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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor Using Built-in Content | 10% | - Interact with data using built-in content - Subscribe to alerts - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution |
| Topic 2: Intro to Alerting on Metrics using Detectors | 10% | - Make a solo detector - Make a muting rule - Make the detector from a chart - Clone an existing detector |
| Topic 3: Introduction to Metric Visualization | 15% | - Visualize a measure in a chart - Analyze data in charts - Create widgets and showcase groups - Use rollups and analytical tools correctly - Create charts and dashboards - Differentiate between several chart visualization types - Look for metrics |
| Topic 4: Create Efficient Dashboards and Alerts | 10% | - View dashboard happenings - Configure personal information linkages - Create single-instance dashboard panels - Include instructions on interfaces |
| Topic 5: Metrics Concepts | 15% | - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - List the components of a datapoint - Discriminate between types of metadata - Data resolution, rollups |
| Topic 6: Get Metrics In with OpenTelemetry | 10% | - Edit the configuration - Deploy the OTel Collector on Linux - Troubleshooting common errors - General OpenTelemetry Concepts - Configure the OTel Collector |
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NEW QUESTION # 17
For the mean analytic function, what steps are needed to create a time series where each point is the mean of values over the previous sliding window leading up to that point?
Answer: A
Explanation:
To create a time series with each point representing the mean over a sliding window, you must select the analytic function, apply it as a transformation, and specify the size of the time window.
This ensures that the mean is calculated over the defined interval leading up to each point.
NEW QUESTION # 18
A customer wants to share a collection of charts with their entire SRE organization. What feature of Splunk Observability Cloud makes this possible?
Answer: D
Explanation:
According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization1. You can create dashboard groups based on different criteria, such as service, team, role, or topic. You can also set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group. Dashboard groups make it possible to share a collection of charts with your entire SRE organization, or any other group of users that you want to collaborate with.
NEW QUESTION # 19
A customer is sending in datapoints regarding the number of API calls made to a particular service. The datapoints are being sent every 10 seconds, and represent the number of API calls seen in the last 10 seconds. What metric type is this?
Answer: D
Explanation:
A gauge metric represents a value measured at a specific point in time or over a defined interval.
In this scenario, the datapoints indicate the number of API calls observed in each 10-second interval, which aligns with the characteristics of a gauge metric.
NEW QUESTION # 20
What information is needed to create a detector?
Answer: D
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 # 21
What are all the possible components of a datapoint in Splunk Observability Cloud?
Answer: A
Explanation:
A datapoint in Splunk Observability Cloud includes the metric type, metric name, dimensions, metric value, and timestamp. These components identify what is being measured, provide contextual metadata, record the measured value, and associate it with a specific time.
NEW QUESTION # 22
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