SPLK-4001 Practice Questions

Properties can be supplied through the public REST API or directly from the UI (Metric Metadata). A property is saved on a specific dimension key=value pair; every MTS that carries that same dimension inherits the property automatically. Properties are metadata and are therefore not carried inside datapoint ingestion calls.

A. Correct – Supported through /property API endpoints.

B. Properties are sent in with datapoints – Datapoint payloads accept dimensions and values only, not properties; metadata calls are separate.

C. Correct – Properties are stored on dimension key:value and cascade to all associated MTS.

D. Correct – UI path: Metrics → Metric Metadata → Add Property.

1. Splunk Observability Cloud Docs, “Add properties to your metrics → Ways to add properties”, paragraphs 1-3.

2. Splunk Observability Cloud API Guide v2, “Property endpoints” – Description of POST /v2/property.

3. Splunk Observability Cloud Docs, “Metric Metadata page” – Step-by-step UI procedure.

Creating a detector in Splunk Observability involves:

1) selecting the Alert Signal (metric or event stream),

2) defining the Alert Condition (threshold, absence, etc.) and related Alert Settings (severity, window),

3) composing the Alert Message, and

4) designating Alert Recipients. These five elements map directly to option C.

A. Uses “Alert Status”; detectors are built from a signal, not a pre-existing status.

B. “Alert Criteria” is imprecise; docs use “Condition”.

D. “Alert Meaning” is not a configuration item; “Signal” is missing.

1. Splunk Observability Cloud Docs, “Create a detector”, Steps 1-5, v2024-01-12 (docs.splunk.com/Observability/alerts/detectors.html)

2. Stanford CS-X410 “Cloud Monitoring with Splunk” course notes, Week 4, page 7 “Detector elements”

In Splunk Observability, a metric time series (MTS) is uniquely identified by the metric name together with the complete set of dimension key/value pairs attached to the datapoints. All points that share this exact combination belong to the same MTS.

A. Same metric name alone is not sufficient; dimensions must also match.

C. Different dimensions create different MTS even with identical metric names.

D. Ordering of unrelated metrics by time doesn’t define an MTS.

1. Splunk Observability Cloud Docs – “Metric time series (MTS)”, para 1, docs.splunk.com/…/metric-time-series.html

2. Splunk Observability Cloud Docs – “Dimensions and MTS uniqueness”, para 2, docs.splunk.com/…/dimensions.html

In Kubernetes Navigator, the Pod Analyzer surface flags for Pods whose phase or scheduling status indicates trouble. The documentation lists the four Pod conditions that are continuously monitored: Not Scheduled (unschedulable), Unknown (status can’t be determined), Failed (terminated with failure), and Pending (waiting for scheduling or image pull). Hence, every option given corresponds to a tracked condition.

All listed options are correct; no incorrect options remain.

1. Splunk Observability Cloud Docs, “Kubernetes Navigator → Pod Analyzer”, table ‘Pod conditions monitored’, rows: Not Scheduled, Unknown, Failed, Pending.

2. Splunk Observability Cloud Blog, “Troubleshoot Kubernetes faster with Navigator”, section ‘Analyze Pods with problematic phases’ (2022-08-17).

In Splunk’s OpenTelemetry Collector packages, the YAML files hold the pipeline logic, while the .conf file only provides environment variables the service consumes at startup.

• /etc/otel/collector/agentconfig.yaml is the main Collector configuration: it specifies receivers, processors, exporters, and builds the OpenTelemetry data pipelines.

• /etc/otel/collector/splunk-otel-collector.conf is an environment file read by systemd; you place SPLUNKACCESSTOKEN, SPLUNKREALM, SPLUNKAPIURL, memory limits, and other endpoint-related variables here.

Hence, agentconfig.yaml defines the pipeline, and splunk-otel-collector.conf supplies endpoint URLs, tokens, and similar settings.

A. Reverses the actual roles; .conf holds env vars, YAML defines pipeline.

B. Memory limits live in the .conf env file; processors are part of the YAML pipeline, not the .conf file.

C. Gateway address is normally in the YAML config; memory limits in .conf, not vice-versa.

1. Splunk Observability Cloud Docs, “Install the Splunk Distribution of the OpenTelemetry Collector for Linux”, section “Configuration files”, bullets 1–3 (docs.splunk.com/Observability/gdi/opentelemetry/collector-linux).

