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Splunk SPLK-4001 exam is designed to test the knowledge and skills of professionals who work with Splunk's Observability Cloud platform. SPLK-4001 exam is designed to validate the expertise of individuals who are responsible for monitoring and analyzing metrics and data in the cloud. SPLK-4001 Exam is ideal for professionals who are looking to demonstrate their proficiency in using Splunk's Observability Cloud to collect, monitor, and analyze data.
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The Splunk SPLK-4001 Exam covers a wide range of topics, including understanding how to create and manipulate metrics in Splunk, configuring and deploying metrics in a cloud infrastructure, using metrics for performance tuning, and integrating metrics with other monitoring tools. Candidates are also tested on their ability to analyze and interpret data, troubleshoot issues in the cloud, and deliver insights to stakeholders. Splunk O11y Cloud Certified Metrics User Sample Questions (Q31-Q36):NEW QUESTION # 31
In the Splunk distribution of the OpenTelemetry Collector, what is the difference between the agent_config.yaml and the splunk-otel-collector.conf files?
A. splunk-otel-collector.conf configures processors and agent_config.yaml sets the memory limits for the collector.
B. splunk-otel-collector.conf defines the OpenTelemetry pipeline, and agent_config.yaml sets endpoint URLs and access tokens.
C. agent_config.yaml defines the OpenTelemetry pipeline, and splunk-otel-collector.conf sets endpoint URLs and access tokens.
D. agent_config.yaml configures the gateway's address and splunk-otel-collector.conf sets the memory limits for the collector.
Answer: B
NEW QUESTION # 32
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)
Answer: B
Explanation:
According to the Splunk Observability Cloud documentation1, the maximum function is an analytic function that returns the highest value of a metric or a dimension over a specified time interval. The maximum function can be used as a transformation or an aggregation. A transformation applies the function to each metric time series (MTS) individually, while an aggregation applies the function to all MTS and returns a single value. For example, to discover the peak page visits for a site over the last day, you can use the following SignalFlow code:
maximum(24h, counters("page.visits"))
This will return the highest value of the page.visits counter metric for each MTS over the last 24 hours. You can then use a chart to visualize the results and identify the peak page visits for each MTS.
NEW QUESTION # 33
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
A. Latency
B. Lag
C. Jitter
D. Delay
Answer: B
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 # 34
Which of the following can be configured when subscribing to a built-in detector?
A. Alerts on a dashboard.
B. Alerts on team landing page.
C. Links to a chart.
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 # 35
What are the best practices for creating detectors? (select all that apply)
A. Have a consistent value.
B. View detector in a chart.
C. View data at highest resolution.
D. Have a consistent type of measurement.
Answer: A,B,C,D
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/Observab ... tices-for-detectors
2: https://docs.splunk.com/Observab ... tices-for-detectors
3: https://docs.splunk.com/Observab ... detector-in-a-chart
4: https://docs.splunk.com/Observab ... tices-for-detectors
P.S. Free & New SPLK-4001 dumps are available on Google Drive shared by DumpsTests: https://drive.google.com/open?id=1DDI4BU0ncK5E2PYdHNTUZv9ShvfNhEMZ Author: kenreed832 Time: 6 hour before
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