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| Section | Weight | Objectives |
|---|---|---|
| Get Metrics In with OpenTelemetry | 10% | - Troubleshooting common errors - General OpenTelemetry Concepts - Deploy the OTel Collector on Linux - Configure the OTel Collector - Edit the configuration |
| Create Efficient Dashboards and Alerts | 10% | - View dashboard happenings - Include instructions on interfaces - Configure personal information linkages - Create single-instance dashboard panels |
| Introduction to Metric Visualization | 15% | - Create charts and dashboards - Create widgets and showcase groups - Differentiate between several chart visualization types - Look for metrics - Visualize a measure in a chart - Analyze data in charts - Use rollups and analytical tools correctly |
| Metrics Concepts | 15% | - List the components of a datapoint - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - Data resolution, rollups - Discriminate between types of metadata |
| Monitor Using Built-in Content | 10% | - Interact with data using built-in content - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Subscribe to alerts - Use the Cluster Analyzer to pinpoint the root of some problems - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution |
| Intro to Alerting on Metrics using Detectors | 10% | - Make a solo detector - Make the detector from a chart - Clone an existing detector - Make a muting rule |
主要な環境では、人々はより多くの仕事のプレッシャーに直面しています。そのため、彼らはSplunk認証を一般の群れよりも高めたいと考えています。有効で効率的なSPLK-4001ガイドトレントを選択する方法は、ほとんどの候補者が懸念する可能性のある重要なトピックです。だから今、それは正しいです、あなたは私たちのところに来ます。当社は、特にSplunk認定試験に関するこの分野の高品質なSPLK-4001試験問題で有名です。試験のためにSPLK-4001学習教材を実践している数千人の受験者に受け入れられています。
質問 # 41
What is the key difference between creating a standalone detector and creating a detector from a chart?
正解：C
質問 # 42
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
正解：B
解説：
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.
質問 # 43
Changes to which type of metadata result in a new metric time series?
正解：D
解説：
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS).
Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host, you will create a new MTS for the same metric name.
Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest. They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed.
質問 # 44
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?
正解：B
解説：
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.
質問 # 45
Which analytic function can be used to discover peak page visits for a site over the last day?
正解：A
解説：
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.
質問 # 46
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