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SPLK 4001 Sample Practice Tests With Explanation
Master SPLK 4001 with Confidence: Ace Your Exam with Comprehensive Sample Practice Tests and Detailed Explanations!
| QUESTION: 1 |
| A customer deals with a holiday rush of traffic during November each year, but does not want to be flooded with alerts when this happens. The increase in traffic is expected and consistent each year. Which detector condition should be used when creating a detector for this data? A. Outlier Detection B. Static Threshold C. Calendar Window D. Historical Anomaly |
| Correct Answer: D |
| Explanation/Reference: Historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern1. Historical anomaly uses machine learning to learn the normal behavior of a signal based on its past data, and then compares the current value of the signal with the expected value based on the learned pattern1. You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles1 Historical anomaly is suitable for creating a detector for the customer data, because it can account for the expected and consistent increase in traffic during November each year. Historical anomaly can learn that the traffic pattern has a seasonal component that peaks in November, and then adjust the expected value of the traffic accordingly1. This way, historical anomaly can avoid triggering alerts when the traffic increases in November, as this is not an anomaly, but rather a normal variation. However, historical anomaly can still trigger alerts when the traffic deviates from the historical pattern in other ways, such as if it drops significantly or spikes unexpectedly |
| QUESTION: 2 |
| With exceptions for transformations or timeshifts, at what resolution do detectors operate? A. 10 seconds B. The resolution of the chart C. The resolution of the dashboard D. Native resolution |
| Correct Answer: D |
| Explanation/Reference: 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 |
| QUESTION: 3 |
| 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. Percentages and ratios B. Timeshift and Bottom N C. Timeshift and Top N D. Chart Options and metadata |
| Correct Answer: A |
| Explanation/Reference: 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: 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. |