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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Topic 2: Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors - Notification rules and incident response |
| Topic 3: Data Collection & Ingestion | 15% | - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment |
| Topic 4: Metrics Concepts | 20% | - Data retention and resolution settings - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality |
| Topic 5: Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Topic 6: Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Topic 7: Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
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NEW QUESTION # 123
Which chart type in Splunk Observability Cloud is best suited for visualizing the proportion of time a resource spends in different states?
Answer: D
Explanation:
A Heatmap is well-suited for visualizing many time series at once and showing how values (such as states or utilization levels) are distributed across a large number of instances over time.
NEW QUESTION # 124
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 # 125
A customer has an over-utilized host, and they want an alert when the disk utilization of a given host goes over 80% for 5 minutes. They create a detector, but do not see an alert when expected. Which of the following could be a reason for this?
Answer: D
Explanation:
If a detector's resolution is too low, it may miss short-duration spikes in metrics. Setting the detector resolution appropriately ensures that sustained conditions, like disk utilization over 80% for 5 minutes, are accurately evaluated and trigger alerts.
NEW QUESTION # 126
A Software Engineer is troubleshooting an issue with memory utilization in their application. They released a new canary version to production and now want to determine if the average memory usage is lower for requests with the 'canary' version dimension. They've already opened the graph of memory utilization for their service.
How does the engineer see if the new release lowered average memory utilization?
Answer: A
Explanation:
On the chart for plot A, select Add Analytics, then select Mean: Aggregation. In the window that appears, select `version' from the Group By field. This will create a new plot B that shows the average memory utilization for each version of the application. The engineer can then compare the values of plot B for the `canary' and `stable' versions to see if there is a significant difference.
To learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation.
NEW QUESTION # 127
Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?
Answer: A
Explanation:
Explanation
The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period1 To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 128
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