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Splunk SPLK-4001 Exam Syllabus Topics:

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

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q30-Q35):

NEW QUESTION # 30
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 Ephemeral 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 Dynamic 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 # 31
A team is troubleshooting high cardinality issues with a custom metric. What is a recommended best practice to reduce cardinality?

A. Convert the metric into a log
B. Add more dimensions to every datapoint
C. Avoid using high-cardinality values (such as unique IDs) as dimensions
D. Increase the metric resolution

Answer: C

Explanation:
Using high-cardinality values, such as unique request IDs or timestamps, as dimensions can cause an explosion in the number of unique time series. Avoiding this practice helps keep cardinality manageable and reduces cost and performance issues.

NEW QUESTION # 32
A customer has created a chart that tracks cpu.utilization of a set of production servers. The customer wants to show which of their servers are under 5% utilization to track underutilization of resources. What is the best analytic function to achieve this?

A. Exclude
B. Percentile
C. Bottom
D. Maximum

Answer: C

Explanation:
The Bottom analytic function identifies the metrics with the lowest values, allowing the customer to highlight servers with CPU utilization under 5% and track underutilized resources effectively.

NEW QUESTION # 33
Changes to which type of metadata result in a new metric time series?

A. Dimensions
B. Tags
C. Properties
D. Sources

Answer: A

Explanation:
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS).
Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host, you will create a new MTS for the same metric name.
Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest. They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed.

NEW QUESTION # 34
What is one reason a user of Splunk Observability Cloud would want to subscribe to an alert?

A. To receive an email notification when a detector is triggered.
B. To determine the root cause of the Issue triggering the detector.
C. To be able to modify the alert parameters.
D. To perform transformations on the data used by the detector.

Answer: A

Explanation:
A detector is a component of Splunk Observability Cloud that monitors metrics or events and triggers alerts when certain conditions are met. A user can create and configure detectors to suit their monitoring needs and goals.
A subscription is a way for a user to receive notifications when a detector triggers an alert. A user can subscribe to a detector by entering their email address in the Subscription tab of the detector page. A user can also unsubscribe from a detector at any time.
When a user subscribes to an alert, they will receive an email notification that contains information about the alert, such as the detector name, the alert status, the alert severity, the alert time, and the alert message. The email notification also includes links to view the detector, acknowledge the alert, or unsubscribe from the detector.

NEW QUESTION # 35
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