Question # 1
| Which of the following are correct ports for the specified components in the OpenTelemetry
Collector? | | A. gRPC (4000), SignalFx (9943), Fluentd (6060)
| | B. gRPC (6831), SignalFx (4317), Fluentd (9080)
| | C. gRPC (4459), SignalFx (9166), Fluentd (8956)
| | D. gRPC (4317), SignalFx (9080), Fluentd (8006) |
D. gRPC (4317), SignalFx (9080), Fluentd (8006)
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.
Question # 2
| The alert recipients tab specifies where notification messages should be sent when alerts
are triggered or cleared. Which of the below options can be used? (select all that apply) | | A. Invoke a webhook URL.
| | B. Export to CSV.
| | C. Send an SMS message.
| | D. Send to email addresses. |
A. Invoke a webhook URL.
C. Send an SMS message.
D. Send to email addresses.
Explanation: The alert recipients tab specifies where notification messages should be sent
when alerts are triggered or cleared. The options that can be used are:
Invoke a webhook URL: This option allows you to send a HTTP POST request to a
custom URL that can perform various actions based on the alert information. For
example, you can use a webhook to create a ticket in a service desk system, post
a message to a chat channel, or trigger another workflow1.
Send an SMS message: This option allows you to send a text message to one or
more phone numbers when an alert is triggered or cleared. You can customize the
message content and format using variables and templates2.
Send to email addresses: This option allows you to send an email notification to
one or more recipients when an alert is triggered or cleared. You can customize
the email subject, body, and attachments using variables and templates. You can
also include information from search results, the search job, and alert triggering in
the email3.
Question # 3
| A customer has a very dynamic infrastructure. During every deployment, all existing
instances are destroyed, and new ones are created Given this deployment model, how
should a detector be created that will not send false notifications of instances being down? | | A. Create the detector. Select Alert settings, then select Auto-Clear Alerts and enter an
appropriate time period. | | B. Create the detector. Select Alert settings, then select Ephemeral Infrastructure and enter
the expected lifetime of an instance. | | C. Check the Dynamic checkbox when creating the detector. | | D. Check the Ephemeral checkbox when creating the detector. |
B. Create the detector. Select Alert settings, then select Ephemeral Infrastructure and enter
the expected lifetime of an instance.
Explanation:
According to the web search results, ephemeral infrastructure is a term that describes
instances that are auto-scaled up or down, or are brought up with new code versions and
discarded or recycled when the next code version is deployed1. Splunk Observability Cloud
has a feature that allows you to create detectors for ephemeral infrastructure without
sending false notifications of instances being down2. To use this feature, you need to do
the following steps:
Create the detector as usual, by selecting the metric or dimension that you want to
monitor and alert on, and choosing the alert condition and severity level.
Select Alert settings, then select Ephemeral Infrastructure. This will enable a
special mode for the detector that will automatically clear alerts for instances that
are expected to be terminated.
Enter the expected lifetime of an instance in minutes. This is the maximum amount
of time that an instance is expected to live before being replaced by a new one.
For example, if your instances are replaced every hour, you can enter 60 minutes
as the expected lifetime.
Save the detector and activate it.
With this feature, the detector will only trigger alerts when an instance stops reporting a
metric unexpectedly, based on its expected lifetime. If an instance stops reporting a metric
within its expected lifetime, the detector will assume that it was terminated on purpose and
will not trigger an alert. Therefore, option B is correct.
Question # 4
| A customer is sending data from a machine that is over-utilized. Because of a lack of
system resources, datapoints from this machine are often delayed by up to 10 minutes.
Which setting can be modified in a detector to prevent alerts from firing before the
datapoints arrive? | | A. Max Delay
| | B. Duration
| | C. Latency
| | D. Extrapolation Policy |
A. Max Delay
Explanation: The correct answer is A. Max Delay.
Max Delay is a parameter that specifies the maximum amount of time that the analytics
engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to
10 minutes, the detector will wait for only a maximum of 10 minutes even if some data
points have not arrived. By default, Max Delay is set to Auto, allowing the analytics engine
to determine the appropriate amount of time to wait for data points1.
In this case, since the customer knows that the data from the over-utilized machine can be
delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10
minutes. This will prevent the detector from firing alerts before the data points arrive, and
avoid false positives or missing data1.
Question # 5
| 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. Percentages and ratios | | B. Timeshift and Bottom N | | C. Timeshift and Top N | | D. Chart Options and metadata |
A. Percentages and ratios
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”))
Question # 6
| A customer deals with a holiday rush of traffic during November each year, but does not
want to be flooded with alerts when this happens. The increase in traffic is expected and
consistent each year. Which detector condition should be used when creating a detector for
this data? | | A. Outlier Detection
| | B. Static Threshold
| | C. Calendar Window
| | D. Historical Anomaly |
D. Historical Anomaly
Explanation: historical anomaly is a detector condition that allows you to trigger an alert
when a signal deviates from its historical pattern1. Historical anomaly uses machine
learning to learn the normal behavior of a signal based on its past data, and then compares
the current value of the signal with the expected value based on the learned pattern1. You
can use historical anomaly to detect unusual changes in a signal that are not explained by
seasonality, trends, or cycles1.
Historical anomaly is suitable for creating a detector for the customer’s data, because it can
account for the expected and consistent increase in traffic during November each
year. Historical anomaly can learn that the traffic pattern has a seasonal component that
peaks in November, and then adjust the expected value of the traffic accordingly1. This
way, historical anomaly can avoid triggering alerts when the traffic increases in November,
as this is not an anomaly, but rather a normal variation. However, historical anomaly can
still trigger alerts when the traffic deviates from the historical pattern in other ways, such as
if it drops significantly or spikes unexpectedly1.
Question # 7
| What is one reason a user of Splunk Observability Cloud would want to subscribe to an
alert? | | A. To determine the root cause of the Issue triggering the detector.
| | B. To perform transformations on the data used by the detector.
| | C. To receive an email notification when a detector is triggered.
| | D. To be able to modify the alert parameters. |
C. To receive an email notification when a detector is triggered.
Explanation: One reason a user of Splunk Observability Cloud would want to subscribe to
an alert is C. To receive an email notification when a detector is triggered.
A detector is a component of Splunk Observability Cloud that monitors metrics or events
and triggers alerts when certain conditions are met. A user can create and configure
detectors to suit their monitoring needs and goals1.
A subscription is a way for a user to receive notifications when a detector triggers an alert.
A user can subscribe to a detector by entering their email address in the Subscription tab of
the detector page. A user can also unsubscribe from a detector at any time2.
When a user subscribes to an alert, they will receive an email notification that contains
information about the alert, such as the detector name, the alert status, the alert severity,
the alert time, and the alert message. The email notification also includes links to view the
detector, acknowledge the alert, or unsubscribe from the detector2.
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