Free PDF Quiz 2026 Splunk SPLK-4001: Splunk O11y Cloud Certified Metrics User Perfect Valid Exam Practice

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Splunk SPLK-4001 Exam Syllabus Topics:

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

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q87-Q92):

NEW QUESTION # 87
Which of the following statements are true about local data links? (select all that apply)

A. Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
B. Local data links are available on only one dashboard.
C. Only Splunk Observability Cloud administrators can create local links.
D. Local data links can only have a Splunk Observability Cloud internal destination.

Answer: A,B

Explanation:
Explanation
The correct answers are A and D.
According to the Get started with Splunk Observability Cloud document1, one of the topics that is covered in the Getting Data into Splunk Observability Cloud course is global and local data links. Data links are shortcuts that provide convenient access to related resources, such as Splunk Observability Cloud dashboards, Splunk Cloud Platform and Splunk Enterprise, custom URLs, and Kibana logs.
The document explains that there are two types of data links: global and local. Global data links are available on all dashboards and charts, while local data links are available on only one dashboard. The document also provides the following information about local data links:
Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
Local data links can have either a Splunk Observability Cloud internal destination or an external destination, such as a custom URL or a Kibana log.
Only Splunk Observability Cloud administrators can delete local data links.
Therefore, based on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:
B is false because local data links can have an external destination as well as an internal one.
C is false because anyone with write permission for a dashboard can create local data links, not just administrators.

NEW QUESTION # 88
Which of the following are correct ports for the specified components in the OpenTelemetry Collector?

A. gRPC (4459), SignalFx (9166), Fluentd (8956)
B. gRPC (4000), SignalFx (9943), Fluentd (6060)
C. gRPC (6831), SignalFx (4317), Fluentd (9080)
D. gRPC (4317), SignalFx (9080), Fluentd (8006)

Answer: D

Explanation:
Explanation
The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006).
According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result1. You can also see the agent and gateway configuration files in the same result for more details.
1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

NEW QUESTION # 89
Which analytic function can be used to discover peak page visits for a site over the last day?

A. Lag: (24h)
B. Maximum: Transformation (24h)
C. Count: (Id)
D. Maximum: Aggregation (Id)

Answer: B

Explanation:
Explanation
According to the Splunk Observability Cloud documentation1, the maximum function is an analytic function that returns the highest value of a metric or a dimension over a specified time interval. The maximum function can be used as a transformation or an aggregation. A transformation applies the function to each metric time series (MTS) individually, while an aggregation applies the function to all MTS and returns a single value. For example, to discover the peak page visits for a site over the last day, you can use the following SignalFlow code:
maximum(24h, counters("page.visits"))
This will return the highest value of the page.visits counter metric for each MTS over the last 24 hours. You can then use a chart to visualize the results and identify the peak page visits for each MTS.

NEW QUESTION # 90
Which of the following statements are true about the datatable on a chart? (select all that apply)

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

Answer: A,B

NEW QUESTION # 91
To smooth a very spiky cpu.utilization metric, what is the correct analytic function to better see if the cpu. utilization for servers is trending up over time?

A. Rate/Sec
B. Median
C. Mean (by host)
D. Mean (Transformation)

Answer: D

Explanation:
According to the web search results, a mean transformation is an analytic function that returns the average value of a metric or a dimension over a specified time interval. A mean transformation can be used to smooth a very spiky metric, such as cpu.utilization, by reducing the impact of outliers and noise. A mean transformation can also help to see if the metric is trending up or down over time, by showing the general direction of the average value. For example, to smooth the cpu.utilization metric and see if it is trending up over time, you can use the following SignalFlow code:
mean(1h, counters("cpu.utilization"))
This will return the average value of the cpu.utilization counter metric for each metric time series (MTS) over the last hour. You can then use a chart to visualize the results and compare the mean values across different MTS.

NEW QUESTION # 92
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