SPLK-4001 Exam Dumps - Splunk O11y Cloud Certified Metrics User Questions and Answers

The correct answer is D. Each data marker represents the sum of API calls in the hour leading up to the data marker.

The metric demo.trans.count is a cumulative counter metric, which means that it represents the total number of API calls since the start of the measurement. A cumulative counter metric can be used to measure the rate of change or the sum of events over a time period1

The chart below shows the metric demo.trans.count with a one-hour rollup and a line chart type. A rollup is a way to aggregate data points over a specified time interval, such as one hour, to reduce the number of data points displayed on a chart. A line chart type connects the data points with a line to show the trend of the metric over time2

Each data marker on the chart represents the sum of API calls in the hour leading up to the data marker. This is because the rollup function for cumulative counter metrics is sum by default, which means that it adds up all the data points in each time interval. For example, the data marker at 10:00 AM shows the sum of API calls from 9:00 AM to 10:00 AM3

To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations123.

1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups-in-charts 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions-for-metric-types

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.

The alert recipients tab specifies where notification messages should be sent when alerts are triggered or cleared. The options that can be used are:

Invoke a webhook URL. This option allows you to send a HTTP POST request to a custom URL that can perform various actions based on the alert information. For example, you can use a webhook to create a ticket in a service desk system, post a message to a chat channel, or trigger another workflow1

Send an SMS message. This option allows you to send a text message to one or more phone numbers when an alert is triggered or cleared. You can customize the message content and format using variables and templates2

Send to email addresses. This option allows you to send an email notification to one or more recipients when an alert is triggered or cleared. You can customize the email subject, body, and attachments using variables and templates. You can also include information from search results, the search job, and alert triggering in the email3

Therefore, the correct answer is A, C, and D.

1: https://docs.splunk.com/Documentation/Splunk/latest/Alert/Webhooks 2: https://docs.splunk.com/Documentation/Splunk/latest/Alert/SMSnotification 3: https://docs.splunk.com/Documentation/Splunk/latest/Alert/Emailnotification

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:

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”))

The correct answer is B. Adding logging into the metrics receiver pipeline.

The logging exporter is a component that allows the OpenTelemetry Collector to send traces, metrics, and logs directly to the console. It can be used to diagnose and troubleshoot issues with telemetry received and processed by the Collector, or to obtain samples for other purposes1

To activate the logging exporter, you need to add it to the pipeline that you want to diagnose. In this case, since you are experiencing issues with a new receiver for metrics, you need to add the logging exporter to the metrics receiver pipeline. This will create a new plot that shows the metrics received by the Collector and any errors or warnings that might occur1

The image that you have sent with your question shows how to add the logging exporter to the metrics receiver pipeline. You can see that the exporters section of the metrics pipeline includes logging as one of the options. This means that the metrics received by any of the receivers listed in the receivers section will be sent to the logging exporter as well as to any other exporters listed2

To learn more about how to use the logging exporter in Splunk Observability Cloud, you can refer to this documentation1.

1: https://docs.splunk.com/Observability/gdi/opentelemetry/components/logging-exporter.html 2: https://docs.splunk.com/Observability/gdi/opentelemetry/exposed-endpoints.html

The correct answer is A, C, and D. Organization metrics give insights into system usage, system limits, data ingested and token quotas. Organization metrics are included for free. A user can plot and alert on them like metrics they send to Splunk Observability Cloud.

Organization metrics are a set of metrics that Splunk Observability Cloud provides to help you measure your organization’s usage of the platform. They include metrics such as:

Ingest metrics: Measure the data you’re sending to Infrastructure Monitoring, such as the number of data points you’ve sent.

App usage metrics: Measure your use of application features, such as the number of dashboards in your organization.

Integration metrics: Measure your use of cloud services integrated with your organization, such as the number of calls to the AWS CloudWatch API.

Resource metrics: Measure your use of resources that you can specify limits for, such as the number of custom metric time series (MTS) you’ve created1

Organization metrics are not charged and do not count against any system limits. You can view them in built-in charts on the Organization Overview page or in custom charts using the Metric Finder. You can also create alerts based on organization metrics to monitor your usage and performance1

To learn more about how to use organization metrics in Splunk Observability Cloud, you can refer to this documentation1.

1: https://docs.splunk.com/observability/admin/org-metrics.html

According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud1. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA.

To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests2. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings3. Therefore, option A and D are correct.

The correct answer is D. The detector is automatically linked to the chart.

When you create a detector from a chart on a custom dashboard, the detector is automatically linked to the chart. This means that you can see the detector status and alerts on the chart, and you can access the detector settings from the chart menu. You can also unlink the detector from the chart if you want to1

Changes made to the chart do not affect the detector, and changes made to the detector do not affect the chart. The detector and the chart are independent entities that have their own settings and parameters. However, if you change the metric or dimension of the chart, you might lose the link to the detector1

The alerts generated by the detector will show up in the Alerts page, where you can view, manage, and acknowledge them. You can also see them on the team landing page if you assign the detector to a team2

To learn more about how to create and link detectors from charts on custom dashboards, you can refer to this documentation1.

1: https://docs.splunk.com/observability/alerts-detectors-notifications/link-detectors-to-charts.html 2: https://docs.splunk.com/observability/alerts-detectors-notifications/view-manage-alerts.html

The correct answer is D. Map, Nodes, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail.

The built-in Kubernetes Navigator is a feature of Splunk Observability Cloud that provides a comprehensive and intuitive way to monitor the performance and health of Kubernetes environments. It includes the following views:

Map: A graphical representation of the Kubernetes cluster topology, showing the relationships and dependencies among nodes, pods, containers, and services. You can use the map to quickly identify and troubleshoot issues in your cluster1

Nodes: A tabular view of all the nodes in your cluster, showing key metrics such as CPU utilization, memory usage, disk usage, and network traffic. You can use the nodes view to compare and analyze the performance of different nodes1

Workloads: A tabular view of all the workloads in your cluster, showing key metrics such as CPU utilization, memory usage, network traffic, and error rate. You can use the workloads view to compare and analyze the performance of different workloads, such as deployments, stateful sets, daemon sets, or jobs1

Node Detail: A detailed view of a specific node in your cluster, showing key metrics and charts for CPU utilization, memory usage, disk usage, network traffic, and pod count. You can also see the list of pods running on the node and their status. You can use the node detail view to drill down into the performance of a single node2

Workload Detail: A detailed view of a specific workload in your cluster, showing key metrics and charts for CPU utilization, memory usage, network traffic, error rate, and pod count. You can also see the list of pods belonging to the workload and their status. You can use the workload detail view to drill down into the performance of a single workload2

Pod Detail: A detailed view of a specific pod in your cluster, showing key metrics and charts for CPU utilization, memory usage, network traffic, error rate, and container count. You can also see the list of containers within the pod and their status. You can use the pod detail view to drill down into the performance of a single pod2

Container Detail: A detailed view of a specific container in your cluster, showing key metrics and charts for CPU utilization, memory usage, network traffic, error rate, and log events. You can use the container detail view to drill down into the performance of a single container2

To learn more about how to use Kubernetes Navigator in Splunk Observability Cloud, you can refer to this documentation3.

1: https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Kubernetes-Navigator 2: https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Detail-pages 3: https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html