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Free SPLK-4001 Exam Braindumps (page: 2)

Page 2 of 8
PDF with 54 Questions

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

  1. View data at highest resolution.
  2. Have a consistent value.
  3. View detector in a chart.
  4. Have a consistent type of measurement.

Answer(s): 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 issues.
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 aggregation. 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 behavior.
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
4: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors



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?

  1. The new signals will be reflected in the original detector.
  2. The new signals will be reflected in the original chart.
  3. You can only monitor one of the new signals.
  4. The new signals will not be added to the original detector.

Answer(s): D

Explanation:

According to the Splunk O11y Cloud Certified Metrics User Track document., 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.



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

  1. average, latest, lag, min, max, sum, rate
  2. std_dev, mean, median, mode, min, max
  3. sigma, epsilon, pi, omega, beta, tau
  4. 1min, 5min, 10min, 15min, 30min

Answer(s): A

Explanation:

According to the Splunk O11y Cloud Certified Metrics User Track document., 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.



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?

  1. On the chart for plot A, select Add Analytics, then select MeanrTransformation. In the window that appears, select 'version' from the Group By field.
  2. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-l'.
  3. 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.
  4. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version.

Answer(s): C

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 documentation.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html



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?

  1. Single-instance dashboard
  2. Machine dashboard
  3. Multiple-service dashboard
  4. Server dashboard

Answer(s): A

Explanation:

According to the Splunk O11y Cloud Certified Metrics User Track document., 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.



To refine a search for a metric a customer types host: test-*.
What does this filter return?

  1. Only metrics with a dimension of host and a value beginning with test-.
  2. Error
  3. Every metric except those with a dimension of host and a value equal to test.
  4. Only metrics with a value of test- beginning with host.

Answer(s): A

Explanation:

The correct answer is A. Only metrics with a dimension of host and a value beginning with test-. This filter returns the metrics that have a host dimension that matches the pattern test-. For example, test-01, test-abc, test-xyz, etc. The asterisk () is a wildcard character that can match any string of characters.
To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics
2: https://docs.splunk.com/Observability/gdi/metrics/search.html



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?

  1. Percentages and ratios
  2. Timeshift and Bottom N
  3. Timeshift and Top N
  4. Chart Options and metadata

Answer(s): A

Explanation:

According to the Splunk O11y Cloud Certified Metrics User Track document., 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"))



Which of the following are correct ports for the specified components in the OpenTelemetry Collector?

  1. gRPC (4000), SignalFx (9943), Fluentd (6060)
  2. gRPC (6831), SignalFx (4317), Fluentd (9080)
  3. gRPC (4459), SignalFx (9166), Fluentd (8956)
  4. gRPC (4317), SignalFx (9080), Fluentd (8006)

Answer(s): D

Explanation:

The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006). According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result. You can also see the agent and gateway configuration files in the same result for more details.
1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html






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