Valid Splunk SPLK-4001 Test Questions - Exam SPLK-4001 Preparation

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Splunk SPLK-4001 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Metrics Concepts: This section of the exam measures the skills of Monitoring Specialists and covers fundamental metrics concepts including data resolution, rollups, and components of a datapoint. It also examines the Splunk Infrastructure Monitoring (IM) Data Model, different metric types, and the role of metadata in defining and structuring metric data.
Topic 2
  • Get Metrics In with OpenTelemetry: This section of the exam measures the skills of Observability Engineers and focuses on configuring and managing OpenTelemetry (OTel) for data collection. It covers deploying the OTel Collector on Linux, editing configurations, troubleshooting common issues, and understanding general OpenTelemetry concepts necessary for integrating metrics efficiently.
Topic 3
  • Monitor Using Built-in Content: This section of the exam measures the skills of Site Reliability Engineers and focuses on utilizing Splunk built-in content for effective monitoring. It includes interpreting data in charts, subscribing to alerts, and using tools such as the Kubernetes Navigator, Cluster Analyzer, and pre-built dashboards to identify and resolve performance issues in nodes, pods, and containers.
Topic 4
  • Introduction to Alerting on Metrics with Detectors: This section of the exam measures the skills of Operations Engineers and focuses on setting up and managing metric alerts using detectors. Candidates learn to create detectors from charts, clone and modify them, build standalone detectors, and configure muting rules to manage alert noise and response efficiency.

Splunk O11y Cloud Certified Metrics User Sample Questions (Q38-Q43):

NEW QUESTION # 38
A customer is experiencing issues getting metrics from a new receiver they have configured in the OpenTelemetry Collector. How would the customer go about troubleshooting further with the logging exporter?

Answer: D

Explanation:
Explanation
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


NEW QUESTION # 39
What information is needed to create a detector?

Answer: A

Explanation:
According to the Splunk Observability Cloud documentation, 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.


NEW QUESTION # 40
When you add instructions to dashboards, what is their primary purpose?

Answer: A


NEW QUESTION # 41
When writing a detector with a large number of MTS, such as memory. free in a deployment with
30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot.
Which of the choices below would most likely reduce the number of MTS below the plot cap?

Answer: C

Explanation:
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 it. 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 Support.


NEW QUESTION # 42
Given that the metric demo. trans. count is being sent at a 10 second native resolution, which of the following is an accurate description of the data markers displayed in the chart below?

Answer: C

Explanation:
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 time2 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 AM3 To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations123.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups-in-charts 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions-for-metric-types


NEW QUESTION # 43
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