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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Get Metrics In with OpenTelemetry | 10% | - Configure the OTel Collector - Edit the configuration - General OpenTelemetry Concepts - Troubleshooting common errors - Deploy the OTel Collector on Linux |
| Topic 2: Metrics Concepts | 15% | - List the components of a datapoint - Data resolution, rollups - Discriminate between types of metadata - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints |
| Topic 3: Intro to Alerting on Metrics using Detectors | 10% | - Make the detector from a chart - Clone an existing detector - Make a solo detector - Make a muting rule |
| Topic 4: Introduction to Metric Visualization | 15% | - Look for metrics - Differentiate between several chart visualization types - Use rollups and analytical tools correctly - Create charts and dashboards - Analyze data in charts - Visualize a measure in a chart - Create widgets and showcase groups |
| Topic 5: Create Efficient Dashboards and Alerts | 10% | - Configure personal information linkages - Create single-instance dashboard panels - Include instructions on interfaces - View dashboard happenings |
| 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 Cluster Analyzer to pinpoint the root of some problems - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers |
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NEW QUESTION # 127
For a high-resolution metric, what is the highest possible native resolution of the metric?
Answer: B
Explanation:
Explanation
The correct answer is C. 1 second.
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.
In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1-second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.
Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.
NEW QUESTION # 128
A customer wants to share a collection of charts with their entire SRE organization. What feature of Splunk Observability Cloud makes this possible?
Answer: D
Explanation:
According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization1. You can create dashboard groups based on different criteria, such as service, team, role, or topic. You can also set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group. Dashboard groups make it possible to share a collection of charts with your entire SRE organization, or any other group of users that you want to collaborate with.
NEW QUESTION # 129
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?
Answer: 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"))
NEW QUESTION # 130
When installing OpenTelemetry Collector, which error message is indicative that there is a misconfigured realm or access token?
Answer: D
Explanation:
According to the web search results, a 401 (UNAUTHORIZED) error message is indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 401 (UNAUTHORIZED) error message means that the request was not authorized by the server due to invalid credentials. A realm is a parameter that specifies the scope of protection for a resource, such as a Splunk Observability Cloud endpoint. An access token is a credential that grants access to a resource, such as a Splunk Observability Cloud API. If the realm or the access token is misconfigured, the request to install OpenTelemetry Collector will be rejected by the server with a 401 (UNAUTHORIZED) error message.
NEW QUESTION # 131
Which of the following best describes a Metric Time Series (MTS) in Splunk Observability Cloud?
Answer: C
Explanation:
An MTS is defined as the unique combination of a metric name plus a specific set of dimension key-value pairs. Each unique combination creates a distinct time series within the metric.
NEW QUESTION # 132
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