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Exam Information
Official specifications published by Splunk
Exam Format
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Skills this practice test covers
- Deploy and configure the OTel Collector on a Linux host to send metrics to Splunk Observability Cloud.
- Explain how datapoints, metric time series and metadata fit together in the Splunk IM Data Model.
- Build charts, dashboards and dashboard groups, and choose the right rollup and analytic function.
- Create detectors from a chart or from scratch, clone them and add muting rules.
- Compare a metric week over week and turn totals into percentages and ratios.
- Investigate nodes, pods and containers with the Kubernetes Navigator and the Cluster Analyzer.
Explore related topics
SPLK-4001 exam content
8 domains · 39 topics · 148 questions
Get Metrics In with OpenTelemetry10%15 questions
Metrics reach Splunk Observability Cloud through the OpenTelemetry Collector. This domain asks how to install the collector on Linux, edit its configuration and fix the errors that stop data from arriving.
Skills tested
- Deploy the OTel Collector on a Linux host.
- Edit the collector configuration and apply the change.
- Trace a common collector error to its cause.
- Explain the general OpenTelemetry concepts the collector relies on.
Questions by topic
- Configure the OTel Collector11
- Deploy the OTel Collector on Linux1
- Troubleshooting common errors2
- General OpenTelemetry Concepts1
Metrics Concepts15%23 questions
Every chart and detector rests on the data model. Candidates name the parts of a datapoint, tell the types of metadata apart and explain how data resolution and rollups shape what a chart shows.
Skills tested
- List the components of a datapoint.
- Define metrics, metric time series and datapoints in the Splunk IM Data Model.
- Tell the types of metadata apart.
- Explain how rollups summarize data at a coarser resolution.
Questions by topic
- Data resolution, rollups10
- Discriminate between types of metadata8
- Define components of the Splunk IM Data Model, Metrics, MTS, datapoints3
- List the components of a datapoint2
Monitor Using Built-in Content10%16 questions
Splunk Observability Cloud ships with ready-made charts, dashboards and Kubernetes views. This domain asks how to read them correctly and use them to find problems without building anything new.
Skills tested
- Read a built-in chart while taking its rollup and resolution into account.
- Subscribe to an existing alert.
- Investigate nodes, pods and containers with the Kubernetes Navigator.
- Use the Cluster Analyzer to find the root of a cluster problem.
Questions by topic
- Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers11
- Use the Cluster Analyzer to pinpoint the root of some problems1
- Subscribe to alerts2
- Use built-in Kubernetes Dashboards to investigate and troubleshoot2
Introduction to Visualizing Metrics15%22 questions
Here candidates build their own views. They search for a metric, plot it, pick a visualization type and group the result into dashboards.
Skills tested
- Search for a metric and plot it in a new chart.
- Choose between the chart visualization types.
- Apply a rollup and an analytic function to a plot.
- Save charts into dashboards and dashboard groups.
Questions by topic
- Distinguish between different chart visualization types10
- Correctly apply rollups and analytic functions7
- Search for metrics1
- Create dashboards and dashboard groups2
- Visualize a metric in a chart2
Introduction to Alerting on Metrics with Detectors10%14 questions
A detector watches a signal and raises an alert when a condition is met. This domain covers the basic ways to create detectors and to silence their alerts.
Skills tested
- Create a detector from an existing chart.
- Clone a detector and change its conditions.
- Create a standalone detector.
- Create a muting rule for a planned maintenance window.
Questions by topic
- Create a muting rule11
- Create a detector from a chart1
- Create a standalone detector2
Create Efficient Dashboards and Alerts10%16 questions
This domain makes dashboards and alerts easier for a team to use. It also asks why a chart or alert can look wrong when datapoints arrive late.
Skills tested
- Add instructions to a dashboard and show events on it.
- Build a single-instance dashboard.
- Configure local data links.
- Customize an alert message.
- Explain how late datapoints and the extrapolation policy affect charts and alerts.
Official topics
- Add instructions to dashboards
- Create single-instance dashboards
- View events on dashboards
- Configure local data links
- Customize alert messages
- Troubleshoot charts and alerts (Impact of late datapoints; extrapolation policy, etc.)
Finding Insights Using Analytics15%22 questions
Analytic functions turn raw metric time series into answers. Candidates total values across sources, combine plots and compare one time period with another.
Skills tested
- Find the total value of a metric across all sources.
- Combine two plots into a percentage or ratio.
- Compare a metric with the same hour, day or week before and alert on the change.
- Apply an analytic function over a moving or calendar time window.
Questions by topic
- Combining plots in charts8
- Apply analytic functions over moving and calendar time windows4
- View and alert on weekly, daily, or hourly comparisons2
- Apply analytics functions to a subset of MTS in a signal3
- Use percentages and ratios to understand trends3
- Finding total value across all sources2
Detectors for Common Use Cases15%20 questions
Real environments need detectors that stay quiet until something is really wrong. This domain covers detectors for populations of sources, cyclic metrics and short-lived infrastructure, and how to troubleshoot them.
