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.
|
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.
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.
|
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
Question # 3
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?
| A. /opt/splunk/
| B. /etc/otel/collector/
| C. /etc/opentelemetry/
| D. /etc/system/default/
|
B. /etc/otel/collector/
Explanation:
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
Question # 4
With exceptions for transformations or timeshifts, at what resolution do detectors operate?
| A. 10 seconds
| B. The resolution of the chart
| C. The resolution of the dashboard
| D. Native resolution
|
D. Native resolution
Explanation:
According to the Splunk Observability Cloud documentation1, detectors operate at the native resolution of the metric or dimension that they monitor, with some exceptions for transformations or timeshifts. The native resolution is the frequency at which the data points are reported by the source. For example, if a metric is reported every 10 seconds, the detector will evaluate the metric every 10 seconds. The native resolution ensures that the detector uses the most granular and accurate data available for alerting.
Question # 5
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.
|
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
Question # 6
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
|
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.
Question # 7
What information is needed to create a detector?
| A. Alert Status, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
| B. Alert Signal, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
| C. Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients
| D. Alert Status, Alert Condition, Alert Settings, Alert Meaning, Alert Recipients
|
C. Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients
Explanation:
According to the Splunk Observability Cloud documentation1, to create a detector, you need the following information:
• Alert Signal: This is the metric or dimension that you want to monitor and alert on. You can select a signal from a chart or a dashboard, or enter a SignalFlow query to define the signal.
• Alert Condition: This is the criteria that determines when an alert is triggered or cleared. You can choose from various built-in alert conditions, such as static threshold, dynamic threshold, outlier, missing data, and so on. You can also specify the severity level and the trigger sensitivity for each alert condition.
• Alert Settings: This is the configuration that determines how the detector behaves and interacts with other detectors. You can set the detector name, description, resolution, run lag, max delay, and detector rules. You can also enable or disable the detector, and mute or unmute the alerts.
• Alert Message: This is the text that appears in the alert notification and event feed. You can customize the alert message with variables, such as signal name, value, condition, severity, and so on. You can also use markdown formatting to enhance the message appearance.
• Alert Recipients: This is the list of destinations where you want to send the alert notifications. You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on. You can also specify the notification frequency and suppression settings.
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