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| Section | Weight | Objectives |
|---|---|---|
| Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Data retention and resolution settings - Metric types: Gauges, Counters, Histograms, Summaries |
| Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors - Notification rules and incident response |
| Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments |
| Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
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NEW QUESTION # 104
Which analytic function can be used to discover peak page visits for a site over the last day?
Answer: B
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 # 105
Which of the following can be configured when subscribing to a built-in detector?
Answer: C
Explanation:
Explanation
According to the web search results1, subscribing to a built-in detector is a way to receive alerts and notifications from Splunk Observability Cloud when certain criteria are met. A built-in detector is a detector that is automatically created and configured by Splunk Observability Cloud based on the data from your integrations, such as AWS, Kubernetes, or OpenTelemetry1. To subscribe to a built-in detector, you need to do the following steps:
Find the built-in detector that you want to subscribe to. You can use the metric finder or the dashboard groups to locate the built-in detectors that are relevant to your data sources1.
Hover over the built-in detector and click the Subscribe button. This will open a dialog box where you can configure your subscription settings1.
Choose an outbound notification channel from the drop-down menu. This is where you can specify how you want to receive the alert notifications from the built-in detector. You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on2. You can also create a new notification channel by clicking the + icon2.
Enter the notification details for the selected channel. This may include your email address, Slack channel name, PagerDuty service key, webhook URL, and so on2. You can also customize the notification message with variables and markdown formatting2.
Click Save. This will subscribe you to the built-in detector and send you alert notifications through the chosen channel when the detector triggers or clears an alert.
Therefore, option C is correct.
NEW QUESTION # 106
Which of the following are correct ports for the specified components in the OpenTelemetry Collector?
Answer: A
Explanation:
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.
NEW QUESTION # 107
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: A
Explanation:
Explanation
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 information1 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 kind2 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 documentation1.
1: https://opentelemetry.io/docs/collector/configuration/#processors 2:
https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor 3:
https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor
NEW QUESTION # 108
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?
Answer: C
Explanation:
Explanation
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 normal1 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 there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 109
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