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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings |
| Topic 2: Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Topic 3: Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors |
| Topic 4: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Topic 5: Visualizing Metrics | 20% | - Using SignalFlow for custom queries - Visualization best practices and effective communication - Creating and configuring charts, graphs, and time-series |
| Topic 6: Data Collection & Ingestion | 15% | - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment |
| Topic 7: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
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NEW QUESTION # 42
A customer wants to create different severity alerts on the same signal, where critical alerts are sent to one recipient and major alerts are sent to a different recipient. Which of the following will accomplish this?
Answer: A
Explanation:
Creating two separate rules in the same detector allows each rule to have its own threshold and recipient configuration, enabling critical alerts to go to one recipient and major alerts to another.
NEW QUESTION # 43
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?
Answer: C
Explanation:
Explanation
The correct answer is B. /etc/otel/collector/
According to the web search results, the Splunk distribution of the OpenTelemetry Collector stores the configuration files on Linux machines in the /etc/otel/collector/ directory by default. You can verify this by looking at the first result1, which explains how to install the Collector for Linux manually. It also provides the locations of the default configuration file, the agent configuration file, and the gateway configuration file.
To learn more about how to install and configure the Splunk distribution of the OpenTelemetry Collector, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/install-linux-manual.html 2:
https://docs.splunk.com/Observability/gdi/opentelemetry.html
NEW QUESTION # 44
Changes to which type of metadata result in a new metric time series?
Answer: C
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 # 45
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
Answer: A
Explanation:
According to the Splunk Observability Cloud documentation, lag is a rollup function that returns the difference between the most recent and the previous data point values seen in the metric time series reporting interval. This can be used to measure the time delta between a data point being sent and a data point being received, as long as the data points have timestamps that reflect their send and receive times. For example, if a data point is sent at 10:00:00 and received at 10:00:05, the lag value for that data point is 5 seconds.
NEW QUESTION # 46
A user wants to reduce the cardinality of a metric before visualizing it on a chart. Which analytic function should be applied to combine multiple time series into a single time series?
Answer: D
Explanation:
Aggregation functions such as Sum combine multiple time series into a single time series by grouping across dimensions, effectively reducing cardinality for easier visualization and analysis.
NEW QUESTION # 47
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