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試験合格保証付きのSplunk O11y Cloud Certified SPLK-4001試験問題集 [Q14-Q32]

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試験合格保証付きのSplunk O11y Cloud Certified SPLK-4001試験問題集

Splunk SPLK-4001日常練習試験は2023年最新のに更新された56問あります


Splunk SPLK-4001試験は、Splunkに関する広範な準備と経験が必要な難しいテストです。候補者は、メトリック監視に強固な基盤を持ち、Splunkの高度な機能と能力に精通している必要があります。また、一般的な問題をトラブルシューティングし、パフォーマンスを最適化して、組織のITインフラストラクチャがスムーズかつ効率的に動作していることを確認できる必要があります。


SPLK-4001試験は、データ収集、メトリック作成、メトリッククエリング、アラート、および可視化など、さまざまなトピックをカバーしています。候補者は、SplunkのObservability Cloudなどのさまざまなツールや技術を使用してデータを監視および分析する能力もテストされます。試験は50問の選択式問題から構成され、制限時間は90分です。


Splunk SPLK-4001（Splunk O11Y Cloud Certified Metricsユーザー）試験は、Splunk O11y Cloudで働く専門家のスキルと知識をテストするために設計されています。 Splunk O11y Cloudは、組織がITインフラストラクチャとアプリケーションをリアルタイムで監視、分析、トラブルシューティングできるようにするクラウドベースのプラットフォームです。この試験は、Splunk O11y Cloudを使用した経験があり、プラットフォームのメトリックと監視機能に関する専門知識を実証したい個人向けです。

 

質問 # 14
Which of the following are true about organization metrics? (select all that apply)

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

正解：A、C、D

解説：
Explanation
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 # 15
For a high-resolution metric, what is the highest possible native resolution of the metric?

  • A. 15 seconds
  • B. 5 seconds
  • C. 1 second
  • D. 2 seconds

正解：C

解説：
Explanation
The correct answer is C. 1 second.
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.
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 Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1-second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.
Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.


質問 # 16
What happens when the limit of allowed dimensions is exceeded for an MTS?

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

正解：D

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 # 17
Which of the following statements about adding properties to MTS are true? (select all that apply)

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

正解：B、C

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 # 18
When creating a standalone detector, individual rules in it are labeled according to severity. Which of the choices below represents the possible severity levels that can be selected?

  • A. Info, Warning, Minor, Major, and Critical.
  • B. Info, Warning, Minor, Severe, and Critical.
  • C. Debug, Warning, Minor, Major, and Critical.
  • D. Info, Warning, Minor, Major, and Emergency.

正解：A

解説：
Explanation
The correct answer is C. Info, Warning, Minor, Major, and Critical.
When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2 To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.
1:
https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector
2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels


質問 # 19
Which of the following statements are true about local data links? (select all that apply)

  • A. Local data links are available on only one dashboard.
  • B. Only Splunk Observability Cloud administrators can create local links.
  • C. Local data links can only have a Splunk Observability Cloud internal destination.
  • D. Anyone with write permission for a dashboard can add local data links that appear on that dashboard.

正解：A、D

解説：
Explanation
The correct answers are A and D.
According to the Get started with Splunk Observability Cloud document1, one of the topics that is covered in the Getting Data into Splunk Observability Cloud course is global and local data links. Data links are shortcuts that provide convenient access to related resources, such as Splunk Observability Cloud dashboards, Splunk Cloud Platform and Splunk Enterprise, custom URLs, and Kibana logs.
The document explains that there are two types of data links: global and local. Global data links are available on all dashboards and charts, while local data links are available on only one dashboard. The document also provides the following information about local data links:
Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
Local data links can have either a Splunk Observability Cloud internal destination or an external destination, such as a custom URL or a Kibana log.
Only Splunk Observability Cloud administrators can delete local data links.
Therefore, based on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:
B is false because local data links can have an external destination as well as an internal one.
C is false because anyone with write permission for a dashboard can create local data links, not just administrators.


質問 # 20
Which of the following are supported rollup functions in Splunk Observability Cloud?

  • A. std_dev, mean, median, mode, min, max
  • B. average, latest, lag, min, max, sum, rate
  • C. 1min, 5min, 10min, 15min, 30min
  • D. sigma, epsilon, pi, omega, beta, tau

正解：B

解説：
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.


