through ratios and percentages, and applying analytic functions across moving or calendar time windows.
Topic 5	Metrics Concepts: This section of the exam measures the skills of Monitoring Specialists and covers fundamental metrics concepts including data resolution, rollups, and components of a datapoint. It also examines the Splunk Infrastructure Monitoring (IM) Data Model, different metric types, and the role of metadata in defining and structuring metric data.