Monitoring is no longer an afterthought in DevOps—it’s core to uptime, performance, and reliability. As modern infrastructures scale across distributed systems, cloud-native services, and containerized environments, tools like Prometheus, Grafana, and the ELK stack (Elasticsearch, Logstash, Kibana) have become essential in production-ready pipelines.
For aspiring DevOps engineers, interview questions now often focus not just on your ability to deploy or automate infrastructure, but on your ability to monitor, visualize, and respond to production signals. If you’re preparing for a DevOps or SRE role in 2025, expect focused technical interviews on observability and monitoring—especially using Prometheus, Grafana, and ELK.
This guide covers the most common and practical interview questions candidates face across these three tools. Whether you’re preparing for a junior DevOps position after Refonte Learning’s DevOps Engineering internship program or upskilling into a full-time SRE role, this list will help you understand what hiring managers are evaluating and how to answer with confidence.
Prometheus Interview Questions and How to Approach Them
Prometheus is widely used for metrics collection and monitoring. It’s popular in Kubernetes environments and integrates easily with exporters and visualization tools like Grafana. Interviewers typically ask about Prometheus when they want to assess your understanding of metrics-driven operations.
What is Prometheus and how does it work?
This foundational question tests your conceptual understanding. A good answer includes that Prometheus is a time-series database and monitoring system that pulls metrics via HTTP from endpoints called exporters. It stores data locally and supports powerful querying through PromQL. Emphasize its pull model, dimensional data collection, and service discovery capabilities.
What is the difference between push and pull models in Prometheus?
Prometheus uses a pull-based model, scraping metrics from targets at defined intervals. Explain why this model improves control, versioning, and service discovery. You can also mention that the Pushgateway allows push behavior when needed, such as from batch jobs.
What are exporters in Prometheus?
Exporters are agents that expose metrics in a Prometheus-compatible format. Common examples include node_exporter for system metrics and blackbox_exporter for endpoint monitoring. Interviewers want to hear if you’ve configured exporters or written custom ones.
How do you write a PromQL query?
Expect to be asked to write or interpret a PromQL query. For example:
rate(http_requests_total[5m])
Explain that this calculates the per-second rate of HTTP requests over the last five minutes. Prepare to discuss aggregations, label filters, and functions like sum, avg, or max.
How do you set up alerting in Prometheus?
This tests practical experience. Explain that Prometheus uses Alertmanager to send alerts via email, Slack, or webhook. Describe configuring alert rules in YAML and using expressions like:
alert: HighMemoryUsage expr: node_memory_Active_bytes / node_memory_MemTotal_bytes > 0.9
Highlight how alert severity and routing are handled in Alertmanager configurations.
Have you ever used Prometheus in Kubernetes?
This is often a follow-up. Talk about setting up Prometheus via Helm or the Prometheus Operator, and how it discovers pods via service annotations. Mention kube-state-metrics and how you used it to monitor pod health, deployments, or resource usage.
ELK Stack Interview Questions: Elasticsearch, Logstash, Kibana
The ELK stack is a logging and search powerhouse. Companies use it to aggregate, parse, store, and visualize logs at scale. These tools are often used together, but interviewers may focus separately on Elasticsearch, Logstash, or Kibana.
What is the ELK stack and what are its components?
Start by breaking it down. Elasticsearch is a distributed search engine that stores and indexes data. Logstash is a pipeline tool used to collect, transform, and forward logs. Kibana is the visualization layer for interacting with Elasticsearch data. Clarify that ELK helps centralize log data for analysis and alerting.
What is a Logstash pipeline?
A Logstash pipeline has three stages: input, filter, and output. You might be asked to sketch a sample pipeline configuration. Use examples like:
Input: syslog, beats, file
Filter: grok, mutate, date
Output: Elasticsearch
Interviewers look for understanding of parsing (e.g., with grok patterns) and how you handle malformed logs.
How does Elasticsearch store and index data?
This assesses your understanding of search architecture. Explain that Elasticsearch stores data in JSON documents, which are grouped in indexes. Each field is tokenized and indexed, making it searchable through REST APIs. You might also be asked about shards, replicas, and cluster nodes.
What is a grok filter in Logstash?
Grok is a pattern-matching filter used to parse logs. You might be asked to write or interpret one. For example:
%{COMMONAPACHELOG}
Or a custom one like:
%{IP:client} %{WORD:method} %{URIPATHPARAM:request}
Be ready to explain how grok translates unstructured log data into structured fields.
How do you secure an ELK stack deployment?
Mention securing Elasticsearch via TLS, user roles, and API keys. Talk about using reverse proxies, protecting Kibana with authentication, and limiting access via firewalls or AWS security groups. If you’ve deployed ELK in a cloud environment, mention hosted services like Amazon OpenSearch.
How do you troubleshoot slow queries in Elasticsearch?
Explain how to use _explain, _profile, and slow logs to identify bottlenecks. Mention index mapping, shard design, and query structure as common performance factors. Bonus points for explaining how to optimize with filters over full-text queries.
Grafana Interview Questions: Visualization and Dashboards
Grafana is the go-to platform for visualizing time-series data. It integrates with Prometheus, Elasticsearch, and many other sources. Interview questions typically assess your ability to create effective dashboards and alerts.
