SPLK-4001 Online Practice Questions and Answers

Correct Answer: B

The correct answer is B. A set of data points that all have the same metric name and list of dimensions.

A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:

MTS: Gauge metric cpu.utilization, dimension "hostname": "host" MTS: Gauge metric cpu.utilization, dimension "hostname": "host" MTS: Gauge metric memory.usage, dimension "hostname": "host"

A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is

a combination of a metric, a dimension, a value, and a timestamp

Correct Answer: C

The correct answer is C. Create a plot C using the formula (A/B-l) and add a scale: 100 function to express the rate of change as a percentage. To calculate the rate of change in latency, you need to compare the current latency (plot A) with the latency a week ago (plot B). One way to do this is to use the formula (A/B-l), which gives you the ratio of the current latency to the previous latency minus one. This ratio represents how much the current latency has increased or decreased relative to the previous latency. For example, if the current latency is 200 ms and the previous latency is 100 ms, then the ratio is (200/100-l) = 1, which means the current latency is 100% higher than the previous latency To express the rate of change as a percentage, you need to multiply the ratio by 100. You can do this by adding a scale: 100 function to the formula. This function scales the values of the plot by a factor of 100. For example, if the ratio is 1, then the scaled value is 100%2 To create a plot C using the formula (A/B-l) and add a scale: 100 function, you need to follow these steps: Select plot A and plot B from the Metric Finder. Click on Add Analytics and choose Formula from the list of functions. In the Formula window, enter (A/B-l) as the formula and click Apply. Click on Add Analytics again and choose Scale from the list of functions. In the Scale window, enter 100 as the factor and click Apply. You should see a new plot C that shows the rate of change in latency as a percentage. To learn more about how to use formulas and scale functions in Splunk Observability Cloud, you can refer to these documentations. https://www.mathsisfun.com/numbers/percentage-change.html https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Formula https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale

Correct Answer: AD

According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud1. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA. To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests2. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings3. Therefore, option A and D are correct.

Correct Answer: A

According to the Splunk O11y Cloud Certified Metrics User Track document, 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"))

Correct Answer: A

The correct answer is A. 64. 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 362 To learn more about how to use properties and dimensions in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions- mts.html#Custom-properties https://docs.splunk.com/Observability/metrics-and- metadata/metrics-dimensions-mts.html

Correct Answer: AD

According to the Splunk Lantern article Resolving flapping detectors in Splunk Infrastructure Monitoring, flapping is a phenomenon where alerts fire and clear repeatedly in a short period of time, due to the signal fluctuating around the

threshold value. To reduce flapping, the article suggests the following ways:

Configure a duration or percent of duration for the alert: This means that you require the signal to stay above or below the threshold for a certain amount of time or percentage of time before triggering an alert. This can help filter out noise and

focus on more persistent issues.

Apply a smoothing transformation (like a rolling mean) to the input data for the detector: This means that you replace the original signal with the average of its last several values, where you can specify the window length. This can reduce the

impact of a single extreme observation and make the signal less fluctuating.

Correct Answer: A

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 characters To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics https://docs.splunk.com/Observability/gdi/metrics/search.html

Correct Answer: A

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.

Correct Answer: AD

The correct answers are A and D.

According to the Get started with Splunk Observability Cloud document1, one of the topics that is covered in the Getting Data into Splunk Observability Cloud course is global and local data links. Data links are shortcuts that provide

convenient access to related resources, such as Splunk Observability Cloud dashboards, Splunk Cloud Platform and Splunk Enterprise, custom URLs, and Kibana logs. The document explains that there are two types of data links: global and

local. Global data links are available on all dashboards and charts, while local data links are available on only one dashboard. The document also provides the following information about local data links:

Anyone with write permission for a dashboard can add local data links that appear on that dashboard.

Local data links can have either a Splunk Observability Cloud internal destination or an external destination, such as a custom URL or a Kibana log. Only Splunk Observability Cloud administrators can delete local data links. Therefore, based

on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:

B is false because local data links can have an external destination as well as an internal one.

C is false because anyone with write permission for a dashboard can create local data links, not just administrators.

Correct Answer: C

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.