Splunk SPLK-4001 Practice Test - Questions Answers, Page 5

The correct answer is D. The detector is automatically linked to the chart.

When you create a detector from a chart on a custom dashboard, the detector is automatically linked to the chart. This means that you can see the detector status and alerts on the chart, and you can access the detector settings from the chart menu. You can also unlink the detector from the chart if you want to1

Changes made to the chart do not affect the detector, and changes made to the detector do not affect the chart. The detector and the chart are independent entities that have their own settings and parameters. However, if you change the metric or dimension of the chart, you might lose the link to the detector1

The alerts generated by the detector will show up in the Alerts page, where you can view, manage, and acknowledge them. You can also see them on the team landing page if you assign the detector to a team2

To learn more about how to create and link detectors from charts on custom dashboards, you can refer to this documentation1.

1: https://docs.splunk.com/observability/alerts-detectors-notifications/link-detectors-to-charts.html 2: https://docs.splunk.com/observability/alerts-detectors-notifications/view-manage-alerts.html

According to the web search results1, subscribing to a built-in detector is a way to receive alerts and notifications from Splunk Observability Cloud when certain criteria are met.A built-in detector is a detector that is automatically created and configured by Splunk Observability Cloud based on the data from your integrations, such as AWS, Kubernetes, or OpenTelemetry1. To subscribe to a built-in detector, you need to do the following steps:

Find the built-in detector that you want to subscribe to.You can use the metric finder or the dashboard groups to locate the built-in detectors that are relevant to your data sources1.

Hover over the built-in detector and click the Subscribe button.This will open a dialog box where you can configure your subscription settings1.

Choose an outbound notification channel from the drop-down menu. This is where you can specify how you want to receive the alert notifications from the built-in detector.You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on2.You can also create a new notification channel by clicking the + icon2.

Enter the notification details for the selected channel.This may include your email address, Slack channel name, PagerDuty service key, webhook URL, and so on2.You can also customize the notification message with variables and markdown formatting2.

Click Save. This will subscribe you to the built-in detector and send you alert notifications through the chosen channel when the detector triggers or clears an alert.

Therefore, option C is correct.

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

The correct answer is C. Create a plot C using the formula (A/B-l) and add a scale: 100 function to express the rate of change as a percentage.

To calculate the rate of change in latency, you need to compare the current latency (plot A) with the latency a week ago (plot B). One way to do this is to use the formula (A/B-l), which gives you the ratio of the current latency to the previous latency minus one. This ratio represents how much the current latency has increased or decreased relative to the previous latency. For example, if the current latency is 200 ms and the previous latency is 100 ms, then the ratio is (200/100-l) = 1, which means the current latency is 100% higher than the previous latency1

To express the rate of change as a percentage, you need to multiply the ratio by 100. You can do this by adding a scale: 100 function to the formula. This function scales the values of the plot by a factor of 100. For example, if the ratio is 1, then the scaled value is 100%2

To create a plot C using the formula (A/B-l) and add a scale: 100 function, you need to follow these steps:

Select plot A and plot B from the Metric Finder.

Click on Add Analytics and choose Formula from the list of functions.

In the Formula window, enter (A/B-l) as the formula and click Apply.

Click on Add Analytics again and choose Scale from the list of functions.

In the Scale window, enter 100 as the factor and click Apply.

You should see a new plot C that shows the rate of change in latency as a percentage.

To learn more about how to use formulas and scale functions in Splunk Observability Cloud, you can refer to these documentations34.

1: https://www.mathsisfun.com/numbers/percentage-change.html 2: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale 3: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Formula 4: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale

historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern1.Historical anomaly uses machine learning to learn the normal behavior of a signal based on its past data, and then compares the current value of the signal with the expected value based on the learned pattern1.You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles1.

Historical anomaly is suitable for creating a detector for the customer's data, because it can account for the expected and consistent increase in traffic during November each year.Historical anomaly can learn that the traffic pattern has a seasonal component that peaks in November, and then adjust the expected value of the traffic accordingly1. This way, historical anomaly can avoid triggering alerts when the traffic increases in November, as this is not an anomaly, but rather a normal variation.However, historical anomaly can still trigger alerts when the traffic deviates from the historical pattern in other ways, such as if it drops significantly or spikes unexpectedly1.

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.

The component of the OpenTelemetry Collector that allows for the modification of metadata is A. Processors.

Processors are components that can modify the telemetry data before sending it to exporters or other components. Processors can perform various transformations on metrics, traces, and logs, such as filtering, adding, deleting, or updating attributes, labels, or resources. Processors can also enrich the telemetry data with additional metadata from various sources, such as Kubernetes, environment variables, or system information1

For example, one of the processors that can modify metadata is the attributes processor. This processor can update, insert, delete, or replace existing attributes on metrics or traces. Attributes are key-value pairs that provide additional information about the telemetry data, such as the service name, the host name, or the span kind2

Another example is the resource processor. This processor can modify resource attributes on metrics or traces. Resource attributes are key-value pairs that describe the entity that produced the telemetry data, such as the cloud provider, the region, or the instance type3

To learn more about how to use processors in the OpenTelemetry Collector, you can refer to this documentation1.

1: https://opentelemetry.io/docs/collector/configuration/#processors 2: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor 3: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor

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

The correct answers are A and D.

According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the alerting concepts that is covered in the exam is detectors and alerts. Detectors are the objects that define the conditions for generating alerts, and alerts are the notifications that are sent when those conditions are met.

The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Alerting with Detectors, which covers how to create, modify, and manage detectors and alerts.

In the Alerting with Detectors course, there is a section on Cloning Detectors, which explains that cloning a detector creates a copy of the detector with all its settings, rules, and alert recipients. The document also provides some reasons why you might want to clone a detector, such as:

To modify the rules without affecting the existing detector. This can be useful if you want to test different thresholds or conditions before applying them to the original detector.

To explore how a detector was created without risk of changing it. This can be helpful if you want to learn from an existing detector or use it as a template for creating a new one.

Therefore, based on these documents, we can conclude that A and D are accurate reasons to clone a detector. B and C are not valid reasons because:

Cloning a detector does not reduce the amount of billed TAPM for the detector. TAPM stands for Tracked Active Problem Metric, which is a metric that has been alerted on by a detector. Cloning a detector does not change the number of TAPM that are generated by the original detector or the clone.

Cloning a detector does not add an additional recipient to the detector's alerts. Cloning a detector copies the alert recipients from the original detector, but it does not add any new ones. To add an additional recipient to a detector's alerts, you need to edit the alert settings of the detector.