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Splunk SPLK-4001 Exam Sample Questions

Question # 1

What happens when the limit of allowed dimensions is exceeded for an MTS?

A. The additional dimensions are dropped.

B. The datapoint is averaged.

C. The datapoint is updated.

D. The datapoint is dropped.

Answer

A. The additional dimensions are dropped.

Explanation:

According to the web search results, dimensions are metadata in the form of key-value pairs that monitoring software sends in along with the metrics. The set of metric time series (MTS) dimensions sent during ingest is used, along with the metric name, to uniquely identify an MTS1. Splunk Observability Cloud has a limit of 36 unique dimensions per MTS2. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud2. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.

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Question # 2

When creating a standalone detector, individual rules in it are labeled according to severity. Which of the choices below represents the possible severity levels that can be selected?

A. Info, Warning, Minor, Major, and Emergency.

B. Debug, Warning, Minor, Major, and Critical.

C. Info, Warning, Minor, Major, and Critical.

D. Info, Warning, Minor, Severe, and Critical.

Answer

C. Info, Warning, Minor, Major, and Critical.

Explanation:

The correct answer is C. Info, Warning, Minor, Major, and Critical.

When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2

To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.

1: https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector 2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels

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Question # 3

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?

A. On the chart for plot A, select Add Analytics, then select MeanrTransformation. In the window that appears, select 'version' from the Group By field.

B. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-l'.

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.

D. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version1.

Answer

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.

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

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Question # 4

For a high-resolution metric, what is the highest possible native resolution of the metric?

A. 2 seconds

B. 15 seconds

C. 1 second

D. 5 seconds

Answer

C. 1 second

Explanation:

The correct answer is C. 1 second.

According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.

The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.

In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1-second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.

Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.

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Question # 5

Which of the following statements about adding properties to MTS are true? (select all that apply)

A. Properties can be set via the API.

B. Properties are sent in with datapoints.

C. Properties are applied to dimension key:value pairs and propagated to all MTS with that dimension

D. Properties can be set in the UI under Metric Metadata.

Answer

A. Properties can be set via the API.

D. Properties can be set in the UI under Metric Metadata.

Explanation:

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.

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