[2026年02月07日]SPLK-4001認定ガイド問題と解答トレーニング [Q31-Q52]

[2026年02月07日]SPLK-4001認定ガイド問題と解答トレーニング

SPLK-4001認定お試しセット最新SPLK-4001のPDF問題集

認証試験では、データの摂取と変換、メトリック分析、警告と視覚化、トラブルシューティングなど、クラウドの監視と分析に関連するさまざまなトピックをカバーしています。候補者は、Splunkを使用してこれらのタスクを効果的に実行する習熟度を示す必要があります。試験に合格すると、個人はSplunk O11Y Cloud認定メトリックユーザー認定を受け取ります。これは、Splunkを使用したクラウドの監視と分析の専門知識のマークとして世界中の組織によって認識されます。

 

質問 # 31
Which of the following statements is true of detectors created from a chart on a custom dashboard?

• A. Changes made to the detector affect the chart.
• B. The detector is automatically linked to the chart.
• C. The alerts will show up in the team landing page.
• D. Changes made to the chart affect the detector.

正解：B

解説：
The correct answer is D. The detector is automatically linked to the chart.
When you create a detector from a chart on a custom dashboard, the detector is automatically linked to the chart. This means that you can see the detector status and alerts on the chart, and you can access the detector settings from the chart menu. You can also unlink the detector from the chart if you want to1 Changes made to the chart do not affect the detector, and changes made to the detector do not affect the chart. The detector and the chart are independent entities that have their own settings and parameters. However, if you change the metric or dimension of the chart, you might lose the link to the detector1 The alerts generated by the detector will show up in the Alerts page, where you can view, manage, and acknowledge them. You can also see them on the team landing page if you assign the detector to a team2 To learn more about how to create and link detectors from charts on custom dashboards, you can refer to this documentation1.
1: https://docs.splunk.com/observability/alerts-detectors-notifications/link-detectors-to-charts.html 2: https://docs.splunk.com/observability/alerts-detectors-notifications/view-manage-alerts.html

質問 # 32
Which of the following are true about organization metrics? (select all that apply)

• A. Organization metrics give insights into system usage, system limits, data ingested and token quotas.
• B. A user can plot and alert on them like metrics they send to Splunk Observability Cloud.
• C. Organization metrics are included for free.
• D. Organization metrics count towards custom MTS limits.

正解：A、B、C

解説：
The correct answer is A, C, and D. Organization metrics give insights into system usage, system limits, data ingested and token quotas. Organization metrics are included for free. A user can plot and alert on them like metrics they send to Splunk Observability Cloud.
Organization metrics are a set of metrics that Splunk Observability Cloud provides to help you measure your organization's usage of the platform. They include metrics such as:
Ingest metrics: Measure the data you're sending to Infrastructure Monitoring, such as the number of data points you've sent.
App usage metrics: Measure your use of application features, such as the number of dashboards in your organization.
Integration metrics: Measure your use of cloud services integrated with your organization, such as the number of calls to the AWS CloudWatch API.
Resource metrics: Measure your use of resources that you can specify limits for, such as the number of custom metric time series (MTS) you've created1 Organization metrics are not charged and do not count against any system limits. You can view them in built-in charts on the Organization Overview page or in custom charts using the Metric Finder. You can also create alerts based on organization metrics to monitor your usage and performance1 To learn more about how to use organization metrics in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/observability/admin/org-metrics.html

質問 # 33
What are the best practices for creating detectors? (select all that apply)

• A. Have a consistent type of measurement.
• B. View detector in a chart.
• C. Have a consistent value.
• D. View data at highest resolution.

正解：A、B、C、D

解説：
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

質問 # 34
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. The new signals will not be added to the original detector.
• D. You can only monitor one of the new signals.

正解：C

解説：
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.

質問 # 35
With exceptions for transformations or timeshifts, at what resolution do detectors operate?

• A. 10 seconds
• B. Native resolution
• C. The resolution of the chart
• D. The resolution of the dashboard

正解：B

解説：
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.

