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Valid SPLK-4001 FREE EXAM DUMPS QUESTIONS & ANSWERS
NEW QUESTION # 33
For which types of charts can individual plot visualization be set?
- A. Line, Bar, Column
- B. Line, Area, Column
- C. Histogram, Line, Column
- D. Bar, Area, Column
Answer: B
Explanation:
Explanation
The correct answer is C. Line, Area, Column.
For line, area, and column charts, you can set the individual plot visualization to change the appearance of each plot in the chart. For example, you can change the color, shape, size, or style of the lines, areas, or columns. You can also change the rollup function, data resolution, or y-axis scale for each plot1 To set the individual plot visualization for line, area, and column charts, you need to select the chart from the Metric Finder, then click on Plot Chart Options and choose Individual Plot Visualization from the list of options. You can then customize each plot according to your preferences2 To learn more about how to use individual plot visualization in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Individual-plot-visualization 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Set-individual-plot-visualization
NEW QUESTION # 34
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?
- A. The detector is disabled.
- B. The detector has an incorrect alert rule.
- C. The detector has an incorrect signal,
- D. The detector has a muting rule.
Answer: D
Explanation:
Explanation
The most likely root cause of the issue is D. The detector has a muting rule.
A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal1 When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 35
Which of the following is optional, but highly recommended to include in a datapoint?
- A. Timestamp
- B. Metric name
- C. Value
- D. Metric type
Answer: D
Explanation:
Explanation
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
NEW QUESTION # 36
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?
- A. The detector is disabled.
- B. The detector has an incorrect alert rule.
- C. The detector has an incorrect signal,
- D. The detector has a muting rule.
Answer: D
Explanation:
Explanation
The most likely root cause of the issue is D. The detector has a muting rule.
A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal1 When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 37
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 10 second delta between counter values.
- B. Each data marker represents the average hourly rate of API calls.
- 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 sum of API calls in the hour leading up to the data marker.
Answer: D
Explanation:
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
NEW QUESTION # 38
A customer operates a caching web proxy. They want to calculate the cache hit rate for their service. What is the best way to achieve this?
- A. Chart Options and metadata
- B. Timeshift and Bottom N
- C. Timeshift and Top N
- D. Percentages and ratios
Answer: D
Explanation:
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, percentages and ratios are useful for calculating the proportion of one metric to another, such as cache hits to cache misses, or successful requests to failed requests. You can use the percentage() or ratio() functions in SignalFlow to compute these values and display them in charts. For example, to calculate the cache hit rate for a service, you can use the following SignalFlow code:
percentage(counters("cache.hits"), counters("cache.misses"))
This will return the percentage of cache hits out of the total number of cache attempts. You can also use the ratio() function to get the same result, but as a decimal value instead of a percentage.
ratio(counters("cache.hits"), counters("cache.misses"))
NEW QUESTION # 39
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. Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
- C. Local data links can only have a Splunk Observability Cloud internal destination.
- D. Only Splunk Observability Cloud administrators can create local links.
Answer: A,B
Explanation:
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.
NEW QUESTION # 40
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, Severe, and Critical.
- B. Info, Warning, Minor, Major, and Critical.
- C. Info, Warning, Minor, Major, and Emergency.
- D. Debug, Warning, Minor, Major, and Critical.
Answer: B
Explanation:
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
NEW QUESTION # 41
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?
- A. /etc/otel/collector/
- B. /etc/system/default/
- C. /etc/opentelemetry/
- D. /opt/splunk/
Answer: A
Explanation:
Explanation
The correct answer is B. /etc/otel/collector/
According to the web search results, the Splunk distribution of the OpenTelemetry Collector stores the configuration files on Linux machines in the /etc/otel/collector/ directory by default. You can verify this by looking at the first result1, which explains how to install the Collector for Linux manually. It also provides the locations of the default configuration file, the agent configuration file, and the gateway configuration file.
To learn more about how to install and configure the Splunk distribution of the OpenTelemetry Collector, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/install-linux-manual.html 2:
https://docs.splunk.com/Observability/gdi/opentelemetry.html
NEW QUESTION # 42
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. Machine dashboard
- B. Single-instance dashboard
- C. Multiple-service dashboard
- D. Server dashboard
Answer: B
Explanation:
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.
NEW QUESTION # 43
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 receiver pipeline:
- B. Adding debug into the metrics exporter pipeline:
- C. Adding logging into the metrics receiver pipeline:
- D. Adding logging into the metrics exporter pipeline:
Answer: C
Explanation:
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
NEW QUESTION # 44
Which of the following are ways to reduce flapping of a detector? (select all that apply)
- A. Establish a reset threshold for the detector.
- B. Enable the anti-flap setting in the detector options menu.
- C. Configure a duration or percent of duration for the alert.
- D. Apply a smoothing transformation (like a rolling mean) to the input data for the detector.
Answer: C,D
Explanation:
Explanation
According to the Splunk Lantern article Resolving flapping detectors in Splunk Infrastructure Monitoring, flapping is a phenomenon where alerts fire and clear repeatedly in a short period of time, due to the signal fluctuating around the threshold value. To reduce flapping, the article suggests the following ways:
Configure a duration or percent of duration for the alert: This means that you require the signal to stay above or below the threshold for a certain amount of time or percentage of time before triggering an alert. This can help filter out noise and focus on more persistent issues.
Apply a smoothing transformation (like a rolling mean) to the input data for the detector: This means that you replace the original signal with the average of its last several values, where you can specify the window length. This can reduce the impact of a single extreme observation and make the signal less fluctuating.
NEW QUESTION # 45
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
- A. Jitter
- B. Latency
- C. Lag
- D. Delay
Answer: C
Explanation:
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.
NEW QUESTION # 46
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. Custom events that have been sent in from an external source.
- B. Random alerts from active detectors.
- C. Events created when a detector triggers an alert.
- D. Events created when a detector clears an alert.
Answer: A,C,D
Explanation:
Explanation
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.
NEW QUESTION # 47
A Software Engineer is troubleshooting an issue with memory utilization in their application. They released a new canary version to production and now want to determine if the average memory usage is lower for requests with the 'canary' version dimension. They've already opened the graph of memory utilization for their service.
How does the engineer see if the new release lowered average memory utilization?
- A. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version1.
- B. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' from the Group By field.
- C. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-l'.
- D. On the chart for plot A, select Add Analytics, then select MeanrTransformation. In the window that appears, select 'version' from the Group By field.
Answer: B
Explanation:
Explanation
The correct answer is C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' from the Group By field.
This will create a new plot B that shows the average memory utilization for each version of the application.
The engineer can then compare the values of plot B for the 'canary' and 'stable' versions to see if there is a significant difference.
To learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html
NEW QUESTION # 48
Which of the following can be configured when subscribing to a built-in detector?
- A. Links to a chart.
- B. Alerts on team landing page.
- C. Alerts on a dashboard.
- D. Outbound notifications.
Answer: D
Explanation:
Explanation
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.
NEW QUESTION # 49
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. When creating the plot, add a discriminator.
- B. Add a filter to narrow the scope of the measurement.
- C. Select the Sharded option when creating the plot.
- D. Add a restricted scope adjustment to the plot.
Answer: B
Explanation:
Explanation
The correct answer is B. 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 it1 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 Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap 3:
https://docs.splunk.com/Observability/gdi/metrics/search.html
NEW QUESTION # 50
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