SPLK-4001 Sample Questions Answers

According to the Splunk Observability Cloud documentation1, detectors operate at the native resolution of the metric or dimension that they monitor, with some exceptions for transformations or timeshifts. The native resolution is the frequency at which the data points are reported by the source. For example, if a metric is reported every 10 seconds, the detector will evaluate the metric every 10 seconds. The native resolution ensures that the detector uses the most granular and accurate data available for alerting.

One reason a user of Splunk Observability Cloud would want to subscribe to an alert is C. To receive an email notification when a detector is triggered.

A detector is a component of Splunk Observability Cloud that monitors metrics or events and triggers alerts when certain conditions are met. A user can create and configure detectors to suit their monitoring needs and goals1

A subscription is a way for a user to receive notifications when a detector triggers an alert. A user can subscribe to a detector by entering their email address in the Subscription tab of the detector page. A user can also unsubscribe from a detector at any time2

When a user subscribes to an alert, they will receive an email notification that contains information about the alert, such as the detector name, the alert status, the alert severity, the alert time, and the alert message. The email notification also includes links to view the detector, acknowledge the alert, or unsubscribe from the detector2

To learn more about how to use detectors and subscriptions in Splunk Observability Cloud, you can refer to these documentations12.

1: https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html 2: https://docs.splunk.com/Observability/alerts-detectors-notifications/subscribe-to-detectors.html

According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization1. You can create dashboard groups based on different criteria, such as service, team, role, or topic. You can also set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group. Dashboard groups make it possible to share a collection of charts with your entire SRE organization, or any other group of users that you want to collaborate with.

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 : https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors

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.

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.

According to the Splunk O11y Cloud Certified Metrics User Track document1, Observability Cloud has the following rollup functions: Sum: (default for counter metrics): Returns the sum of all data points in the MTS reporting interval. Average (default for gauge metrics): Returns the average value of all data points in the MTS reporting interval. Min: Returns the minimum data point value seen in the MTS reporting interval. Max: Returns the maximum data point value seen in the MTS reporting interval. Latest: Returns the most recent data point value seen in the MTS reporting interval. Lag: Returns the difference between the most recent and the previous data point values seen in the MTS reporting interval. Rate: Returns the rate of change of data points in the MTS reporting interval. Therefore, option A is correct.

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.

According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization1. You can set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group1. To create a dashboard group that no one else can edit, you need to do the following steps:

• Create a dashboard group as usual, by selecting Dashboard Group from the Create menu on the navigation bar, entering a name and description, and adding dashboards to the group1.
• Select Alert settings from the Dashboard actions menu (⋯) on the top right corner of the dashboard group. This will open a dialog box where you can configure the permissions for the dashboard group1.
• Under Write access, select Only me. This will restrict the write access to the dashboard group to yourself only. No one else will be able to edit or delete the dashboards in the group1.
• Click Save. This will create a dashboard group that no one else can edit.

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.

1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html

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

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

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.