質問 # 36
Which of the following is optional, but highly recommended to include in a datapoint?

• A. Metric name
• B. Metric type
• C. Timestamp
• D. Value

正解：B

解説：
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

質問 # 37
An SRE creates an event feed chart in a dashboard that shows a list of events that meet criteria they specify. Which of the following should they include? (select all that apply)

• A. Events created when a detector triggers an alert.
• B. Random alerts from active detectors.
• C. Custom events that have been sent in from an external source.
• D. Events created when a detector clears an alert.

正解：A、C、D

解説：
According to the web search results1, an event feed chart is a type of chart that shows a list of events that meet criteria you specify. An event feed chart can display one or more event types depending on how you specify the criteria. The event types that you can include in an event feed chart are:
Custom events that have been sent in from an external source: These are events that you have created or received from a third-party service or tool, such as AWS CloudWatch, GitHub, Jenkins, or PagerDuty. You can send custom events to Splunk Observability Cloud using the API or the Event Ingest Service.
Events created when a detector triggers or clears an alert: These are events that are automatically generated by Splunk Observability Cloud when a detector evaluates a metric or dimension and finds that it meets the alert condition or returns to normal. You can create detectors to monitor and alert on various metrics and dimensions using the UI or the API.
Therefore, option A, B, and D are correct.

質問 # 38
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. Send an SMS message.
• B. Send to email addresses.
• C. Export to CSV.
• D. Invoke a webhook URL.

正解：A、B、D

解説：
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 Therefore, the correct answer is A, C, and D.
1: https://docs.splunk.com/Documentation/Splunk/latest/Alert/Webhooks 2:
https://docs.splunk.com/Documentation/Splunk/latest/Alert/SMSnotification 3:
https://docs.splunk.com/Documentation/Splunk/latest/Alert/Emailnotification

質問 # 39
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. Historical Anomaly
• C. Calendar Window
• D. Static Threshold

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 # 40
The Sum Aggregation option for analytic functions does which of the following?

• A. Calculates the number of MTS present in the plot.
• B. Calculates 1/2 of the values present in the input time series.
• C. Calculates the sum of values per time series across a period of time.
• D. Calculates the sum of values present in the input time series across the entire environment or per group.

正解：D

解説：
Explanation
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is analytic functions. Analytic functions are mathematical operations that can be applied to metrics to transform, aggregate, or analyze them.
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 Analytic Functions, which explains that analytic functions can be used to perform calculations on metrics, such as sum, average, min, max, count, etc. The document also provides examples of how to use analytic functions in charts and dashboards.
One of the analytic functions that can be used is Sum Aggregation, which calculates the sum of values present in the input time series across the entire environment or per group. The document gives an example of how to use Sum Aggregation to calculate the total CPU usage across all hosts in a group by using the following syntax:
sum(cpu.utilization) by hostgroup

質問 # 41
Which component of the OpenTelemetry Collector allows for the modification of metadata?

• A. Pipelines
• B. Exporters
• C. Receivers
• D. Processors

正解：D

解説：
The component of the OpenTelemetry Collector that allows for the modification of metadata is A. Processors.
Processors are components that can modify the telemetry data before sending it to exporters or other components. Processors can perform various transformations on metrics, traces, and logs, such as filtering, adding, deleting, or updating attributes, labels, or resources. Processors can also enrich the telemetry data with additional metadata from various sources, such as Kubernetes, environment variables, or system information1 For example, one of the processors that can modify metadata is the attributes processor. This processor can update, insert, delete, or replace existing attributes on metrics or traces. Attributes are key-value pairs that provide additional information about the telemetry data, such as the service name, the host name, or the span kind2 Another example is the resource processor. This processor can modify resource attributes on metrics or traces. Resource attributes are key-value pairs that describe the entity that produced the telemetry data, such as the cloud provider, the region, or the instance type3 To learn more about how to use processors in the OpenTelemetry Collector, you can refer to this documentation1.
1: https://opentelemetry.io/docs/collector/configuration/#processors 2: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor 3: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor

質問 # 42
Which analytic function can be used to discover peak page visits for a site over the last day?

