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Splunk SPLK-4001 Exam Syllabus Topics:

Section	Weight	Objectives
Alerting on Metrics with Detectors	10%	- Detectors and Alerting 1. Standalone detectors 2. Clone and manage detectors 3. Muting rules 4. Create detectors from charts
Introduction to Visualizing Metrics	15%	- Metrics Visualization 1. Visualization types 2. Search and visualize metrics 3. Dashboard groups 4. Create charts and dashboards 5. Apply rollups and analytics functions
Finding Insights Using Analytics	15%	- Metrics Analytics 1. Moving and calendar window analytics 2. Time-based comparisons 3. Subset analysis of MTS signals 4. Aggregation across sources 5. Combining plots in charts 6. Ratios and percentage trends
Monitor Using Built-in Content	10%	- Observability Navigation & Monitoring 1. Kubernetes Navigator usage 2. Interact with built-in dashboards and content 3. Cluster Analyzer troubleshooting 4. Interpret charts using rollups and analytics functions 5. Use alerts and detectors
Get Metrics In with OpenTelemetry	10%	- OpenTelemetry Collector Deployment 1. General OpenTelemetry concepts 2. Configure the OTel Collector 3. Troubleshooting common errors 4. Deploy the OTel Collector on Linux 5. Edit configuration files
Create Efficient Dashboards and Alerts	10%	- Dashboard Optimization 1. Event overlays 2. Dashboard instructions 3. Data links configuration 4. Single-instance dashboards 5. Troubleshooting charts and alerts 6. Alert customization
Detectors for Common Use Cases	15%	- Advanced Detector Scenarios 1. Cyclic pattern monitoring 2. Detect common issues 3. Non-flapping detectors 4. Ephemeral infrastructure monitoring 5. Population monitoring 6. Large-scale monitoring 7. Troubleshoot detectors
Metrics Concepts	15%	- Core Metrics Data Model 1. Components of a datapoint 2. Splunk IM data model, metrics, MTS, datapoints 3. Data resolution and rollups 4. Types of metadata

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Splunk O11y Cloud Certified Metrics User SPLK-4001 Prüfungsfragen mit Lösungen (Q49-Q54):

49. Frage
Which of the following are supported rollup functions in Splunk Observability Cloud?

A. sigma, epsilon, pi, omega, beta, tau
B. 1min, 5min, 10min, 15min, 30min
C. average, latest, lag, min, max, sum, rate
D. std_dev, mean, median, mode, min, max

Antwort: C

Begründung:
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, Observability Cloud has the following rollup functions: Sum: (default for counter metrics): Returns the sum of all data points in the MTS reporting interval. Average (default for gauge metrics): Returns the average value of all data points in the MTS reporting interval. Min: Returns the minimum data point value seen in the MTS reporting interval. Max:
Returns the maximum data point value seen in the MTS reporting interval. Latest: Returns the most recent data point value seen in the MTS reporting interval. Lag: Returns the difference between the most recent and the previous data point values seen in the MTS reporting interval. Rate: Returns the rate of change of data points in the MTS reporting interval. Therefore, option A is correct.

50. Frage
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard? (select all that apply)

A. List
B. Line
C. Single Value
D. Heatmap

Antwort: A,C

Begründung:
The chart visualization types that are unaffected by changing the time picker on a dashboard are:
Single Value: A single value chart shows the current value of a metric or an expression. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart1 List: A list chart shows the values of a metric or an expression for each dimension value in a table format. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart2 Therefore, the correct answer is A and D.
To learn more about how to use different chart visualization types in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Single-value 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#List 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html

51. Frage
What is the key difference between creating a standalone detector and creating a detector from a chart?

A. Creating a detector from a chart involves converting chart data into a muting rule.
B. Creating a standalone detector requires linking it to a chart, while creating a detector from a chart does not.
C. Standalone detectors do not require any conditions or settings, while detectors from charts do.
D. Standalone detectors are used for muting rules, while detectors from charts are used for cloning.

Antwort: B

52. Frage
When writing a detector with a large number of MTS, such as memory. free in a deployment with 30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot. Which of the choices below would most likely reduce the number of MTS below the plot cap?

A. When creating the plot, add a discriminator.
B. Add a restricted scope adjustment to the plot.
C. Select the Sharded option when creating the plot.
D. Add a filter to narrow the scope of the measurement.

Antwort: D

Begründung:
Explanation
The correct answer is B. Add a filter to narrow the scope of the measurement.
A filter is a way to reduce the number of metric time series (MTS) that are displayed on a chart or used in a detector. A filter specifies one or more dimensions and values that the MTS must have in order to be included.
For example, if you want to monitor the memory.free metric only for hosts that belong to a certain cluster, you can add a filter like cluster:my-cluster to the plot or detector. This will exclude any MTS that do not have the cluster dimension or have a different value for it1 Adding a filter can help you avoid exceeding the plot cap, which is the maximum number of MTS that can be contained in a single plot. The plot cap is 100,000 by default, but it can be changed by contacting Splunk Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap 3:
https://docs.splunk.com/Observability/gdi/metrics/search.html

53. Frage
A customer wants to share a collection of charts with their entire SRE organization. What feature of Splunk Observability Cloud makes this possible?

A. Shared charts
B. Dashboard groups
C. Public dashboards
D. Chart exporter

Antwort: B

Begründung:
According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization.
You can create dashboard groups based on different criteria, such as service, team, role, or topic.
You can also set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group. Dashboard groups make it possible to share a collection of charts with your entire SRE organization, or any other group of users that you want to collaborate with.

54. Frage
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