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| Section | Weight | Objectives |
|---|---|---|
| Alerting with Detectors | 15% | - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors |
| Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings |
| Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments |
| Visualizing Metrics | 20% | - Creating and configuring charts, graphs, and time-series - Visualization best practices and effective communication - Using SignalFlow for custom queries |
| Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
>> Exam Splunk SPLK-4001 Materials <<
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NEW QUESTION # 127
In monitoring systems, which feature allows you to troubleshoot and identify issues with charts and alerts effectively?
Answer: C
NEW QUESTION # 128
A user wants to create an alert that only fires if CPU usage is above 90% AND memory usage is above 85% at the same time. What should they configure?
Answer: C
Explanation:
Splunk Observability Cloud allows users to build detectors using signal expressions that combine multiple conditions (such as AND logic across two metrics), enabling alerts that trigger only when all specified conditions are met simultaneously.
NEW QUESTION # 129
What constitutes a single metrics time series (MTS)?
Answer: C
Explanation:
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension "hostname": "host1"
MTS2: Gauge metric cpu.utilization, dimension "hostname": "host2"
MTS3: Gauge metric memory.usage, dimension "hostname": "host1"
A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp.
NEW QUESTION # 130
A customer has a very dynamic infrastructure. During every deployment, all existing instances are destroyed, and new ones are created Given this deployment model, how should a detector be created that will not send false notifications of instances being down?
Answer: B
Explanation:
Explanation
According to the web search results, ephemeral infrastructure is a term that describes instances that are auto-scaled up or down, or are brought up with new code versions and discarded or recycled when the next code version is deployed1. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down2. To use this feature, you need to do the following steps:
Create the detector as usual, by selecting the metric or dimension that you want to monitor and alert on, and choosing the alert condition and severity level.
Select Alert settings, then select Ephemeral Infrastructure. This will enable a special mode for the detector that will automatically clear alerts for instances that are expected to be terminated.
Enter the expected lifetime of an instance in minutes. This is the maximum amount of time that an instance is expected to live before being replaced by a new one. For example, if your instances are replaced every hour, you can enter 60 minutes as the expected lifetime.
Save the detector and activate it.
With this feature, the detector will only trigger alerts when an instance stops reporting a metric unexpectedly, based on its expected lifetime. If an instance stops reporting a metric within its expected lifetime, the detector will assume that it was terminated on purpose and will not trigger an alert. Therefore, option B is correct.
NEW QUESTION # 131
What happens when the limit of allowed dimensions is exceeded for an MTS?
Answer: D
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.
NEW QUESTION # 132
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