• A. Count: (Id)
• B. Lag: (24h)
• C. Maximum: Transformation (24h)
• D. Maximum: Aggregation (Id)

正解：C

解説：
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 # 43
Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?

• A. Maximum / Minimum
• B. Best/Worst
• C. Top / Bottom
• D. Exclude / Include

正解：C

解説：
Explanation
The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period1 To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation1.

質問 # 44
A customer wants to share a collection of charts with their entire SRE organization. What feature of Splunk Observability Cloud makes this possible?

• A. Chart exporter
• B. Dashboard groups
• C. Shared charts
• D. Public dashboards

正解：B

解説：
Explanation
According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization1. You can create dashboard groups based on different criteria, such as service, team, role, or topic. You can also set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group. Dashboard groups make it possible to share a collection of charts with your entire SRE organization, or any other group of users that you want to collaborate with.

質問 # 45
Clicking a metric name from the results in metric finder displays the metric in Chart Builder. What action needs to be taken in order to save the chart created in the UI?

• A. Save the chart to a dashboard.
• B. Make sure that data is coming in for the metric then save the chart.
• C. Create a new dashboard and save the chart.
• D. Save the chart to multiple dashboards.

正解：A

解説：
According to the web search results, clicking a metric name from the results in metric finder displays the metric in Chart Builder1. Chart Builder is a tool that allows you to create and customize charts using metrics, dimensions, and analytics functions2. To save the chart created in the UI, you need to do the following steps:
Click the Save button on the top right corner of the Chart Builder. This will open a dialog box where you can enter the chart name and description, and choose the dashboard where you want to save the chart.
Enter a name and a description for your chart. The name should be descriptive and unique, and the description should explain the purpose and meaning of the chart.
Choose an existing dashboard from the drop-down menu, or create a new dashboard by clicking the + icon. A dashboard is a collection of charts that display metrics and events for your services or hosts3. You can organize and share dashboards with other users in your organization using dashboard groups3.
Click Save. This will save your chart to the selected dashboard and redirect you to the dashboard view. You can also access your saved chart from the Dashboards menu on the left navigation bar.

質問 # 46
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?

• A. Adjust the threshold.
• B. Choose another signal.
• C. Adjust the Trigger sensitivity. Duration set to 1 minute.
• D. Adjust the notification sensitivity. Duration set to 1 minute.

正解：C

解説：
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.

質問 # 47
Which of the following statements about adding properties to MTS are true? (select all that apply)

• A. Properties can be set via the API.
• B. Properties can be set in the UI under Metric Metadata.
• C. Properties are applied to dimension key:value pairs and propagated to all MTS with that dimension
• D. Properties are sent in with datapoints.

正解：A、B

解説：
Explanation
According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud1. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA.
To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests2. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings3.
Therefore, option A and D are correct.

質問 # 48
A DevOps engineer wants to determine if the latency their application experiences is growing fester after a new software release a week ago. They have already created two plot lines, A and B, that represent the current latency and the latency a week ago, respectively. How can the engineer use these two plot lines to determine the rate of change in latency?

• A. Create a plot C using the formula (A-B) and add a scale:percent function to express the rate of change as a percentage.
• B. Create a temporary plot by clicking the Change% button in the upper-right corner of the plot showing lines A and B.
• C. Create a temporary plot by dragging items A and B into the Analytics Explorer window.
• D. Create a plot C using the formula (A/B-l) and add a scale: 100 function to express the rate of change as a percentage.

