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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Visualizing Metrics | 20% | - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries - Visualization best practices and effective communication |
| Topic 2: Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Topic 3: Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings - Dimensions, time-series data, and cardinality |
| Topic 4: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Topic 5: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 6: Alerting with Detectors | 15% | - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors |
| Topic 7: Data Collection & Ingestion | 15% | - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment |
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NEW QUESTION # 16
A user wants to be alerted when a detector created by someone else on their team fires. How can this user get notifications from the detector?
Answer: C
Explanation:
By subscribing to a detector, a user receives notifications whenever the detector fires, even if they did not create it, allowing them to stay informed about relevant alerts from their team's detectors.
NEW QUESTION # 17
To configure the OTel Collector, which file is typically used to define the settings for receivers, exporters, and processors?
Answer: D
NEW QUESTION # 18
What is the recommended approach to monitor a large number of metric time series (MTS)?
Answer: D
Explanation:
Using dimensions to filter and break down metric time series allows you to organize and manage large numbers of MTS efficiently. Dimensions provide context and enable grouping, filtering, and targeted analysis without overwhelming dashboards or alerts.
NEW QUESTION # 19
Changes to which type of metadata result in a new metric time series?
Answer: B
Explanation:
Explanation
The correct answer is A. Dimensions.
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS)1 Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host2, you will create a new MTS for the same metric name1 Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest.
They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed2 To learn more about how to use metadata in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/metrics-and-metadata/metrics.html#Dimensions 2:
https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html
NEW QUESTION # 20
Which analytic function can be used to discover peak page visits for a site over the last day?
Answer: A
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 # 21
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