質問 # 21
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. Export to CSV.
  • B. Send an SMS message.
  • C. Send to email addresses.
  • D. Invoke a webhook URL.

正解：B、C、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 # 22
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. Median
  • B. Mean (Transformation)
  • C. Mean (by host)
  • D. Rate/Sec

正解：B

解説：
Explanation
The correct answer is D. Mean (Transformation).
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 interval1. 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.
Option A is incorrect because rate/sec is not an analytic function, but rather a rollup function that returns the rate of change of data points in the MTS reporting interval1. Rate/sec can be used to convert cumulative counter metrics into counter metrics, but it does not smooth or trend a metric. Option B is incorrect because median is not an analytic function, but rather an aggregation function that returns the middle value of a metric or a dimension over the entire time range1. Median can be used to find the typical value of a metric, but it does not smooth or trend a metric. Option C is incorrect because mean (by host) is not an analytic function, but rather an aggregation function that returns the average value of a metric or a dimension across all MTS with the same host dimension1. Mean (by host) can be used to compare the performance of different hosts, but it does not smooth or trend a metric.
Mean (Transformation) is an analytic function that allows you to smooth a very spiky metric by applying a moving average over a specified time window. This can help you see the general trend of the metric over time, without being distracted by the short-term fluctuations1 To use Mean (Transformation) on a cpu.utilization metric, you need to select the metric from the Metric Finder, then click on Add Analytics and choose Mean (Transformation) from the list of functions. You can then specify the time window for the moving average, such as 5 minutes, 15 minutes, or 1 hour. You can also group the metric by host or any other dimension to compare the smoothed values across different servers2 To learn more about how to use Mean (Transformation) and other analytic functions in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Mean-Transformation 2:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html


質問 # 23
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 average of the sum of datapoints over the last minute, averaged over the hour.
  • B. Each data marker represents the 10 second delta between counter values.
  • C. Each data marker represents the sum of API calls in the hour leading up to the data marker.
  • D. Each data marker represents the average hourly rate of API calls.

正解：C

解説：
Explanation
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 # 24
A customer has a large population of servers. They want to identify the servers where utilization has increased the most since last week. Which analytics function is needed to achieve this?

  • A. Standard deviation
  • B. Sum transformation
  • C. Rate
  • D. Tlmeshift

正解：D

解説：
Explanation
The correct answer is C. Timeshift.
According to the Splunk Observability Cloud documentation1, timeshift is an analytic function that allows you to compare the current value of a metric with its value at a previous time interval, such as an hour ago or a week ago. You can use the timeshift function to measure the change in a metric over time and identify trends, anomalies, or patterns. For example, to identify the servers where utilization has increased the most since last week, you can use the following SignalFlow code:
timeshift(1w, counters("server.utilization"))
This will return the value of the server.utilization counter metric for each server one week ago. You can then subtract this value from the current value of the same metric to get the difference in utilization. You can also use a chart to visualize the results and sort them by the highest difference in utilization.


質問 # 25
One server in a customer's data center is regularly restarting due to power supply issues. What type of dashboard could be used to view charts and create detectors for this server?

  • A. Server dashboard
  • B. Single-instance dashboard
  • C. Machine dashboard
  • D. Multiple-service dashboard

正解：B

解説：
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, a single-instance dashboard is a type of dashboard that displays charts and information for a single instance of a service or host. You can use a single-instance dashboard to monitor the performance and health of a specific server, such as the one that is restarting due to power supply issues. You can also create detectors for the metrics that are relevant to the server, such as CPU usage, memory usage, disk usage, and uptime. Therefore, option A is correct.


質問 # 26
When installing OpenTelemetry Collector, which error message is indicative that there is a misconfigured realm or access token?

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

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 # 27
What is the limit on the number of properties that an MTS can have?

