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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors - Notification rules and incident response |
| Topic 2: Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality - Data retention and resolution settings |
| Topic 3: Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Topic 4: Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Topic 5: Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments |
| Topic 6: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Topic 7: Visualizing Metrics | 20% | - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series - Visualization best practices and effective communication |
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NEW QUESTION # 37
A customer has a dashboard that tracks multiple performance metrics for hosts related to a production application. They want to create an alert based on an existing chart that shows them the p90 latency for a service of their application. What is the most efficient way to achieve this?
Answer: A
Explanation:
Creating a new detector from an existing chart allows the alert to leverage the same signals and analytic functions, such as p90 latency, efficiently without having to rebuild the logic manually.
NEW QUESTION # 38
What is the purpose of muting rules in Splunk Observability Cloud?
Answer: D
Explanation:
Muting rules allow users to temporarily suppress notifications from a detector, which is useful during planned maintenance windows or known issues, without disabling or deleting the detector itself.
NEW QUESTION # 39
What are the best practices for creating detectors? (select all that apply)
Answer: A,B,C,D
Explanation:
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues1 Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation2 View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior3 Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
2: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
3: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart
4: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors
NEW QUESTION # 40
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard? (select all that apply)
Answer: B,C
Explanation:
The chart visualization types that are unaffected by changing the time picker on a dashboard are:
Single Value: A single value chart shows the current value of a metric or an expression. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart1 List: A list chart shows the values of a metric or an expression for each dimension value in a table format. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart2 Therefore, the correct answer is A and D.
To learn more about how to use different chart visualization types in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Single-value 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#List 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html
NEW QUESTION # 41
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
Answer: C
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 # 42
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