Splunk SPLK-4001 Dumps

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.

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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 deployed1. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down2. 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. Therefore, option B is correct.

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.

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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 MTS1. Splunk Observability Cloud has a limit of 36 unique dimensions per MTS2. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud2. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.

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.

According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud1. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA.

To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests2. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings3. Therefore, option A and D are correct.

The correct answer is B. /etc/otel/collector/

According to the web search results, the Splunk distribution of the OpenTelemetry Collector stores the configuration files on Linux machines in the /etc/otel/collector/ directory by default. You can verify this by looking at the first result1, which explains how to install the Collector for Linux manually. It also provides the locations of the default configuration file, the agent configuration file, and the gateway configuration file.

To learn more about how to install and configure the Splunk distribution of the OpenTelemetry Collector, you can refer to this documentation2.

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The correct answer is C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select ‘version’ from the Group By field.

This will create a new plot B that shows the average memory utilization for each version of the application. The engineer can then compare the values of plot B for the ‘canary’ and ‘stable’ versions to see if there is a significant difference.

To learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation1.

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The correct answer is D. Mean (Transformation).

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 interval1. 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.

Option A is incorrect because rate/sec is not an analytic function, but rather a rollup function that returns the rate of change of data points in the MTS reporting interval1. Rate/sec can be used to convert cumulative counter metrics into counter metrics, but it does not smooth or trend a metric. Option B is incorrect because median is not an analytic function, but rather an aggregation function that returns the middle value of a metric or a dimension over the entire time range1. Median can be used to find the typical value of a metric, but it does not smooth or trend a metric. Option C is incorrect because mean (by host) is not an analytic function, but rather an aggregation function that returns the average value of a metric or a dimension across all MTS with the same host dimension1. Mean (by host) can be used to compare the performance of different hosts, but it does not smooth or trend a metric.

Mean (Transformation) is an analytic function that allows you to smooth a very spiky metric by applying a moving average over a specified time window. This can help you see the general trend of the metric over time, without being distracted by the short-term fluctuations1

To use Mean (Transformation) on a cpu.utilization metric, you need to select the metric from the Metric Finder, then click on Add Analytics and choose Mean (Transformation) from the list of functions. You can then specify the time window for the moving average, such as 5 minutes, 15 minutes, or 1 hour. You can also group the metric by host or any other dimension to compare the smoothed values across different servers2

To learn more about how to use Mean (Transformation) and other analytic functions in Splunk Observability Cloud, you can refer to this documentation2.

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The correct answer is C. 1 second.

According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.

The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.

In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1-second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.

Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.

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.

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.

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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.

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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.

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The Pod conditions that the Analyzer panel in Kubernetes Navigator monitors are:

• Not Scheduled: This condition indicates that the Pod has not been assigned to a Node yet. This could be due to insufficient resources, node affinity, or other scheduling constraints1
• Unknown: This condition indicates that the Pod status could not be obtained or is not known by the system. This could be due to communication errors, node failures, or other unexpected situations1
• Failed: This condition indicates that the Pod has terminated in a failure state. This could be due to errors in the application code, container configuration, or external factors1
• Pending: This condition indicates that the Pod has been accepted by the system, but one or more of its containers has not been created or started yet. This could be due to image pulling, volume mounting, or network issues1

Therefore, the correct answer is A, B, C, and D.

To learn more about how to use the Analyzer panel in Kubernetes Navigator, you can refer to this documentation2.

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The correct answer is C. Timeshift.

According to the Splunk Observability Cloud documentation1, timeshift is an analytic function that allows you to compare the current value of a metric with its value at a previous time interval, such as an hour ago or a week ago. You can use the timeshift function to measure the change in a metric over time and identify trends, anomalies, or patterns. For example, to identify the servers where utilization has increased the most since last week, you can use the following SignalFlow code:

timeshift(1w, counters(“server.utilization”))

This will return the value of the server.utilization counter metric for each server one week ago. You can then subtract this value from the current value of the same metric to get the difference in utilization. You can also use a chart to visualize the results and sort them by the highest difference in utilization.