2026年最新の実際に出るSPLK-4001問題集テストエンジン試験問題はここにある [Q38-Q61]

2026年最新の実際に出るSPLK-4001問題集テストエンジン試験問題はここにある

更新された公式資料はSPLK-4001認証済みのSPLK-4001問題集PDF

質問 # 38
When metrics in a detector have different dimensions, a correlation conflict can occur, which prevents an alert from firing. Which of the choices below is the best method to resolve a correlation conflict?

• A. Shift-left the two plots.
• B. Apply an aggregate analytic function.
• C. Apply a transformation analytic function.
• D. Overlay the two plots.

正解：B

解説：
Applying an aggregate analytic function resolves correlation conflicts by summarizing metrics with different dimensions into a common value, allowing the detector to evaluate the alert condition across all relevant metrics consistently.

質問 # 39
Changes to which type of metadata result in a new metric time series?

• A. Dimensions
• B. Sources
• C. Tags
• D. Properties

正解：A

解説：
Explanation
The correct answer is A. Dimensions.
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)1 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 host2, you will create a new MTS for the same metric name1 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 changed2 To learn more about how to use metadata in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/metrics-and-metadata/metrics.html#Dimensions 2:
https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html

質問 # 40
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. Export to CSV.
• C. Send to email addresses.
• D. Invoke a webhook URL.

正解：A、C、D

解説：
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 # 41
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. 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.
• C. Check the Dynamic checkbox when creating the detector.
• D. Create the detector. Select Alert settings, then select Auto-Clear Alerts and enter an appropriate time period.

正解：B

解説：
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.

質問 # 42
What happens when the limit of allowed dimensions is exceeded for an MTS?

• A. The datapoint is averaged.
• B. The datapoint is updated.
• C. The additional dimensions are dropped.
• D. The datapoint is dropped.

正解：C

解説：
Explanation
According to the web search results, dimensions are metadata in the form of key-value pairs that monitoring software sends in along with the metrics. The set of metric time series (MTS) dimensions sent during ingest is used, along with the metric name, to uniquely identify an MTS1. Splunk Observability Cloud has a limit of 36 unique dimensions per MTS2. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud2. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.

質問 # 43
A customer has a single-instance dashboard that multiple teams within their organization use to track different environments. What is the best way to make it more tailored for all teams without adding filters into the signal or adding more signals?

• A. Create a new metric for each team.
• B. Create more signals.
• C. Create dashboard variables.
• D. Create new dashboards.

正解：C

解説：
Dashboard variables allow the same dashboard to be dynamically filtered for different teams or environments without modifying the underlying signals, providing a tailored view for each team efficiently.

質問 # 44
Which analytic function can be used to discover peak page visits for a site over the last day?

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

正解：B

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 # 45
Which of the following are required in the configuration of a data point? (select all that apply)

• A. Timestamp
• B. Metric Name
• C. Value
• D. Metric Type

正解：A、B、C

解説：
Explanation
The required components in the configuration of a data point are:
Metric Name: A metric name is a string that identifies the type of measurement that the data point represents, such as cpu.utilization, memory.usage, or response.time. A metric name is mandatory for every data point, and it must be unique within a Splunk Observability Cloud organization1 Timestamp: A timestamp is a numerical value that indicates the time at which the data point was collected or generated. A timestamp is mandatory for every data point, and it must be in epoch time format, which is the number of seconds since January 1, 1970 UTC1 Value: A value is a numerical value that indicates the magnitude or quantity of the measurement that the data point represents. A value is mandatory for every data point, and it must be compatible with the metric type of the data point1 Therefore, the correct answer is A, C, and D.
To learn more about how to configure data points in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Data-points

質問 # 46
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard?
(select all that apply)

• A. Heatmap
• B. Line
• C. Single Value
• D. List

正解：C、D

解説：
Explanation
The chart visualization types that are unaffected by changing the time picker on a dashboard are:
Single Value: A single value chart shows the current value of a metric or an expression. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart1 List: A list chart shows the values of a metric or an expression for each dimension value in a table format. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart2 Therefore, the correct answer is A and D.
To learn more about how to use different chart visualization types in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Single-value 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#List 3:
https://docs.splunk.com/Observability/gdi/metrics/charts.html

質問 # 47
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?