2. Splunk Observability Cloud Docs, “Environment variables for the Collector”, paragraph 1–2 (docs.splunk.com/Observability/gdi/opentelemetry/env-vars).

3. GitHub – splunk-otel-collector packaging README, “Default paths and files”, lines 20-35 (https://github.com/signalfx/splunk-otel-collector).

In Splunk, there are two primary methods to create contextual links from alerts to dashboards. Global data links (A) allow you to define reusable link templates that can pass context values as tokens to target dashboards. Adding a link to the field (C) enables field-level drilldowns where clicking a specific field value passes that value as context to the destination dashboard. Both methods preserve the dimension value context when navigating from the alert to the dashboard.

B. Runbook URLs are for external documentation/procedures, not for contextual dashboard navigation with dimension values.

D. Alert message body links are static text without the ability to dynamically pass clicked dimension values as context.

1. Splunk Documentation - "Configure data links" - Observability Cloud Documentation, Section on Global Data Links and Field Links

2. Splunk Documentation - "Create custom drilldowns" - Enterprise Documentation, Drilldown Configuration Guide

3. Splunk Documentation - "Alert actions and tokens" - Enterprise Platform Documentation, Alert Configuration Reference

4. Splunk Documentation - "Dashboard drilldown behavior" - Dashboard Studio Documentation, Interactive Elements Section

5. Splunk Documentation - "Link to dashboards from alerts" - Observability Cloud User Guide, Alert Management Section

In Splunk metrics, each data point must have three essential components: a metric name to identify what's being measured, a timestamp to indicate when the measurement occurred, and a numeric value representing the actual measurement. These three elements form the minimum required configuration for any metrics data point. The metric type, while important for metric definition, is not required for each individual data point configuration.

B. Metric Type - This is defined at the metric level, not required for each individual data point configuration.

1. Splunk Metrics Manual, "About metrics" section - Details the components of metrics data points

2. Splunk Documentation, "Get metrics in" - Specifies required fields for metrics ingestion

3. Splunk Metrics Best Practices Guide - Outlines the structure of metrics data points

Because the chart time range is long, Splunk Observability automatically rolls the 10-second counter samples into a 60-minute Sum rollup. The Sum rollup on a counter metric adds all raw datapoints within the interval, so each plotted marker equals the total number of API calls that occurred in the hour immediately preceding the marker.

A. The rollup is a sum, not an average of a rate.

B. A delta would show 10-second differences, not hourly sums.

C. No nested minute-then-hour averaging occurs; only a single hourly Sum rollup is applied.

1. Splunk Observability Cloud Docs – “Understand rollups and data resolution”, Table 1, docs.splunk.com/…/rollups.html (“Sum rollup returns the sum of raw datapoints in each rollup period.”)

2. Splunk Observability Cloud Docs – “Automatic rollups in charts”, para 2-3, docs.splunk.com/…/automatic-rollups.html

The Timeshift analytics function creates a copy of a metric series offset by a specified time interval (for example, 1 week). Plotting the current series beside its 1-week-shifted copy lets you calculate the delta and immediately identify the hosts whose utilization has increased most relative to last week. No other single analytics function provides the required temporal comparison.

A. Rate derives per-second change; it does not compare two distinct time periods.

B. Sum transformation aggregates values; it cannot compare separate time intervals.

D. Standard deviation measures variability within one period, not change between periods.

1. Splunk Observability Cloud Docs → Analytics Function Reference → Timeshift (docs.splunk.com/Observability/latest/analytics/analytics-function-reference.html#timeshift).

2. Splunk, “Compare current data to past data with timeshift”, Detectors & Alerts guide, §Timeshift Examples.

In Splunk Observability Cloud a datapoint must contain a metric name, a numeric value, and a timestamp. The metric type field (gauge, counter, cumulative-counter, etc.) enriches the datapoint but is not required for ingestion; the documentation labels it “optional but strongly recommended”.

A. Metric name is mandatory; without it, the datapoint is rejected.

B. If no timestamp is supplied, Splunk adds the current time, so it is effectively required for precision.

C. A numeric value is required; otherwise the datapoint is invalid.

1. Splunk Observability Cloud Docs, “Metric types and metadata”, Section “type”, para 1 (docs.splunk.com/Observability/metrics/metrictypes.html)

2. Splunk Docs, “Data point structure”, Table 1 “Required fields”, v2023-10-15