  • A. No limit
  • B. 0
  • C. 1
  • D. 2

正解：C

解説：
Explanation
The correct answer is A. 64.
According to the web search results, the limit on the number of properties that an MTS can have is 64. A property is a key-value pair that you can assign to a dimension of an existing MTS to add more context to the metrics. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host is used for QA1 Properties are different from dimensions, which are key-value pairs that are sent along with the metrics at the time of ingest. Dimensions, along with the metric name, uniquely identify an MTS. The limit on the number of dimensions per MTS is 362 To learn more about how to use properties and dimensions in Splunk Observability Cloud, you can refer to this documentation2.
1:
https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html#Custom-properties
2: https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html


質問 # 28
What Pod conditions does the Analyzer panel in Kubernetes Navigator monitor? (select all that apply)

  • A. Pending
  • B. Not Scheduled
  • C. Unknown
  • D. Failed

正解：A、B、C、D

解説：
Explanation
The Pod conditions that the Analyzer panel in Kubernetes Navigator monitors are:
Not Scheduled: This condition indicates that the Pod has not been assigned to a Node yet. This could be due to insufficient resources, node affinity, or other scheduling constraints1 Unknown: This condition indicates that the Pod status could not be obtained or is not known by the system. This could be due to communication errors, node failures, or other unexpected situations1 Failed: This condition indicates that the Pod has terminated in a failure state. This could be due to errors in the application code, container configuration, or external factors1 Pending: This condition indicates that the Pod has been accepted by the system, but one or more of its containers has not been created or started yet. This could be due to image pulling, volume mounting, or network issues1 Therefore, the correct answer is A, B, C, and D.
To learn more about how to use the Analyzer panel in Kubernetes Navigator, you can refer to this documentation2.
1: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase 2:
https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Analyzer-panel


質問 # 29
Which of the following are accurate reasons to clone a detector? (select all that apply)

  • A. To explore how a detector was created without risk of changing it.
  • B. To reduce the amount of billed TAPM for the detector.
  • C. To modify the rules without affecting the existing detector.
  • D. To add an additional recipient to the detector's alerts.

正解：A、C

解説：
Explanation
The correct answers are A and D.
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the alerting concepts that is covered in the exam is detectors and alerts. Detectors are the objects that define the conditions for generating alerts, and alerts are the notifications that are sent when those conditions are met.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Alerting with Detectors, which covers how to create, modify, and manage detectors and alerts.
In the Alerting with Detectors course, there is a section on Cloning Detectors, which explains that cloning a detector creates a copy of the detector with all its settings, rules, and alert recipients. The document also provides some reasons why you might want to clone a detector, such as:
To modify the rules without affecting the existing detector. This can be useful if you want to test different thresholds or conditions before applying them to the original detector.
To explore how a detector was created without risk of changing it. This can be helpful if you want to learn from an existing detector or use it as a template for creating a new one.
Therefore, based on these documents, we can conclude that A and D are accurate reasons to clone a detector.
B and C are not valid reasons because:
Cloning a detector does not reduce the amount of billed TAPM for the detector. TAPM stands for Tracked Active Problem Metric, which is a metric that has been alerted on by a detector. Cloning a detector does not change the number of TAPM that are generated by the original detector or the clone.
Cloning a detector does not add an additional recipient to the detector's alerts. Cloning a detector copies the alert recipients from the original detector, but it does not add any new ones. To add an additional recipient to a detector's alerts, you need to edit the alert settings of the detector.


質問 # 30
How is it possible to create a dashboard group that no one else can edit?

  • A. Restrict the write access on the dashboard group.
  • B. Ask the admin to lock the dashboard group.
  • C. Hide the edit menu on the dashboard group.
  • D. Link the dashboard group to the team.

正解：A

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 set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group1. To create a dashboard group that no one else can edit, you need to do the following steps:
Create a dashboard group as usual, by selecting Dashboard Group from the Create menu on the navigation bar, entering a name and description, and adding dashboards to the group1.
Select Alert settings from the Dashboard actions menu () on the top right corner of the dashboard group. This will open a dialog box where you can configure the permissions for the dashboard group1.
Under Write access, select Only me. This will restrict the write access to the dashboard group to yourself only. No one else will be able to edit or delete the dashboards in the group1.
Click Save. This will create a dashboard group that no one else can edit.


質問 # 31
Which component of the OpenTelemetry Collector allows for the modification of metadata?

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

正解：A

解説：
Explanation
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 # 32
......

テストエンジン練習SPLK-4001テスト問題：https://www.passtest.jp/Splunk/SPLK-4001-shiken.html

有効問題を試そう！SPLK-4001実際の試験問題解答：https://drive.google.com/open?id=1yoREl1h67SHAfpsltrjSk5jgz0EVzS_C

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