• A. Adding debug into the metrics exporter pipeline:
  
• B. Adding debug into the metrics receiver pipeline:
  
• C. Adding logging into the metrics receiver pipeline:
  
• D. Adding logging into the metrics exporter pipeline:
  

正解：C

解説：
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

質問 # 48
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. Extrapolation Policy
• D. Latency

正解：A

解説：
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 To learn more about how to use Max Delay in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector-options.html#Max-Delay

質問 # 49
Which of the following best describes the purpose of the "Metric Finder" in Splunk Observability Cloud?

• A. To configure user roles and permissions
• B. To create new detectors automatically
• C. To search for and discover available metrics and their associated dimensions
• D. To delete unused dashboards

正解：C

解説：
The Metric Finder allows users to search, browse, and explore available metrics along with their dimensions, helping users discover what data is available before building charts, dashboards, or detectors.

質問 # 50
Which of the following statements are true about the datatable on a chart? (Choose all that apply.)

• A. By default all dimensions on the output signal are displayed.
• B. By default all metadata on the output signal are displayed.
• C. Properties cannot be displayed.
• D. A user can choose which of the output dimensions are displayed.

正解：A、D

解説：
By default, all dimensions of the output signal are displayed in a chart's datatable, and users can select which of these dimensions to show or hide to customize the view.

質問 # 51
With exceptions for transformations or timeshifts, at what resolution do detectors operate?

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

正解：C

解説：
Explanation
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 # 52
What information is needed to create a detector?

• A. Alert Signal, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
• B. Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients
• C. Alert Status, Alert Condition, Alert Settings, Alert Meaning, Alert Recipients
• D. Alert Status, Alert Criteria, Alert Settings, Alert Message, Alert Recipients

正解：B

解説：
Explanation
According to the Splunk Observability Cloud documentation1, to create a detector, you need the following information:
Alert Signal: This is the metric or dimension that you want to monitor and alert on. You can select a signal from a chart or a dashboard, or enter a SignalFlow query to define the signal.
Alert Condition: This is the criteria that determines when an alert is triggered or cleared. You can choose from various built-in alert conditions, such as static threshold, dynamic threshold, outlier, missing data, and so on. You can also specify the severity level and the trigger sensitivity for each alert condition.
Alert Settings: This is the configuration that determines how the detector behaves and interacts with other detectors. You can set the detector name, description, resolution, run lag, max delay, and detector rules. You can also enable or disable the detector, and mute or unmute the alerts.
Alert Message: This is the text that appears in the alert notification and event feed. You can customize the alert message with variables, such as signal name, value, condition, severity, and so on. You can also use markdown formatting to enhance the message appearance.
Alert Recipients: This is the list of destinations where you want to send the alert notifications. You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on. You can also specify the notification frequency and suppression settings.

質問 # 53
To smooth a very spiky cpu.utilization metric, what is the correct analytic function to better see if the cpu. utilization for servers is trending up over time?

• A. Mean (by host)
• B. Rate/Sec
• C. Median
• D. Mean (Transformation)

正解：D

解説：
According to the web search results, a mean transformation is an analytic function that returns the average value of a metric or a dimension over a specified time interval. A mean transformation can be used to smooth a very spiky metric, such as cpu.utilization, by reducing the impact of outliers and noise. A mean transformation can also help to see if the metric is trending up or down over time, by showing the general direction of the average value. For example, to smooth the cpu.utilization metric and see if it is trending up over time, you can use the following SignalFlow code:
mean(1h, counters("cpu.utilization"))
This will return the average value of the cpu.utilization counter metric for each metric time series (MTS) over the last hour. You can then use a chart to visualize the results and compare the mean values across different MTS.

質問 # 54
When installing OpenTelemetry Collector, which error message is indicative that there is a misconfigured realm or access token?

