SPLK-4001 Splunk O11y Cloud Certified Metrics User Exam
Exam Details:
Level: Foundational
Prerequisites: None
Length: 60 minutes
Format: 54 multiple choice questions
Pricing: $
Delivery: Exam is given by our testing partner,
OVERVIEW
Enhance your Splunk Observability Cloud monitoring
Go beyond logs and use real-time monitoring at scale for every layer of the development environment. Work with OpenTelemetry, find insights using analytics, visualize metrics, alert with detectors, and create efficient dashboards.
Preparation:
Review exam requirements and recommendations on the Splunk O11y Cloud Certified Metrics User track flowchart.
View recommended courses in the Splunk Certification Exams Study Guide.
Discover what to expect on the exam via the test blueprint.
Get step-by-step registration assistance with the Exam Registration Tutorial.
Who should take this exam?
This exam establishes a baseline for users of Splunk Observability Cloud. Take your monitoring to new heights as an observability professional. With this certification, you will be able to demonstrate the concepts and features critical to getting the most out of Splunk Observability Cloud.
Career builders
Set your sights on a new goal
Additional Splunk training and certifications increase the value you can deliver. Expand your options with other learning opportunities.
Take your career to the next level by earning a certification that will help you climb the ranks as a Splunk certified professional.
Developers and architects
Optimize your applications and infrastructure using Splunk Observability Cloud’s toolsets.
Observability professionals
Take your DevOps/SRE career further and level up as a Splunk O11y Cloud Certified Metrics User.
Exam Content
The following topics are general guidelines for the content likely to be included on the exam; however, other related topics may also appear on any specific delivery of the exam. In order to better reflect the contents of the exam and for clarity purposes, the guidelines below may change at any time without notice.
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1.0 Get Metrics In with OpenTelemetry 10%
1.1 Deploy the OTel Collector on Linux
1.2 Configure the OTel Collector
1.3 Edit the configuration
1.4 Troubleshooting common errors
1.5 General OpenTelemetry Concepts
2.0 Metrics Concepts 15%
2.1 Data resolution, rollups
2.2 List the components of a datapoint
2.3 Define components of the Splunk IM Data Model, Metrics, MTS, datapoints
2.4 Discriminate between types of metadata
3.0 Monitor Using Built-in Content 10%
3.1 Interact with data using built-in content
3.2 Correctly interpret data in charts based on rollups, analytic functions, and chart resolution
3.3 Subscribe to alerts
3.4 Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers
3.5 Use the Cluster Analyzer to pinpoint the root of some problems
3.6 Use built-in Kubernetes Dashboards to investigate and troubleshoot
4.0 Introduction to Visualizing Metrics 15%
4.1 Create charts, dashboards
4.2 Search for metrics
4.3 Visualize a metric in a chart
4.4 Create dashboards and dashboard groups
4.5 Distinguish between different chart visualization types
4.6 Correctly apply rollups and analytic functions
4.7 Interpret data in charts
5.0 Introduction to Alerting on Metrics with Detectors 10%
5.1 Create a detector from a chart
5.2 Clone a detector
5.3 Create a standalone detector
5.4 Create a muting rule
6.0 Create Efficient Dashboards and Alerts 10%
6.1 Add instructions to dashboards
6.2 Create single-instance dashboards
6.3 View events on dashboards
6.4 Configure local data links
6.5 Customize alert messages
6.6 Troubleshoot charts and alerts (Impact of late datapoints; extrapolation policy, etc.)
7.0 Finding Insights Using Analytics 15%
7.1 Finding total value across all sources
7.2 Combining plots in charts
7.3 View and alert on weekly, daily, or hourly comparisons
7.4 Use percentages and ratios to understand trends
7.5 Apply analytic functions over moving and calendar time windows
7.6 Apply analytics functions to a subset of MTS in a signal
8.0 Detectors for Common Use Cases 15%
8.1 Identify common issues with detectors
8.2 Troubleshoot a detector
8.3 Create detectors to monitor populations
8.4 Create non-flapping detectors
8.5 Monitor metrics with cyclic patterns
8.6 Monitor a large number of sources
8.7 Monitor an ephemeral infrastructure
Exam Preparation
Candidates may reference the Splunk How-To YouTube Channel, Splunk Docs, and draw from their own Splunk experience.
The following is a suggested and non-exhaustive list of training from the O11y Cloud Certified Metrics User Learning Path that may cover topics listed in the above blueprint:
* Getting Data into Splunk Observability Cloud
* Introduction to Splunk Observability
* Introduction to Splunk Infrastructure Monitoring
* Splunk Observability Cloud Teams
* Splunk Observability Cloud Enterprise Features
* Fundamentals of Metrics Monitoring in Splunk Observability
* Kubernetes Monitoring with Splunk Observability Cloud
* Visualizing and Alerting in Splunk IM
There are no prerequisite exams for this certification.
QUESTION 1
What are the best practices for creating detectors? (select all that apply)
A. View data at highest resolution.
B. Have a consistent value.
C. View detector in a chart.
D. Have a consistent type of measurement.
Answer: 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.
QUESTION 2
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?
A. The new signals will be reflected in the original detector.
B. The new signals will be reflected in the original chart.
C. You can only monitor one of the new signals.
D. The new signals will not be added to the original detector.
Answer: 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.
QUESTION 3
Which of the following are supported rollup functions in Splunk Observability Cloud?
A. average, latest, lag, min, max, sum, rate
B. std_dev, mean, median, mode, min, max
C. sigma, epsilon, pi, omega, beta, tau
D. 1min, 5min, 10min, 15min, 30min
Answer: 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.
QUESTION 4
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
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.