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Splunk SPLK-4001 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Metrics Concepts	15%	- Core Metrics Data Model 1. Splunk IM data model, metrics, MTS, datapoints 2. Data resolution and rollups 3. Types of metadata 4. Components of a datapoint
Topic 2: Detectors for Common Use Cases	15%	- Advanced Detector Scenarios 1. Large-scale monitoring 2. Troubleshoot detectors 3. Ephemeral infrastructure monitoring 4. Non-flapping detectors 5. Population monitoring 6. Detect common issues 7. Cyclic pattern monitoring
Topic 3: Introduction to Visualizing Metrics	15%	- Metrics Visualization 1. Apply rollups and analytics functions 2. Search and visualize metrics 3. Dashboard groups 4. Create charts and dashboards 5. Visualization types
Topic 4: Monitor Using Built-in Content	10%	- Observability Navigation & Monitoring 1. Cluster Analyzer troubleshooting 2. Kubernetes Navigator usage 3. Use alerts and detectors 4. Interact with built-in dashboards and content 5. Interpret charts using rollups and analytics functions
Topic 5: Create Efficient Dashboards and Alerts	10%	- Dashboard Optimization 1. Alert customization 2. Event overlays 3. Single-instance dashboards 4. Dashboard instructions 5. Data links configuration 6. Troubleshooting charts and alerts
Topic 6: Finding Insights Using Analytics	15%	- Metrics Analytics 1. Aggregation across sources 2. Ratios and percentage trends 3. Combining plots in charts 4. Subset analysis of MTS signals 5. Moving and calendar window analytics 6. Time-based comparisons
Topic 7: Get Metrics In with OpenTelemetry	10%	- OpenTelemetry Collector Deployment 1. Edit configuration files 2. Deploy the OTel Collector on Linux 3. Configure the OTel Collector 4. General OpenTelemetry concepts 5. Troubleshooting common errors
Topic 8: Alerting on Metrics with Detectors	10%	- Detectors and Alerting 1. Clone and manage detectors 2. Standalone detectors 3. Create detectors from charts 4. Muting rules

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q112-Q117):

NEW QUESTION # 112
When installing OpenTelemetry Collector, which error message is indicative that there is a misconfigured realm or access token?

A. 403 (NOT ALLOWED)
B. 401 (UNAUTHORIZED)
C. 503 (SERVICE UNREACHABLE)
D. 404 (NOT FOUND)

Answer: B

Explanation:
According to the web search results, a 401 (UNAUTHORIZED) error message is indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 401 (UNAUTHORIZED) error message means that the request was not authorized by the server due to invalid credentials. A realm is a parameter that specifies the scope of protection for a resource, such as a Splunk Observability Cloud endpoint. An access token is a credential that grants access to a resource, such as a Splunk Observability Cloud API. If the realm or the access token is misconfigured, the request to install OpenTelemetry Collector will be rejected by the server with a 401 (UNAUTHORIZED) error message.

NEW QUESTION # 113
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?

A. The detector has an incorrect alert rule.
B. The detector has a muting rule.
C. The detector is disabled.
D. The detector has an incorrect signal,

Answer: B

Explanation:
The most likely root cause of the issue is D. The detector has a muting rule.
A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal1 When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.

NEW QUESTION # 114
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard? (select all that apply)

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

Answer: B,C

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 # 115
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. Standalone detectors are used for muting rules, while detectors from charts are used for cloning.
C. Creating a standalone detector requires linking it to a chart, while creating a detector from a chart does not.
D. Standalone detectors do not require any conditions or settings, while detectors from charts do.

Answer: C

NEW QUESTION # 116
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. Check the Dynamic checkbox when creating the detector.
C. Create the detector. Select Alert settings, then select Auto-Clear Alerts and enter an appropriate time period.
D. Check the Ephemeral checkbox when creating the detector.

Answer: A

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 deployed. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down. 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.

NEW QUESTION # 117
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