What are the best practices for creating detectors? (select all that apply)
ABCD
Explanation:
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 issues1
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 aggregation2
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 behavior3
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.
1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 3
:
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
An SRE came across an existing detector that is a good starting point for a detector they want to
create. They clone the detector, update the metric, and add multiple new signals. As a result of the
cloned detector, which of the following is true?
D
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1
, cloning a detector
creates a copy of the detector that you can modify without affecting the original detector. You can
change the metric, filter, and signal settings of the cloned detector. However, the new signals that
you add to the cloned detector will not be reflected in the original detector, nor in the original chart
that the detector was based on. Therefore, option D is correct.
Option A is incorrect because the new signals will not be reflected in the original detector. Option B is
incorrect because the new signals will not be reflected in the original chart. Option C is incorrect
because you can monitor all of the new signals that you add to the cloned detector.
Which of the following are supported rollup functions in Splunk Observability Cloud?
A
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1
, Observability Cloud has
the following rollup functions: Sum: (default for counter metrics): Returns the sum of all data points
in the MTS reporting interval. Average (default for gauge metrics): Returns the average value of all
data points in the MTS reporting interval. Min: Returns the minimum data point value seen in the
MTS reporting interval. Max: Returns the maximum data point value seen in the MTS reporting
interval. Latest: Returns the most recent data point value seen in the MTS reporting interval. Lag:
Returns the difference between the most recent and the previous data point values seen in the MTS
reporting interval. Rate: Returns the rate of change of data points in the MTS reporting interval.
Therefore, option A is correct.
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?
C
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
.
: https://docs.splunk.com/Observability/gdi/metrics/analytics.html
One server in a customer's data center is regularly restarting due to power supply issues. What type
of dashboard could be used to view charts and create detectors for this server?
A
Explanation:
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.
To refine a search for a metric a customer types host: test-*. What does this filter return?
A
Explanation:
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
A customer operates a caching web proxy. They want to calculate the cache hit rate for their service.
What is the best way to achieve this?
A
Explanation:
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”))
Which of the following are correct ports for the specified components in the OpenTelemetry
Collector?
D
Explanation:
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.
: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html
When writing a detector with a large number of MTS, such as memory. free in a deployment with
30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot. Which of
the choices below would most likely reduce the number of MTS below the plot cap?
B
Explanation:
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 it1
Adding 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 Support2
To 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
An SRE creates a new detector to receive an alert when server latency is higher than 260
milliseconds. Latency below 260 milliseconds is healthy for their service. The SRE creates a New
Detector with a Custom Metrics Alert Rule for latency and sets a Static Threshold alert condition at
260ms.
How can the number of alerts be reduced?
B
Explanation:
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.
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on
Linux machines by default?
B
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
Which of the following rollups will display the time delta between a datapoint being sent and a
datapoint being received?
C
Explanation:
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.
Which of the following is optional, but highly recommended to include in a datapoint?
D
Explanation:
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
correctly1
To 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
Which analytic function can be used to discover peak page visits for a site over the last day?
A
Explanation:
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.
A customer is experiencing issues getting metrics from a new receiver they have configured in the
OpenTelemetry Collector. How would the customer go about troubleshooting further with the
logging exporter?
B
Explanation:
The correct answer is B. Adding logging into the metrics receiver pipeline.
The logging exporter is a component that allows the OpenTelemetry Collector to send traces,
metrics, and logs directly to the console.
It can be used to diagnose and troubleshoot issues with
telemetry received and processed by the Collector, or to obtain samples for other purposes1
To activate the logging exporter, you need to add it to the pipeline that you want to diagnose. In this
case, since you are experiencing issues with a new receiver for metrics, you need to add the logging
exporter to the metrics receiver pipeline.
This will create a new plot that shows the metrics received
by the Collector and any errors or warnings that might occur1
The image that you have sent with your question shows how to add the logging exporter to the
metrics receiver pipeline. You can see that the exporters section of the metrics pipeline includes
logging as one of the options.
This means that the metrics received by any of the receivers listed in
the receivers section will be sent to the logging exporter as well as to any other exporters listed2
To learn more about how to use the logging exporter in Splunk Observability Cloud, you can refer to
this documentation1
.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/components/logging-exporter.html 2
:
https://docs.splunk.com/Observability/gdi/opentelemetry/exposed-endpoints.html