[Jul 27, 2026] Verified SPLK-4001 dumps and 128 unique questions
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Splunk SPLK-4001 certification exam is an excellent opportunity for professionals to validate their knowledge and skills in Splunk O11y Cloud. Splunk O11y Cloud Certified Metrics User certification exam is a globally recognized certification that helps professionals to enhance their career prospects in the field of cloud computing and data analytics. If you are interested in taking the Splunk SPLK-4001 certification exam, it is essential to prepare thoroughly and practice regularly to increase your chances of success.
NEW QUESTION # 45
Which of the following are correct ports for the specified components in the OpenTelemetry Collector?
- A. gRPC (6831), SignalFx (4317), Fluentd (9080)
- B. gRPC (4317), SignalFx (9080), Fluentd (8006)
- C. gRPC (4459), SignalFx (9166), Fluentd (8956)
- D. gRPC (4000), SignalFx (9943), Fluentd (6060)
Answer: B
Explanation:
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 result. You can also see the agent and gateway configuration files in the same result for more details.
NEW QUESTION # 46
What kind of metadata is sent with a datapoint
- A. Properties
- B. Tags
- C. Sources
- D. Dimensions
Answer: D
Explanation:
Dimensions are the metadata sent with a datapoint. They describe the context of the metric, such as the source, host, environment, or other identifying attributes used to filter and group metric data.
NEW QUESTION # 47
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. Mean (by host)
- C. Median
- 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 # 48
Splunk Observability Cloud includes which of the following out-of-the-box tools to help users interact with data? (Choose all that apply.)
- A. Infrastructure Navigators
- B. Detectors
- C. Infrastructure Maps
- D. Dashboard Groups
Answer: A,B,C,D
Explanation:
Splunk Observability Cloud provides detectors for alerting, Infrastructure Navigators and Infrastructure Maps for exploring infrastructure health and relationships, and Dashboard Groups for organizing dashboards that help users interact with and analyze their data.
NEW QUESTION # 49
Changes to which type of metadata result in a new metric time series?
- A. Properties
- B. Tags
- C. Sources
- D. Dimensions
Answer: D
Explanation:
The correct answer is A. Dimensions.
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)1 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 host2, you will create a new MTS for the same metric name1 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 changed2 To learn more about how to use metadata in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/metrics-and-metadata/metrics.html#Dimensions 2: https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html
NEW QUESTION # 50
Which of the following is optional, but highly recommended to include in a datapoint?
- A. Metric type
- B. Metric name
- C. Value
- D. Timestamp
Answer: A
Explanation:
Explanation
The correct answer is D. Metric type.
A metric type is an optional, but highly recommended field that specifies the kind of measurement that a datapoint represents. For example, a metric type can be gauge, counter, cumulative counter, or histogram. A metric type helps Splunk Observability Cloud to interpret and display the data correctly1 To learn more about how to send metrics to Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2:
https://docs.splunk.com/Observability/gdi/metrics/metrics.html
NEW QUESTION # 51
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard?
(select all that apply)
- A. Line
- B. Heatmap
- C. List
- D. Single Value
Answer: C,D
Explanation:
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 # 52
An SRE creates an event feed chart in a dashboard that shows a list of events that meet criteria they specify.
Which of the following should they include? (select all that apply)
- A. Events created when a detector clears an alert.
- B. Random alerts from active detectors.
- C. Events created when a detector triggers an alert.
- D. Custom events that have been sent in from an external source.
Answer: A,C,D
Explanation:
Explanation
According to the web search results1, an event feed chart is a type of chart that shows a list of events that meet criteria you specify. An event feed chart can display one or more event types depending on how you specify the criteria. The event types that you can include in an event feed chart are:
Custom events that have been sent in from an external source: These are events that you have created or received from a third-party service or tool, such as AWS CloudWatch, GitHub, Jenkins, or PagerDuty.
You can send custom events to Splunk Observability Cloud using the API or the Event Ingest Service.
Events created when a detector triggers or clears an alert: These are events that are automatically generated by Splunk Observability Cloud when a detector evaluates a metric or dimension and finds that it meets the alert condition or returns to normal. You can create detectors to monitor and alert on various metrics and dimensions using the UI or the API.
Therefore, option A, B, and D are correct.
NEW QUESTION # 53
What is the primary purpose of the "Infrastructure Navigator" in Splunk Infrastructure Monitoring?
- A. To manage user permissions
- B. To store historical logs
- C. To create new detectors automatically
- D. To provide a hierarchical, filterable view of infrastructure resources and their health
Answer: D
Explanation:
The Infrastructure Navigator provides a tree-based, filterable view that lets users drill down through clusters, hosts, and other resources to quickly assess health and performance across their infrastructure.
NEW QUESTION # 54
What are the best practices for creating detectors? (select all that apply)
- A. View detector in a chart.
- B. View data at highest resolution.
- C. Have a consistent type of measurement.
- D. Have a consistent value.
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 issues1 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 aggregation2 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 behavior3 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.
1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
2: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
3: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart
4: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
NEW QUESTION # 55
A customer is sending in datapoints regarding the number of API calls made to a particular service. The datapoints are being sent every 10 seconds, and represent the number of API calls seen in the last 10 seconds. What metric type is this?
- A. Counter
- B. Cumulative Counter
- C. Numeric
- D. Gauge
Answer: D
Explanation:
A gauge metric represents a value measured at a specific point in time or over a defined interval.
In this scenario, the datapoints indicate the number of API calls observed in each 10-second interval, which aligns with the characteristics of a gauge metric.
NEW QUESTION # 56
To configure the OTel Collector, which file is typically used to define the settings for receivers, exporters, and processors?
- A. settings.conf
- B. config.yaml
- C. otel-agent.yml
- D. otel-collector-config.json
Answer: D
NEW QUESTION # 57
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 signal,
- B. The detector has a muting rule.
