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| Section | Weight | Objectives |
|---|---|---|
| Intro to Alerting on Metrics using Detectors | 10% | - Make a muting rule - Make a solo detector - Make the detector from a chart - Clone an existing detector |
| Monitor Using Built-in Content | 10% | - Subscribe to alerts - Use the Cluster Analyzer to pinpoint the root of some problems - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Interact with data using built-in content |
| Introduction to Metric Visualization | 15% | - Look for metrics - Use rollups and analytical tools correctly - Differentiate between several chart visualization types - Create charts and dashboards - Create widgets and showcase groups - Visualize a measure in a chart - Analyze data in charts |
| Metrics Concepts | 15% | - Discriminate between types of metadata - Data resolution, rollups - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - List the components of a datapoint |
| Get Metrics In with OpenTelemetry | 10% | - Configure the OTel Collector - General OpenTelemetry Concepts - Troubleshooting common errors - Deploy the OTel Collector on Linux - Edit the configuration |
| Create Efficient Dashboards and Alerts | 10% | - Create single-instance dashboard panels - Configure personal information linkages - View dashboard happenings - Include instructions on interfaces |
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NEW QUESTION # 25
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: B
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 # 26
What information is needed to create a detector?
Answer: B
Explanation:
Explanation
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.
NEW QUESTION # 27
Which feature allows users to receive an alert notification via Slack, email, or PagerDuty when a detector triggers?
Answer: D
Explanation:
Notification integrations allow detectors to send alerts to external systems such as Slack, email, PagerDuty, and others, ensuring the right teams are notified when an alert condition is triggered.
NEW QUESTION # 28
Which analytic function can be used to discover peak page visits for a site over the last day?
Answer: C
Explanation:
Explanation
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.
NEW QUESTION # 29
Which of the following visualization types exist within Splunk Observability Cloud? (Choose all that apply.)
Answer: A,B,D
Explanation:
Splunk Observability Cloud supports Table, Single Value, and Pie Chart visualizations to present metric data in structured, summarized, and categorical forms, enabling flexible analysis and reporting.
NEW QUESTION # 30
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