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Splunk SPLK-4001 Exam Syllabus Topics:

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

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard? (select all that apply)

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

Answer: A,D

Explanation:
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

NEW QUESTION # 112
A customer has a very dynamic infrastructure. During every deployment, all existing instances are destroyed, and new ones are created Given this deployment model, how should a detector be created that will not send false notifications of instances being down?

A. Create the detector. Select Alert settings, then select Ephemeral Infrastructure and enter the expected lifetime of an instance.
B. Create the detector. Select Alert settings, then select Auto-Clear Alerts and enter an appropriate time period.
C. Check the Dynamic checkbox when creating the detector.
D. Check the Ephemeral checkbox when creating the detector.

Answer: A

Explanation:
Explanation
According to the web search results, ephemeral infrastructure is a term that describes instances that are auto-scaled up or down, or are brought up with new code versions and discarded or recycled when the next code version is deployed1. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down2. To use this feature, you need to do the following steps:
Create the detector as usual, by selecting the metric or dimension that you want to monitor and alert on, and choosing the alert condition and severity level.
Select Alert settings, then select Ephemeral Infrastructure. This will enable a special mode for the detector that will automatically clear alerts for instances that are expected to be terminated.
Enter the expected lifetime of an instance in minutes. This is the maximum amount of time that an instance is expected to live before being replaced by a new one. For example, if your instances are replaced every hour, you can enter 60 minutes as the expected lifetime.
Save the detector and activate it.
With this feature, the detector will only trigger alerts when an instance stops reporting a metric unexpectedly, based on its expected lifetime. If an instance stops reporting a metric within its expected lifetime, the detector will assume that it was terminated on purpose and will not trigger an alert. Therefore, option B is correct.

NEW QUESTION # 113
When configuring a new receiver to send data to Splunk Observability Cloud, which sections must the new receiver be added to in the OpenTelemetry Collector configuration file? (Choose all that apply.)

A. receivers
B. pipelines
C. splunk
D. exporters

Answer: A,B,D

Explanation:
In the OpenTelemetry Collector configuration, a new receiver must be defined under receivers, referenced in a pipeline, and paired with an appropriate exporter to send data to Splunk Observability Cloud.

NEW QUESTION # 114
With exceptions for transformations or timeshifts, at what resolution do detectors operate?

A. 10 seconds
B. The resolution of the dashboard
C. Native resolution
D. The resolution of the chart

Answer: C

Explanation:
According to the Splunk Observability Cloud documentation1, detectors operate at the native resolution of the metric or dimension that they monitor, with some exceptions for transformations or timeshifts. The native resolution is the frequency at which the data points are reported by the source. For example, if a metric is reported every 10 seconds, the detector will evaluate the metric every 10 seconds. The native resolution ensures that the detector uses the most granular and accurate data available for alerting.

NEW QUESTION # 115
A customer wants to be alerted only when a condition is true across a majority of their hosts, rather than for each individual host. Which detector condition type should they use?

A. Historical Anomaly
B. Custom Threshold with "at or above" combined with aggregation across all signals
C. Static Threshold
D. Sudden Change

Answer: B

Explanation:
By aggregating the signal across all hosts before applying a custom threshold, the detector evaluates the overall condition rather than firing separately for each individual host, allowing alerts based on the majority state.

NEW QUESTION # 116
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