Skills tested
- Find and fix the common faults in a detector.
- Create a detector that monitors a population of sources.
- Build a detector that does not flap.
- Alert on a metric with a daily or weekly cycle.
- Monitor infrastructure whose hosts and containers come and go.
Questions by topic
- Create non-flapping detectors4
- Monitor metrics with cyclic patterns2
- Troubleshoot a detector3
- Create detectors to monitor populations3
- Monitor a large number of sources3
- Monitor an ephemeral infrastructure2
- Identify common issues with detectors3
Requirements
- No formal prerequisites: anyone can book the exam.
About the SPLK-4001 exam
SPLK-4001 checks whether a user can bring metrics into Splunk Observability Cloud, chart them and alert on them. It is a foundational exam in Splunk's observability track for DevOps engineers, site reliability engineers and IT administrators. The questions start with the OpenTelemetry Collector and the metrics data model. They then move to charts, dashboards, analytics and detectors. Passing earns the Splunk O11y Cloud Certified Metrics User certification.
Four domains carry the most weight: metrics concepts, visualizing metrics, analytics and detectors for common use cases. The PlanetCert practice test explanations show why a chart looks the way it does, for example how a rollup changes a value at a coarse chart resolution. Study Mode suits the detector questions, where one setting can decide whether a detector flaps. Brainy AI Assistant can explain a missed analytics answer or write a new question about late datapoints and extrapolation policy.
How to earn the certification
- Earns
- Splunk O11y Cloud Certified Metrics UserFoundational
The certification stays valid for three years.
How to prepare for SPLK-4001
- 1
Get a collector sending data
Install the OTel Collector on a Linux machine, send its metrics to Splunk Observability Cloud, and break the configuration on purpose to see the errors it reports.
- 2
Learn the data model cold
Write out how a datapoint, a metric time series and each type of metadata relate. Metrics Concepts underpins every other domain.
- 3
Rebuild charts with analytics
Plot one metric several ways: change the rollup, total it across sources, divide it by another plot and compare it with last week. Finding Insights Using Analytics rewards this hands-on work.
- 4
Make detectors misbehave
Create detectors from charts and from scratch, then make one flap, feed it late data and fix it. Detectors for Common Use Cases is one of the heaviest domains.
- 5
Finish with the practice test
Take the PlanetCert practice test in Study Mode one domain at a time, then a full run in Timed Mode. Go back to the lab for any rollup or detector question missed twice.
Start a timed SPLK-4001run
Splunk points candidates to its DevOps engineer and IT administrator learning paths.
Who SPLK-4001 is for
- DevOps engineers and site reliability engineers who monitor services with Splunk Observability Cloud.
- IT administrators who build dashboards and alerts for hosts and Kubernetes clusters.
- Splunk Platform users, such as holders of Core Certified Power User, who now also work with metrics.
Typical job titles: DevOps engineer · Site reliability engineer · IT administrator · Observability engineer · Cloud operations engineer
SPLK-4001 glossary
18 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- analytic functions
- Calculations such as sum or mean applied to a plot to change or combine its metric time series.
- chart resolution
- The time interval each plotted value on a chart represents, which decides how much detail the chart shows.
- Cluster Analyzer
- A tool that helps pinpoint the likely root of problems in a Kubernetes cluster.
- dashboard groups
- Collections of related dashboards that are kept and shared together.
- datapoint
- A single measured value of a metric, stored with a timestamp and the metadata that identifies its source.
- detector
- A rule that watches a signal and creates an alert when the signal meets a set condition.
- ephemeral infrastructure
- Hosts, containers or other resources that start and stop often, so their metric time series are short-lived.
- extrapolation policy
- A setting that tells a chart or detector what value to assume when a datapoint is missing.
- Kubernetes Navigator
- A built-in view that shows the health of a Kubernetes cluster and lets a user drill into nodes, pods and containers.
- local data links
- Links set on a dashboard that take a user from a value on a chart to a related dashboard or view.
- metadata
- Extra information attached to metric data, used to filter, group and find it.
- MTS
- Metric time series: the sequence of datapoints for one metric from one unique source.
- muting rule
- A rule that silences notifications from chosen detectors for a set time, such as a maintenance window.
- non-flapping detectors
- Detectors set up so that they do not trigger and clear again and again when a value moves around a threshold.
- OpenTelemetry
- An open standard and set of tools for collecting metrics, traces and logs from applications and hosts.
- OTel Collector
- The OpenTelemetry Collector, an agent that receives, processes and sends metrics and other data to Splunk Observability Cloud.
- rollups
- Summaries such as average, sum or maximum that combine many datapoints into one value for a longer time interval.
- Splunk IM Data Model
- The way Splunk Infrastructure Monitoring organizes data into metrics, metric time series and datapoints.