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According to the Splunk O11y Cloud Certified Metrics User Track document1, a single-instance dashboard is a type of dashboard that displays charts and information for a single instance of a service or host. You can use a single-instance dashboard to monitor the performance and health of a specific server, such as the one that is restarting due to power supply issues. You can also create detectors for the metrics that are relevant to the server, such as CPU usage, memory usage, disk usage, and uptime. Therefore, option A is correct.

The correct answer is A. Only metrics with a dimension of host and a value beginning with test-.This filter returns the metrics that have a host dimension that matches the pattern test-. For example, test-01, test-abc, test-xyz, etc. The asterisk () is a wildcard character that can match any string of characters1 To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation2.1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2: https://docs.splunk.com/Observability/gdi/metrics/search.html

According to the Splunk O11y Cloud Certified Metrics User Track document1, percentages and ratios are useful for calculating the proportion of one metric to another, such as cache hits to cache misses, or successful requests to failed requests. You can use the percentage() or ratio() functions in SignalFlow to compute these values and display them in charts. For example, to calculate the cache hit rate for a service, you can use the following SignalFlow code:percentage(counters(''cache.hits''), counters(''cache.misses''))This will return the percentage of cache hits out of the total number of cache attempts. You can also use the ratio() function to get the same result, but as a decimal value instead of a percentage.ratio(counters(''cache.hits''), counters(''cache.misses''))

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 result1. You can also see the agent and gateway configuration files in the same result for more details.1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

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 it1Adding 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 Support2To 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

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.

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 result1, 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 documentation2.1: https://docs.splunk.com/Observability/gdi/opentelemetry/install-linux-manual.html  2: https://docs.splunk.com/Observability/gdi/opentelemetry.html

According to the Splunk Observability Cloud documentation1, 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.

The correct answer is D. Metric type.A metric type is an optional, but highly recommended field that specifies the kind of measurement that a datapoint represents. For example, a metric type can be gauge, counter, cumulative counter, or histogram.A metric type helps Splunk Observability Cloud to interpret and display the data correctly1To learn more about how to send metrics to Splunk Observability Cloud, you can refer to this documentation2.1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types  2: https://docs.splunk.com/Observability/gdi/metrics/metrics.html

According to the Splunk Observability Cloud documentation1, the maximum function is an analytic function that returns the highest value of a metric or a dimension over a specified time interval. The maximum function can be used as a transformation or an aggregation. A transformation applies the function to each metric time series (MTS) individually, while an aggregation applies the function to all MTS and returns a single value. For example, to discover the peak page visits for a site over the last day, you can use the following SignalFlow code:maximum(24h, counters(''page.visits''))This will return the highest value of the page.visits counter metric for each MTS over the last 24 hours. You can then use a chart to visualize the results and identify the peak page visits for each MTS.