Pass the Splunk O11y Cloud Certified Metrics User SPLK-4001 Questions and answers with ExamsMirror

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 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

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.

1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Mean-Transformation  2: https://docs.splunk.com/Observability/gdi/metrics/analytics.html

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.

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.

The correct answer is A. 64.

According to the web search results, the limit on the number of properties that an MTS can have is 64. A property is a key-value pair that you can assign to a dimension of an existing MTS to add more context to the metrics. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host is used for QA1

Properties are different from dimensions, which are key-value pairs that are sent along with the metrics at the time of ingest. Dimensions, along with the metric name, uniquely identify an MTS. The limit on the number of dimensions per MTS is 362

To learn more about how to use properties and dimensions in Splunk Observability Cloud, you can refer to this documentation2.

1: https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html#Custom-properties 2: https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html

According to the web search results, clicking a metric name from the results in metric finder displays the metric in Chart Builder1. Chart Builder is a tool that allows you to create and customize charts using metrics, dimensions, and analytics functions2. To save the chart created in the UI, you need to do the following steps:

Click the Save button on the top right corner of the Chart Builder. This will open a dialog box where you can enter the chart name and description, and choose the dashboard where you want to save the chart.

Enter a name and a description for your chart. The name should be descriptive and unique, and the description should explain the purpose and meaning of the chart.

Choose an existing dashboard from the drop-down menu, or create a new dashboard by clicking the + icon. A dashboard is a collection of charts that display metrics and events for your services or hosts3. You can organize and share dashboards with other users in your organization using dashboard groups3.

Click Save. This will save your chart to the selected dashboard and redirect you to the dashboard view. You can also access your saved chart from the Dashboards menu on the left navigation bar.

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 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.