SPLK-4001 Splunk O11y Cloud Certified Metrics User Exam Questions and Answers

The correct answers are A and D.

According to the Get started with Splunk Observability Cloud document1, one of the topics that is covered in the Getting Data into Splunk Observability Cloud course is global and local data links. Data links are shortcuts that provide convenient access to related resources, such as Splunk Observability Cloud dashboards, Splunk Cloud Platform and Splunk Enterprise, custom URLs, and Kibana logs.

The document explains that there are two types of data links: global and local. Global data links are available on all dashboards and charts, while local data links are available on only one dashboard. The document also provides the following information about local data links:

Anyone with write permission for a dashboard can add local data links that appear on that dashboard.

Local data links can have either a Splunk Observability Cloud internal destination or an external destination, such as a custom URL or a Kibana log.

Only Splunk Observability Cloud administrators can delete local data links.

Therefore, based on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:

B is false because local data links can have an external destination as well as an internal one.

C is false because anyone with write permission for a dashboard can create local data links, not just administrators.

According to the Splunk Observability Cloud documentation1, lag is a rollup function that returns the difference between the most recent and the previous data point values seen in the metric time series reporting interval. This can be used to measure the time delta between a data point being sent and a data point being received, as long as the data points have timestamps that reflect their send and receive times. For example, if a data point is sent at 10:00:00 and received at 10:00:05, the lag value for that data point is 5 seconds.

The possible ways to add a link to an existing dashboard from an alert are:

Build a global data link. A global data link is a feature that allows you to create a link from any dimension value in any chart or table to a dashboard of your choice. You can specify the source and target dashboards, the dimension name and value, and the query parameters to pass along. When you click on the dimension value in the alert message, you will be taken to the dashboard with the context preserved1

Add a link to the field. A field link is a feature that allows you to create a link from any field value in any search result or alert message to a dashboard of your choice. You can specify the field name and value, the dashboard name and ID, and the query parameters to pass along. When you click on the field value in the alert message, you will be taken to the dashboard with the context preserved2

Therefore, the correct answer is A and C.

To learn more about how to use global data links and field links in Splunk Observability Cloud, you can refer to these documentations12.

1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Global-data-links 2: https://docs.splunk.com/Observability/gdi/metrics/search.html#Field-links

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

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

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 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 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 D. gRPC (4317), SignalFx (9080), Fluentd (8006).

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 result1. You can also see the agent and gateway configuration files in the same result for more details.

1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

The correct answer is B. A set of data points that all have the same metric name and list of dimensions.

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 timestamp1

The correct answer is C. Info, Warning, Minor, Major, and Critical.

When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1

Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2

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

1: https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector 2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels

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.

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