SPLK-4001 Online Practice Questions and Answers

Correct Answer: A

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.

Correct Answer: D

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 result. You can also see the agent and gateway configuration files in the same result for more details. https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

Correct Answer: D

The correct answer is D. Each data marker represents the sum of API calls in the hour leading up to the data marker. The metric demo.trans.count is a cumulative counter metric, which means that it represents the total number of API calls since the start of the measurement. A cumulative counter metric can be used to measure the rate of change or the sum of events over a time period1 The chart below shows the metric demo.trans.count with a one-hour rollup and a line chart type. A rollup is a way to aggregate data points over a specified time interval, such as one hour, to reduce the number of data points displayed on a chart. A line chart type connects the data points with a line to show the trend of the metric over time Each data marker on the chart represents the sum of API calls in the hour leading up to the data marker. This is because the rollup function for cumulative counter metrics is sum by default, which means that it adds up all the data points in each time interval. For example, the data marker at 10:00 AM shows the sum of API calls from 9:00 AM to 10:00 AM To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations. https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups- in-charts https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions- for-metric-types

Correct Answer: B

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:

MTS: Gauge metric cpu.utilization, dimension "hostname": "host" MTS: Gauge metric cpu.utilization, dimension "hostname": "host" MTS: Gauge metric memory.usage, dimension "hostname": "host"

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 timestamp

Correct Answer: A

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 MTS. Splunk Observability Cloud has a limit of 36 unique dimensions per MTS. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.

Correct Answer: D

The most likely root cause of the issue is D. The detector has a muting rule. A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation.

Correct Answer: A

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

Correct Answer: ABD

According to the web search results, 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.

Correct Answer: A

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 host, 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 changed To learn more about how to use metadata in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/metrics-and-metadata/metrics.html#Dimensions https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html

Correct Answer: A

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 information 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 kind 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 documentation. https://opentelemetry.io/docs/collector/configuration/#processors https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor https://github.com/open- telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor