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Splunk SPLK-4001 Exam Syllabus Topics:

Section	Weight	Objectives
Built-in Monitoring Content	10%	- Content packs and quick-start monitoring - Using pre-built dashboards and navigators
Metrics Concepts	20%	- Data retention and resolution settings - Dimensions, time-series data, and cardinality - Metric types: Gauges, Counters, Histograms, Summaries
Alerting with Detectors	15%	- Notification rules and incident response - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors
Dashboards & Operational Efficiency	10%	- Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards
Data Collection & Ingestion	15%	- Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment - Signal flow and data processing pipeline
Analytics & Insights	10%	- Correlation and pattern detection - Aggregation, filtering, and transformation functions
Visualizing Metrics	20%	- Visualization best practices and effective communication - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q92-Q97):

NEW QUESTION # 92
Which of the following are supported rollup functions in Splunk Observability Cloud?

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

Answer: D

Explanation:
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.

NEW QUESTION # 93
A customer wants to find the total number of transmission errors that occurred over a discrete period of time. Assuming that the metric type is Cumulative Counter, which of the following combinations of rollup and analytic function could be used to accomplish this?

A. Delta rollup and Average analytic function.
B. Delta rollup and Sum Transformation analytic function.
C. Sum rollup and Sum Transformation analytic function.
D. Average rollup and Sum Transformation analytic function.

Answer: B

Explanation:
For a cumulative counter, the Delta rollup calculates the change in the counter value over each interval. Applying the Sum Transformation analytic function then adds those deltas together to get the total number of transmission errors over the selected time period.

NEW QUESTION # 94
In the Splunk distribution of the OpenTelemetry Collector, what is the difference between the agent_config.yaml and the splunk-otel-collector.conf files?

A. agent_config.yaml configures the gateway's address and splunk-otel-collector.conf sets the memory limits for the collector.
B. agent_config.yaml defines the OpenTelemetry pipeline, and splunk-otel-collector.conf sets endpoint URLs and access tokens.
C. splunk-otel-collector.conf configures processors and agent_config.yaml sets the memory limits for the collector.
D. splunk-otel-collector.conf defines the OpenTelemetry pipeline, and agent_config.yaml sets endpoint URLs and access tokens.

Answer: D

NEW QUESTION # 95
How is it possible to create a dashboard group that no one else can edit?

A. Restrict the write access on the dashboard group.
B. Hide the edit menu on the dashboard group.
C. Link the dashboard group to the team.
D. Ask the admin to lock the dashboard group.

Answer: A

Explanation:
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 organization1. You can set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group1. To create a dashboard group that no one else can edit, you need to do the following steps:
Create a dashboard group as usual, by selecting Dashboard Group from the Create menu on the navigation bar, entering a name and description, and adding dashboards to the group1.
Select Alert settings from the Dashboard actions menu (⋯) on the top right corner of the dashboard group. This will open a dialog box where you can configure the permissions for the dashboard group1.
Under Write access, select Only me. This will restrict the write access to the dashboard group to yourself only. No one else will be able to edit or delete the dashboards in the group1.
Click Save. This will create a dashboard group that no one else can edit.

NEW QUESTION # 96
What are the best practices for creating detectors? (select all that apply)

A. View detector in a chart.
B. Have a consistent value.
C. View data at highest resolution.
D. Have a consistent type of measurement.

Answer: A,B,C,D

Explanation:
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues.
Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation.
View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior.
Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.

NEW QUESTION # 97
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