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

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. 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 C. 401 (UNAUTHORIZED).

According to the web search results, a 401 (UNAUTHORIZED) error message is indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector1. A 401 (UNAUTHORIZED) error message means that the request was not authorized by the server due to invalid credentials. A realm is a parameter that specifies the scope of protection for a resource, such as a Splunk Observability Cloud endpoint. An access token is a credential that grants access to a resource, such as a Splunk Observability Cloud API. If the realm or the access token is misconfigured, the request to install OpenTelemetry Collector will be rejected by the server with a 401 (UNAUTHORIZED) error message.

Option A is incorrect because a 403 (NOT ALLOWED) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 403 (NOT ALLOWED) error message means that the request was authorized by the server but not allowed due to insufficient permissions. Option B is incorrect because a 404 (NOT FOUND) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 404 (NOT FOUND) error message means that the request was not found by the server due to an invalid URL or resource. Option D is incorrect because a 503 (SERVICE UNREACHABLE) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 503 (SERVICE UNREACHABLE) error message means that the server was unable to handle the request due to temporary overload or maintenance.

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.

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 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

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.

1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2: https://docs.splunk.com/Observability/gdi/metrics/metrics.html

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 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

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 A. Max Delay.

Max Delay is a parameter that specifies the maximum amount of time that the analytics engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to 10 minutes, the detector will wait for only a maximum of 10 minutes even if some data points have not arrived. By default, Max Delay is set to Auto, allowing the analytics engine to determine the appropriate amount of time to wait for data points1

In this case, since the customer knows that the data from the over-utilized machine can be delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10 minutes. This will prevent the detector from firing alerts before the data points arrive, and avoid false positives or missing data1

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

1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector-options.html#Max-Delay

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.

1: https://docs.splunk.com/Observability/gdi/opentelemetry/install-linux-manual.html 2: https://docs.splunk.com/Observability/gdi/opentelemetry.html

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 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