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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to Alerting on Metrics using Detectors | 10% | - Clone an existing detector - Make a muting rule - Make the detector from a chart - Make a solo detector |
| Topic 2: Create Efficient Dashboards and Alerts | 10% | - Include instructions on interfaces - Create single-instance dashboard panels - View dashboard happenings - Configure personal information linkages |
| Topic 3: Metrics Concepts | 15% | - Data resolution, rollups - Discriminate between types of metadata - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - List the components of a datapoint |
| Topic 4: 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 |
| Topic 5: Introduction to Metric Visualization | 15% | - Create charts and dashboards - Use rollups and analytical tools correctly - Look for metrics - Create widgets and showcase groups - Differentiate between several chart visualization types - Visualize a measure in a chart - Analyze data in charts |
| Topic 6: Monitor Using Built-in Content | 10% | - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Subscribe to alerts - Interact with data using built-in content - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems |
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NEW QUESTION # 100
To smooth a very spiky cpu.utilization metric, what is the correct analytic function to better see if the cpu.
utilization for servers is trending up over time?
Answer: C
Explanation:
Explanation
The correct answer is D. Mean (Transformation).
According to the web search results, a mean transformation is an analytic function that returns the average value of a metric or a dimension over a specified time interval1. A mean transformation can be used to smooth a very spiky metric, such as cpu.utilization, by reducing the impact of outliers and noise. A mean transformation can also help to see if the metric is trending up or down over time, by showing the general direction of the average value. For example, to smooth the cpu.utilization metric and see if it is trending up over time, you can use the following SignalFlow code:
mean(1h, counters("cpu.utilization"))
This will return the average value of the cpu.utilization counter metric for each metric time series (MTS) over the last hour. You can then use a chart to visualize the results and compare the mean values across different MTS.
Option A is incorrect because rate/sec is not an analytic function, but rather a rollup function that returns the rate of change of data points in the MTS reporting interval1. Rate/sec can be used to convert cumulative counter metrics into counter metrics, but it does not smooth or trend a metric. Option B is incorrect because median is not an analytic function, but rather an aggregation function that returns the middle value of a metric or a dimension over the entire time range1. Median can be used to find the typical value of a metric, but it does not smooth or trend a metric. Option C is incorrect because mean (by host) is not an analytic function, but rather an aggregation function that returns the average value of a metric or a dimension across all MTS with the same host dimension1. Mean (by host) can be used to compare the performance of different hosts, but it does not smooth or trend a metric.
Mean (Transformation) is an analytic function that allows you to smooth a very spiky metric by applying a moving average over a specified time window. This can help you see the general trend of the metric over time, without being distracted by the short-term fluctuations1 To use Mean (Transformation) on a cpu.utilization metric, you need to select the metric from the Metric Finder, then click on Add Analytics and choose Mean (Transformation) from the list of functions. You can then specify the time window for the moving average, such as 5 minutes, 15 minutes, or 1 hour. You can also group the metric by host or any other dimension to compare the smoothed values across different servers2 To learn more about how to use Mean (Transformation) and other analytic functions in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Mean-Transformation 2:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html
NEW QUESTION # 101
What information is needed to create a detector?
Answer: A
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 # 102
When metrics in a detector have different dimensions, a correlation conflict can occur, which prevents an alert from firing. Which of the choices below is the best method to resolve a correlation conflict?
Answer: B
Explanation:
Applying an aggregate analytic function resolves correlation conflicts by summarizing metrics with different dimensions into a common value, allowing the detector to evaluate the alert condition across all relevant metrics consistently.
NEW QUESTION # 103
When a user selects the "New detector from chart" feature, which of the following configurations is predefined in the detector?
Answer: A
Explanation:
The "New detector from chart" feature automatically predefines the alert signal in the detector based on the metric or data visualized in the chart. Other configurations like conditions, recipients, and alert settings need to be set manually.
NEW QUESTION # 104
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: D
Explanation:
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 type.
NEW QUESTION # 105
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