- C. The detector has an incorrect alert rule.
- D. The detector is disabled.
Answer: B
Explanation:
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 # 58
For which types of charts can individual plot visualization be set?
- A. Line, Bar, Column
- B. Bar, Area, Column
- C. Histogram, Line, Column
- D. Line, Area, Column
Answer: D
Explanation:
For line, area, and column charts, you can set the individual plot visualization to change the appearance of each plot in the chart. For example, you can change the color, shape, size, or style of the lines, areas, or columns. You can also change the rollup function, data resolution, or y-axis scale for each plot.
To set the individual plot visualization for line, area, and column charts, you need to select the chart from the Metric Finder, then click on Plot Chart Options and choose Individual Plot Visualization from the list of options. You can then customize each plot according to your preferences.
NEW QUESTION # 59
What information is needed to create a detector?
- A. Alert Status, Alert Condition, Alert Settings, Alert Meaning, Alert Recipients
- B. Alert Signal, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
- C. Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients
- D. Alert Status, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
Answer: C
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 # 60
A customer is experiencing issues getting metrics from a new receiver they have configured in the OpenTelemetry Collector. How would the customer go about troubleshooting further with the logging exporter?
- A. Adding logging into the metrics exporter pipeline:
- B. Adding debug into the metrics receiver pipeline:
- C. Adding logging into the metrics receiver pipeline:
- D. Adding debug into the metrics exporter pipeline:
Answer: C
Explanation:
Explanation
The correct answer is B. Adding logging into the metrics receiver pipeline.
The logging exporter is a component that allows the OpenTelemetry Collector to send traces, metrics, and logs directly to the console. It can be used to diagnose and troubleshoot issues with telemetry received and processed by the Collector, or to obtain samples for other purposes1 To activate the logging exporter, you need to add it to the pipeline that you want to diagnose. In this case, since you are experiencing issues with a new receiver for metrics, you need to add the logging exporter to the metrics receiver pipeline. This will create a new plot that shows the metrics received by the Collector and any errors or warnings that might occur1 The image that you have sent with your question shows how to add the logging exporter to the metrics receiver pipeline. You can see that the exporters section of the metrics pipeline includes logging as one of the options.
This means that the metrics received by any of the receivers listed in the receivers section will be sent to the logging exporter as well as to any other exporters listed2 To learn more about how to use the logging exporter in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/components/logging-exporter.html 2:
https://docs.splunk.com/Observability/gdi/opentelemetry/exposed-endpoints.html
NEW QUESTION # 61
For which types of charts can individual plot visualization be set?
- A. Line, Bar, Column
- B. Bar, Area, Column
- C. Histogram, Line, Column
- D. Line, Area, Column
Answer: D
Explanation:
Explanation
The correct answer is C. Line, Area, Column.
For line, area, and column charts, you can set the individual plot visualization to change the appearance of each plot in the chart. For example, you can change the color, shape, size, or style of the lines, areas, or columns. You can also change the rollup function, data resolution, or y-axis scale for each plot1 To set the individual plot visualization for line, area, and column charts, you need to select the chart from the Metric Finder, then click on Plot Chart Options and choose Individual Plot Visualization from the list of options. You can then customize each plot according to your preferences2 To learn more about how to use individual plot visualization in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Individual-plot-visualization 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Set-individual-plot-visualization
NEW QUESTION # 62
What constitutes a single metrics time series (MTS)?
- A. A set of data points that all have the same metric name and list of dimensions.
- B. A set of data points that use different dimensions but the same metric name.
- C. A set of metrics that are ordered in series based on timestamp.
- D. A series of timestamps that all reflect the same metric.
Answer: A
Explanation:
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension "hostname": "host1"
MTS2: Gauge metric cpu.utilization, dimension "hostname": "host2"
MTS3: Gauge metric memory.usage, dimension "hostname": "host1"
A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp.
NEW QUESTION # 63
What happens when the limit of allowed dimensions is exceeded for an MTS?
- A. The additional dimensions are dropped.
- B. The datapoint is averaged.
- C. The datapoint is updated.
- D. The datapoint is dropped.
Answer: A
Explanation:
According to the web search results, dimensions are metadata in the form of key-value pairs that monitoring software sends in along with the metrics. The set of metric time series (MTS) dimensions sent during ingest is used, along with the metric name, to uniquely identify an MTS.
Splunk Observability Cloud has a limit of 36 unique dimensions per MTS. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.
NEW QUESTION # 64
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. Bottom
- B. Maximum
- C. Exclude
- D. Percentile
Answer: A
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 # 65
When writing a detector with a large number of MTS, such as memory. free in a deployment with 30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot. Which of the choices below would most likely reduce the number of MTS below the plot cap?
- A. When creating the plot, add a discriminator.
- B. Add a filter to narrow the scope of the measurement.
- C. Select the Sharded option when creating the plot.
- D. Add a restricted scope adjustment to the plot.
Answer: B
Explanation:
The correct answer is B. Add a filter to narrow the scope of the measurement.
A filter is a way to reduce the number of metric time series (MTS) that are displayed on a chart or used in a detector. A filter specifies one or more dimensions and values that the MTS must have in order to be included. For example, if you want to monitor the memory.free metric only for hosts that belong to a certain cluster, you can add a filter like cluster:my-cluster to the plot or detector. This will exclude any MTS that do not have the cluster dimension or have a different value for it1 Adding a filter can help you avoid exceeding the plot cap, which is the maximum number of MTS that can be contained in a single plot. The plot cap is 100,000 by default, but it can be changed by contacting Splunk Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap 3: https://docs.splunk.com/Observability/gdi/metrics/search.html
NEW QUESTION # 66
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