• A. 401 (UNAUTHORIZED)
• B. 403 (NOT ALLOWED)
• C. 404 (NOT FOUND)
• D. 503 (SERVICE UNREACHABLE)

正解：A

解説：
Explanation
The correct answer is C. 401 (UNAUTHORIZED).
According to the web search results, a 401 (UNAUTHORIZED) error message is indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector1. A 401 (UNAUTHORIZED) error message means that the request was not authorized by the server due to invalid credentials. A realm is a parameter that specifies the scope of protection for a resource, such as a Splunk Observability Cloud endpoint.
An access token is a credential that grants access to a resource, such as a Splunk Observability Cloud API. If the realm or the access token is misconfigured, the request to install OpenTelemetry Collector will be rejected by the server with a 401 (UNAUTHORIZED) error message.
Option A is incorrect because a 403 (NOT ALLOWED) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 403 (NOT ALLOWED) error message means that the request was authorized by the server but not allowed due to insufficient permissions. Option B is incorrect because a 404 (NOT FOUND) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 404 (NOT FOUND) error message means that the request was not found by the server due to an invalid URL or resource. Option D is incorrect because a 503 (SERVICE UNREACHABLE) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 503 (SERVICE UNREACHABLE) error message means that the server was unable to handle the request due to temporary overload or maintenance.

質問 # 55
A team is troubleshooting high cardinality issues with a custom metric. What is a recommended best practice to reduce cardinality?

• A. Add more dimensions to every datapoint
• B. Avoid using high-cardinality values (such as unique IDs) as dimensions
• C. Convert the metric into a log
• D. Increase the metric resolution

正解：B

解説：
Using high-cardinality values, such as unique request IDs or timestamps, as dimensions can cause an explosion in the number of unique time series. Avoiding this practice helps keep cardinality manageable and reduces cost and performance issues.

質問 # 56
What is the purpose of muting rules in Splunk Observability Cloud?

• A. To temporarily suppress alert notifications for a detector, such as during planned maintenance
• B. To permanently delete a detector
• C. To archive old dashboards
• D. To change the resolution of a metric

正解：A

解説：
Muting rules allow users to temporarily suppress notifications from a detector, which is useful during planned maintenance windows or known issues, without disabling or deleting the detector itself.

質問 # 57
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-l) and add a scale: 100 function to express the rate of change as a percentage.
• B. Create a plot C using the formula (A-B) and add a scale:percent function to express the rate of change as a percentage.
• C. Create a temporary plot by clicking the Change% button in the upper-right corner of the plot showing lines A and B.
• D. Create a temporary plot by dragging items A and B into the Analytics Explorer window.

正解：A

解説：
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 # 58
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.

質問 # 59
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?

• A. Select the Sharded option when creating the plot.
• B. When creating the plot, add a discriminator.
• C. Add a filter to narrow the scope of the measurement.
• D. Add a restricted scope adjustment to the plot.

正解：C

解説：
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 it. 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 Support.

質問 # 60
What kind of metadata is sent with a datapoint

• A. Dimensions
• B. Sources
• C. Tags
• D. Properties

正解：A

解説：
Dimensions are the metadata sent with a datapoint. They describe the context of the metric, such as the source, host, environment, or other identifying attributes used to filter and group metric data.

質問 # 61
......

Splunk SPLK-4001試験は、Splunk O11y Cloud Certified Metricsを扱う個人の知識とスキルをテストするために設計されています。この認定は、Splunk Cloud環境でメトリックスデータを監視、分析、視覚化する責任を持つ専門家を対象としています。この試験は、候補者がSplunk Cloudを使用してメトリックスデータを収集、保存、分析し、ダッシュボードを作成し、メトリックスデータに関連する問題をトラブルシューティングする能力を評価するために設計されています。

 

最新版無料体験を掴み取れ！Splunk SPLK-4001問題集PDFは更新された：https://www.passtest.jp/Splunk/SPLK-4001-shiken.html

最新リリースのSPLK-4001問題集はSplunk O11y Cloud Certified認証済み：https://drive.google.com/open?id=1HKeljv1jyQSEoewxfTWmJTomgvmuPkKF