What is Grafana and how does it differ from Kibana?
Grafana is a visualization tool that supports many data sources and is tailored to time-series analysis. Kibana is primarily tied to Elasticsearch and is used for log analysis. Grafana excels at metrics visualization and alerting across Prometheus, InfluxDB, Graphite, and more.
How do you create a dashboard in Grafana?
Walk through the process: choose a data source (e.g., Prometheus), create panels, define queries, and select visualization types (graph, gauge, table). Interviewers want to hear how you present data clearly and how you make dashboards actionable.
How do you define alerts in Grafana?
Explain that alerts are set up on panels. You define thresholds and evaluation intervals. Alerts can be routed through notification channels like Slack, PagerDuty, or email. Share an example of a CPU usage alert tied to a PromQL query.
Have you ever used Grafana templating?
Templating allows dashboard variables like $hostname or $service so users can dynamically filter data. Describe how you used templates to make reusable dashboards across environments or clients.
What plugins or integrations have you used?
This question tests the breadth of your experience. Mention data sources like MySQL, PostgreSQL, or Loki, and visual panels you’ve used such as heatmaps or stat panels. If you installed plugins via Grafana CLI or used JSON dashboards, share that.
Have you used Grafana for capacity planning or SLA reporting?
If yes, share how you built dashboards that track resource usage over time or measure uptime against service-level objectives. If no, focus on how Grafana helps decision-makers understand trends and risks visually.
What Hiring Managers Want to Hear
Technical interviews are not only about memorization. Interviewers want to see whether you can reason through systems, troubleshoot under pressure, and communicate clearly.
Expect questions like:
How would you monitor a Kubernetes cluster using these tools?
This integrates multiple tools. Talk about using Prometheus with kube-state-metrics, visualizing with Grafana, and shipping logs to Elasticsearch via Fluentd. Explain how you build observability around nodes, pods, and services.
How would you diagnose a spike in 500 errors?
Say you’d start with Prometheus to check service and API error rates, correlate with pod restarts or latency, then review logs in Kibana. This shows you understand end-to-end observability, not just individual tools.
How do you balance alert fatigue vs coverage?
This tests judgment. Talk about tuning thresholds, using multiple severity levels, grouping alerts, and setting up proper silencing rules. Demonstrate you think about maintainability, not just technical correctness.
What trade-offs would you make when choosing Prometheus vs ELK for a project?
Here, the interviewer wants to hear how you align tool choice to use case. Prometheus is best for real-time metrics and alerting. ELK is best for log aggregation and retrospective analysis. Grafana can be the bridge that visualizes both.
If you were onboarding a new team member, how would you explain these tools?
This gauges communication. Focus on system architecture, common workflows, and key concepts. Keep it practical. Show you can teach—not just do.
How Refonte Learning Prepares You for These Interviews
Refonte’s DevOps internship program covers end-to-end monitoring and observability. Students don’t just watch videos—they work through real case scenarios using Prometheus, Grafana, and ELK.
In project simulations, Refonte interns:
Deploy Prometheus with exporters in a Kubernetes cluster
Configure and tune alerts using PromQL
Design dashboards in Grafana to monitor application health
Set up a Logstash pipeline for log ingestion ad filtering
Use Kibana to analyze application errors and performance trends
Build a unified observability report showing how metrics and logs correlate
Interns are evaluated on how they configure, interpret, and respond to monitoring signals. This experience translates directly to interview readiness. When you’ve built dashboards and alerts yourself, you’re better prepared to explain them.
Refonte’s mentorship program also includes mock interview practice for these topics, resume optimization, and portfolio preparation so candidates can showcase their monitoring skills effectively.
Conclusion
If you're aiming to land a DevOps or SRE role in 2025, mastering tools like Prometheus, Grafana, and ELK is no longer optional. These platforms form the core of modern observability stacks—and employers expect candidates who can monitor, diagnose, and optimize production systems.
Interview questions on these tools are designed to assess real-world readiness. They test your ability to apply monitoring theory to practical systems. Refonte Learning’s DevOps program helps you prepare with hands-on labs, project-based internships, and career-focused mentorship.
Don’t just memorize commands. Learn how to build dashboards, write alert rules, and solve real incidents. Join Refonte Learning’s next DevOps internship cohort and take the next step in your engineering career—fully prepared for every question that comes your way.
FAQs
Do I need to know all three tools for a DevOps interview?
Not always, but most roles require Prometheus and at least one log or visualization platform. Knowing all three gives you an edge.
Can I learn these tools without a background in development?
Yes. Refonte teaches each tool from first principles and guides you through practical projects even if you’re new to DevOps.
Are these tools used in cloud environments?
Yes. Prometheus and Grafana are common in Kubernetes, and ELK is widely used with AWS, Azure, and GCP logging pipelines.
What’s the best way to practice PromQL or Logstash pipelines?
Build small projects using public datasets. Refonte provides real scenarios so you can learn by doing, not guessing.
Will these tools be on the job even in non-SRE roles?
Absolutely. Developers, analysts, and support engineers use Grafana and Kibana too. Observability is cross-functional.