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Splunk SPLK-4001 Exam Syllabus Topics:

Section	Weight	Objectives
Finding Insights Using Analytics	15%	- Metrics Analytics 1. Moving and calendar window analytics 2. Subset analysis of MTS signals 3. Time-based comparisons 4. Aggregation across sources 5. Ratios and percentage trends 6. Combining plots in charts
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
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
Detectors for Common Use Cases	15%	- Advanced Detector Scenarios 1. Troubleshoot detectors 2. Large-scale monitoring 3. Population monitoring 4. Cyclic pattern monitoring 5. Non-flapping detectors 6. Ephemeral infrastructure monitoring 7. Detect common issues
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
Create Efficient Dashboards and Alerts	10%	- Dashboard Optimization 1. Alert customization 2. Data links configuration 3. Troubleshooting charts and alerts 4. Event overlays 5. Single-instance dashboards 6. Dashboard instructions
Get Metrics In with OpenTelemetry	10%	- OpenTelemetry Collector Deployment 1. Deploy the OTel Collector on Linux 2. General OpenTelemetry concepts 3. Troubleshooting common errors 4. Configure the OTel Collector 5. Edit configuration files
Introduction to Visualizing Metrics	15%	- Metrics Visualization 1. Apply rollups and analytics functions 2. Search and visualize metrics 3. Dashboard groups 4. Visualization types 5. Create charts and dashboards

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?

A. Top / Bottom
B. Best/Worst
C. Exclude / Include
D. Maximum / Minimum

Answer: A

Explanation:
Explanation
The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period1 To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation1.

NEW QUESTION # 54
What information is needed to create a detector?

A. Alert Status, Alert Condition, Alert Settings, Alert Meaning, Alert Recipients
B. Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients
C. Alert Signal, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
D. Alert Status, Alert Criteria, Alert Settings, Alert Message, Alert Recipients

Answer: B

Explanation:
According to the Splunk Observability Cloud documentation1, to create a detector, you need the following information:
Alert Signal: This is the metric or dimension that you want to monitor and alert on. You can select a signal from a chart or a dashboard, or enter a SignalFlow query to define the signal.
Alert Condition: This is the criteria that determines when an alert is triggered or cleared. You can choose from various built-in alert conditions, such as static threshold, dynamic threshold, outlier, missing data, and so on. You can also specify the severity level and the trigger sensitivity for each alert condition.
Alert Settings: This is the configuration that determines how the detector behaves and interacts with other detectors. You can set the detector name, description, resolution, run lag, max delay, and detector rules. You can also enable or disable the detector, and mute or unmute the alerts.
Alert Message: This is the text that appears in the alert notification and event feed. You can customize the alert message with variables, such as signal name, value, condition, severity, and so on. You can also use markdown formatting to enhance the message appearance.
Alert Recipients: This is the list of destinations where you want to send the alert notifications. You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on. You can also specify the notification frequency and suppression settings.

NEW QUESTION # 55
Which of the following statements are true about the datatable on a chart? (Choose all that apply.)

A. By default all metadata on the output signal are displayed.
B. Properties cannot be displayed.
C. A user can choose which of the output dimensions are displayed.
D. By default all dimensions on the output signal are displayed.

Answer: C,D

Explanation:
By default, all dimensions of the output signal are displayed in a chart's datatable, and users can select which of these dimensions to show or hide to customize the view.

NEW QUESTION # 56
To refine a search for a metric a customer types host: test-*. What does this filter return?

A. Only metrics with a dimension of host and a value beginning with test-.
B. Every metric except those with a dimension of host and a value equal to test.
C. Error
D. Only metrics with a value of test- beginning with host.

Answer: A

Explanation:
The correct answer is A. Only metrics with a dimension of host and a value beginning with test-.
This filter returns the metrics that have a host dimension that matches the pattern test-. For example, test-01, test-abc, test-xyz, etc. The asterisk () is a wildcard character that can match any string of characters1 To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2: https://docs.splunk.com/Observability/gdi/metrics/search.html

NEW QUESTION # 57
A team wants to visualize the 95th percentile of request latency across all instances of a service.
Which analytic function should be applied?

A. Rate
B. Max
C. Percentile
D. Count

Answer: C

Explanation:
The Percentile analytic function allows users to calculate a specific percentile, such as p95, across the values of a signal, which is useful for understanding latency distribution and tail performance.

NEW QUESTION # 58
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