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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Topic 2: Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Topic 3: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 4: Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings |
| Topic 5: Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment |
| Topic 6: Visualizing Metrics | 20% | - Visualization best practices and effective communication - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries |
| Topic 7: Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors |
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NEW QUESTION # 68
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 # 69
When writing a detector with a large number of MTS, such as memory. free in a deployment with 30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot. Which of the choices below would most likely reduce the number of MTS below the plot cap?
Answer: B
Explanation:
Explanation
The correct answer is B. Add a filter to narrow the scope of the measurement.
A filter is a way to reduce the number of metric time series (MTS) that are displayed on a chart or used in a detector. A filter specifies one or more dimensions and values that the MTS must have in order to be included.
For example, if you want to monitor the memory.free metric only for hosts that belong to a certain cluster, you can add a filter like cluster:my-cluster to the plot or detector. This will exclude any MTS that do not have the cluster dimension or have a different value for it1 Adding a filter can help you avoid exceeding the plot cap, which is the maximum number of MTS that can be contained in a single plot. The plot cap is 100,000 by default, but it can be changed by contacting Splunk Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap 3:
https://docs.splunk.com/Observability/gdi/metrics/search.html
NEW QUESTION # 70
Which of the following best describes the difference between a Chart and a Single Value chart in a dashboard?
Answer: B
Explanation:
A standard Chart is designed to show trends and patterns over a time range, while a Single Value chart focuses on displaying a single, current aggregated number, often used for at-a-glance status indicators.
NEW QUESTION # 71
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:
The correct answer is C. 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 documentation1.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html
NEW QUESTION # 72
What is the limit on the number of properties that an MTS can have?
Answer: B
Explanation:
According to the web search results, the limit on the number of properties that an MTS can have is 64. A property is a key-value pair that you can assign to a dimension of an existing MTS to add more context to the metrics. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host is used for QA.
Properties are different from dimensions, which are key-value pairs that are sent along with the metrics at the time of ingest. Dimensions, along with the metric name, uniquely identify an MTS.
The limit on the number of dimensions per MTS is 36.
NEW QUESTION # 73
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