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| Section | Weight | Objectives |
|---|---|---|
| Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Alerting with Detectors | 15% | - Notification rules and incident response - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors |
| Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline |
| Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality - Data retention and resolution settings |
| Visualizing Metrics | 20% | - Visualization best practices and effective communication - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries |
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NEW QUESTION # 59
What does the "Sudden Change" detector condition type identify?
Answer: A
Explanation:
The Sudden Change condition compares recent signal behavior to a recent historical window and alerts when there is a significant, abrupt deviation, making it useful for detecting spikes or drops.
NEW QUESTION # 60
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 # 61
When installing OpenTelemetry Collector, which error message is indicative that there is a misconfigured realm or access token?
Answer: C
Explanation:
The correct answer is C. 401 (UNAUTHORIZED).
According to the web search results, a 401 (UNAUTHORIZED) error message is indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector1. A 401 (UNAUTHORIZED) error message means that the request was not authorized by the server due to invalid credentials. A realm is a parameter that specifies the scope of protection for a resource, such as a Splunk Observability Cloud endpoint. An access token is a credential that grants access to a resource, such as a Splunk Observability Cloud API. If the realm or the access token is misconfigured, the request to install OpenTelemetry Collector will be rejected by the server with a 401 (UNAUTHORIZED) error message.
Option A is incorrect because a 403 (NOT ALLOWED) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 403 (NOT ALLOWED) error message means that the request was authorized by the server but not allowed due to insufficient permissions. Option B is incorrect because a 404 (NOT FOUND) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 404 (NOT FOUND) error message means that the request was not found by the server due to an invalid URL or resource. Option D is incorrect because a 503 (SERVICE UNREACHABLE) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 503 (SERVICE UNREACHABLE) error message means that the server was unable to handle the request due to temporary overload or maintenance.
NEW QUESTION # 62
Given that the metric demo. trans. count is being sent at a 10 second native resolution, which of the following is an accurate description of the data markers displayed in the chart below?
Answer: B
Explanation:
The correct answer is D. Each data marker represents the sum of API calls in the hour leading up to the data marker.
The metric demo.trans.count is a cumulative counter metric, which means that it represents the total number of API calls since the start of the measurement. A cumulative counter metric can be used to measure the rate of change or the sum of events over a time period1 The chart below shows the metric demo.trans.count with a one-hour rollup and a line chart type. A rollup is a way to aggregate data points over a specified time interval, such as one hour, to reduce the number of data points displayed on a chart. A line chart type connects the data points with a line to show the trend of the metric over time2 Each data marker on the chart represents the sum of API calls in the hour leading up to the data marker. This is because the rollup function for cumulative counter metrics is sum by default, which means that it adds up all the data points in each time interval. For example, the data marker at 10:00 AM shows the sum of API calls from 9:00 AM to 10:00 AM3 To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations123.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups-in-charts 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions-for-metric-types
NEW QUESTION # 63
What Pod conditions does the Analyzer panel in Kubernetes Navigator monitor? (select all that apply)
Answer: A,B,C,D
Explanation:
The Pod conditions that the Analyzer panel in Kubernetes Navigator monitors are:
Not Scheduled: This condition indicates that the Pod has not been assigned to a Node yet. This could be due to insufficient resources, node affinity, or other scheduling constraints1 Unknown: This condition indicates that the Pod status could not be obtained or is not known by the system. This could be due to communication errors, node failures, or other unexpected situations1 Failed: This condition indicates that the Pod has terminated in a failure state. This could be due to errors in the application code, container configuration, or external factors1 Pending: This condition indicates that the Pod has been accepted by the system, but one or more of its containers has not been created or started yet. This could be due to image pulling, volume mounting, or network issues1 Therefore, the correct answer is A, B, C, and D.
To learn more about how to use the Analyzer panel in Kubernetes Navigator, you can refer to this documentation2.
1: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase 2: https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Analyzer-panel
NEW QUESTION # 64
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