正解：D

解説：
Explanation
The correct answer is C. Create a plot C using the formula (A/B-l) and add a scale: 100 function to express the rate of change as a percentage.
To calculate the rate of change in latency, you need to compare the current latency (plot A) with the latency a week ago (plot B). One way to do this is to use the formula (A/B-l), which gives you the ratio of the current latency to the previous latency minus one. This ratio represents how much the current latency has increased or decreased relative to the previous latency. For example, if the current latency is 200 ms and the previous latency is 100 ms, then the ratio is (200/100-l) = 1, which means the current latency is 100% higher than the previous latency1 To express the rate of change as a percentage, you need to multiply the ratio by 100. You can do this by adding a scale: 100 function to the formula. This function scales the values of the plot by a factor of 100. For example, if the ratio is 1, then the scaled value is 100%2 To create a plot C using the formula (A/B-l) and add a scale: 100 function, you need to follow these steps:
Select plot A and plot B from the Metric Finder.
Click on Add Analytics and choose Formula from the list of functions.
In the Formula window, enter (A/B-l) as the formula and click Apply.
Click on Add Analytics again and choose Scale from the list of functions.
In the Scale window, enter 100 as the factor and click Apply.
You should see a new plot C that shows the rate of change in latency as a percentage.
To learn more about how to use formulas and scale functions in Splunk Observability Cloud, you can refer to these documentations34.
1: https://www.mathsisfun.com/numbers/percentage-change.html 2:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale 3:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Formula 4:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Scale

質問 # 49
Which of the following can be configured when subscribing to a built-in detector?

• A. Alerts on team landing page.
• B. Outbound notifications.
• C. Links to a chart.
• D. Alerts on a dashboard.

正解：B

解説：
According to the web search results1, subscribing to a built-in detector is a way to receive alerts and notifications from Splunk Observability Cloud when certain criteria are met. A built-in detector is a detector that is automatically created and configured by Splunk Observability Cloud based on the data from your integrations, such as AWS, Kubernetes, or OpenTelemetry1. To subscribe to a built-in detector, you need to do the following steps:
Find the built-in detector that you want to subscribe to. You can use the metric finder or the dashboard groups to locate the built-in detectors that are relevant to your data sources1.
Hover over the built-in detector and click the Subscribe button. This will open a dialog box where you can configure your subscription settings1.
Choose an outbound notification channel from the drop-down menu. This is where you can specify how you want to receive the alert notifications from the built-in detector. You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on2. You can also create a new notification channel by clicking the + icon2.
Enter the notification details for the selected channel. This may include your email address, Slack channel name, PagerDuty service key, webhook URL, and so on2. You can also customize the notification message with variables and markdown formatting2.
Click Save. This will subscribe you to the built-in detector and send you alert notifications through the chosen channel when the detector triggers or clears an alert.
Therefore, option C is correct.

質問 # 50
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?

• A. Jitter
• B. Delay
• C. Lag
• D. Latency

正解：C

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 # 51
Given that the metric demo. trans. count is being sent at a 10 second native resolution, which of the following is an accurate description of the data markers displayed in the chart below?

• A. Each data marker represents the sum of API calls in the hour leading up to the data marker.
• B. Each data marker represents the 10 second delta between counter values.
• C. Each data marker represents the average of the sum of datapoints over the last minute, averaged over the hour.
• D. Each data marker represents the average hourly rate of API calls.

正解：A

解説：
The correct answer is D. Each data marker represents the sum of API calls in the hour leading up to the data marker.
The metric demo.trans.count is a cumulative counter metric, which means that it represents the total number of API calls since the start of the measurement. A cumulative counter metric can be used to measure the rate of change or the sum of events over a time period1 The chart below shows the metric demo.trans.count with a one-hour rollup and a line chart type. A rollup is a way to aggregate data points over a specified time interval, such as one hour, to reduce the number of data points displayed on a chart. A line chart type connects the data points with a line to show the trend of the metric over time2 Each data marker on the chart represents the sum of API calls in the hour leading up to the data marker. This is because the rollup function for cumulative counter metrics is sum by default, which means that it adds up all the data points in each time interval. For example, the data marker at 10:00 AM shows the sum of API calls from 9:00 AM to 10:00 AM3 To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations123.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups-in-charts 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions-for-metric-types

質問 # 52
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