SPLK-4001 Online Practice Questions and Answers

Correct Answer: D

The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006). According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result. You can also see the agent and gateway configuration files in the same result for more details. https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

Correct Answer: B

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 result, 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 documentation. https://docs.splunk.com/Observability/gdi/opentelemetry/install-linux-manual.html https://docs.splunk.com/Observability/gdi/opentelemetry.html

Correct Answer: D

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 time 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 AM To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations. https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups- in-charts https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions- for-metric-types

Correct Answer: ABCD

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 constraints 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 situations 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 factors 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 issues 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 documentation. https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Analyzer-panel

Correct Answer: B

According to the Splunk O11y Cloud Certified Metrics User Track document1, trigger sensitivity is a setting that determines how long a signal must remain above or below a threshold before an alert is triggered. By default, trigger sensitivity is set to Immediate, which means that an alert is triggered as soon as the signal crosses the threshold. This can result in a lot of alerts, especially if the signal fluctuates frequently around the threshold value. To reduce the number of alerts, you can adjust the trigger sensitivity to a longer duration, such as 1 minute, 5 minutes, or 15 minutes. This means that an alert is only triggered if the signal stays above or below the threshold for the specified duration. This can help filter out noise and focus on more persistent issues.

Correct Answer: A

The correct answer is A. Max Delay. Max Delay is a parameter that specifies the maximum amount of time that the analytics engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to 10 minutes, the detector will wait for only a maximum of 10 minutes even if some data points have not arrived. By default, Max Delay is set to Auto, allowing the analytics engine to determine the appropriate amount of time to wait for data points1 In this case, since the customer knows that the data from the over-utilized machine can be delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10 minutes. This will prevent the detector from firing alerts before the data points

arrive, and avoid false positives or missing data

To learn more about how to use Max Delay in Splunk Observability Cloud, you can refer to this documentation.

1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector- options.html#Max-Delay

Correct Answer: C

The correct answer is C. Timeshift.

According to the Splunk Observability Cloud documentation1, timeshift is an analytic function that allows you to compare the current value of a metric with its value at a previous time interval, such as an hour ago or a week ago. You can use

the timeshift function to measure the change in a metric over time and identify trends, anomalies, or patterns. For example, to identify the servers where utilization has increased the most since last week, you can use the following SignalFlow

code:

timeshift(1w, counters("server.utilization"))

This will return the value of the server.utilization counter metric for each server one week ago. You can then subtract this value from the current value of the same metric to get the difference in utilization. You can also use a chart to visualize

the results and sort them by the highest difference in utilization.

Correct Answer: ABCD

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 issues 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 aggregation 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 behavior 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. https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for- detectors https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best- practices-for-detectors https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart : https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for- detectors

Correct Answer: D

historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern. Historical anomaly uses machine learning to learn the normal behavior of a signal based on its past data, and then compares the current value of the signal with the expected value based on the learned pattern. You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles. Historical anomaly is suitable for creating a detector for the customer's data, because it can account for the expected and consistent increase in traffic during November each year. Historical anomaly can learn that the traffic pattern has a seasonal component that peaks in November, and then adjust the expected value of the traffic accordingly. This way, historical anomaly can avoid triggering alerts when the traffic increases in November, as this is not an anomaly, but rather a normal variation. However, historical anomaly can still trigger alerts when the traffic deviates from the historical pattern in other ways, such as if it drops significantly or spikes unexpectedly.

Correct Answer: ACD

The required components in the configuration of a data point are: Metric Name: A metric name is a string that identifies the type of measurement that the data point represents, such as cpu.utilization, memory.usage, or response.time. A metric name is mandatory for every data point, and it must be unique within a Splunk Observability Cloud organization1 Timestamp: A timestamp is a numerical value that indicates the time at which the data point was collected or generated. A timestamp is mandatory for every data point, and it must be in epoch time format, which is the number of seconds since January, 1970 UTC Value: A value is a numerical value that indicates the magnitude or quantity of the measurement that the data point represents. A value is mandatory for every data point, and it must be compatible with the metric type of the data point Therefore, the correct answer is A, C, and D. To learn more about how to configure data points in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Data-points