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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metrics Concepts | 15% | - Discriminate between types of metadata - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - List the components of a datapoint - Data resolution, rollups |
| Topic 2: Create Efficient Dashboards and Alerts | 10% | - View dashboard happenings - Create single-instance dashboard panels - Configure personal information linkages - Include instructions on interfaces |
| Topic 3: Introduction to Metric Visualization | 15% | - Create widgets and showcase groups - Use rollups and analytical tools correctly - Look for metrics - Visualize a measure in a chart - Differentiate between several chart visualization types - Analyze data in charts - Create charts and dashboards |
| Topic 4: Intro to Alerting on Metrics using Detectors | 10% | - Make the detector from a chart - Make a muting rule - Clone an existing detector - Make a solo detector |
| Topic 5: Get Metrics In with OpenTelemetry | 10% | - Configure the OTel Collector - Deploy the OTel Collector on Linux - Edit the configuration - Troubleshooting common errors - General OpenTelemetry Concepts |
| Topic 6: Monitor Using Built-in Content | 10% | - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Subscribe to alerts - Interact with data using built-in content - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems |
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NEW QUESTION # 69
Which mode is needed when the OpenTelemetry Collector is deployed on the same host as the application being monitored?
Answer: C
Explanation:
When the OpenTelemetry Collector runs on the same host as the application, it operates in agent mode. In this mode, it directly receives telemetry data from the application and forwards it to a centralized backend or gateway for further processing.
NEW QUESTION # 70
A team is troubleshooting high cardinality issues with a custom metric. What is a recommended best practice to reduce cardinality?
Answer: C
Explanation:
Using high-cardinality values, such as unique request IDs or timestamps, as dimensions can cause an explosion in the number of unique time series. Avoiding this practice helps keep cardinality manageable and reduces cost and performance issues.
NEW QUESTION # 71
A customer has put a lot of time into building a detector with multiple signals, analytics, and filters.
That customer wants to create an identical detector, but change the metric. What is the process to do so?
Answer: C
Explanation:
Cloning a detector creates an identical copy of the existing detector, preserving its signals, analytics, filters, and configuration so the customer can modify only the metric as needed.
NEW QUESTION # 72
What is the key difference between creating a standalone detector and creating a detector from a chart?
Answer: B
NEW QUESTION # 73
What constitutes a single metrics time series (MTS)?
Answer: D
Explanation:
Explanation
The correct answer is B. A set of data points that all have the same metric name and list of dimensions.
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 timestamp1
NEW QUESTION # 74
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