SRE Interview Questions : Mock Interviews

A SysAdmin's Journey to SRE Leadership

Alex began his career as a systems administrator, skilled in manually managing servers and responding to alerts. As the company's services grew, the manual approach became unsustainable, leading to frequent outages and burnout. Frustrated but determined, Alex taught himself Python to automate repetitive tasks and started exploring distributed systems concepts. This proactive mindset led him to transition into the company's first Site Reliability Engineer role. He championed the adoption of monitoring tools like Prometheus and implemented a blameless post-mortem culture. After successfully navigating a major multi-region outage by leveraging his automation scripts and deep system knowledge, he proved the immense value of the SRE discipline. This success eventually propelled him into a leadership position, where he now builds and mentors a team of SREs dedicated to proactive reliability.

SRE Job Skill Interpretation

Key Responsibilities Interpretation

A Site Reliability Engineer (SRE) acts as the crucial bridge between software development and IT operations, applying a software engineering mindset to system administration challenges. The primary goal is to create scalable, ultra-reliable software systems that deliver a seamless user experience. SREs spend their time diagnosing and resolving production issues, but their core value lies in preventing those issues from recurring. This involves designing and implementing robust monitoring and alerting systems, defining Service Level Objectives (SLOs), and managing error budgets. A key responsibility is automating operational tasks to eliminate manual labor (toil), which frees up engineering time for long-term projects. SREs are also central to leading the incident response process, from initial alert to post-mortem analysis and remedial action. Ultimately, they are the guardians of production, ensuring that the system's availability, performance, and capacity meet the ever-growing demands of the business.

Must-Have Skills

Linux/Unix Systems: A deep understanding of the operating system is essential for troubleshooting, performance tuning, and managing system resources.
Programming/Scripting: Proficiency in languages like Python or Go is required to automate operational tasks, build tooling, and contribute to the application codebase.
Container Orchestration (Kubernetes): Mastering Kubernetes is crucial for managing, scaling, and deploying containerized applications in modern cloud-native environments.
Cloud Platforms (AWS/GCP/Azure): Hands-on experience with at least one major cloud provider is necessary for managing infrastructure, networking, and platform services.
Monitoring & Observability: You must be skilled with tools like Prometheus, Grafana, and the ELK stack to gain insights into system health and diagnose issues proactively.
CI/CD Pipelines: Knowledge of tools like Jenkins or GitLab CI is needed to build and maintain automated build, test, and deployment pipelines.
Networking Fundamentals: A strong grasp of TCP/IP, DNS, HTTP, and load balancing is vital for diagnosing connectivity and latency issues in distributed systems.
Distributed Systems Concepts: Understanding principles like consensus, replication, and fault tolerance is key to building and maintaining reliable large-scale services.
Incident Management: The ability to calmly lead incident response, from diagnosis to resolution and post-mortem, is a core competency for any SRE.
Infrastructure as Code (IaC): Experience with tools like Terraform or Ansible is required to manage infrastructure programmatically, ensuring consistency and repeatability.

Preferred Qualifications

Chaos Engineering: Experience with deliberately injecting failure into systems to identify weaknesses before they cause outages demonstrates a proactive approach to reliability.
Database Reliability Engineering: Specialized knowledge in managing the performance, scalability, and reliability of databases (SQL or NoSQL) is highly valued for data-intensive applications.
Security Best Practices (DevSecOps): An understanding of security principles and experience integrating security controls into the CI/CD pipeline makes you a more well-rounded and valuable engineer.

The Evolution from DevOps to SRE

While often used interchangeably, DevOps and SRE represent distinct philosophies with overlapping goals. DevOps is a cultural movement that emphasizes collaboration, communication, and integration between development and operations teams to accelerate software delivery. It focuses on breaking down silos and improving the "how" of building and shipping software. SRE, born at Google, is a specific implementation of DevOps principles that applies a software engineering approach to operations problems. It is highly prescriptive, using data-driven metrics like Service Level Objectives (SLOs) and error budgets to balance reliability with feature development speed. An SRE team is fundamentally an engineering team that owns the reliability of the production environment. They are empowered to push back on releases that violate error budgets and spend at least 50% of their time on engineering work—automating, building tools, and improving system architecture—to eliminate manual toil. In essence, while DevOps provides the guiding philosophy, SRE provides the concrete engineering discipline to achieve it at scale.

Mastering Chaos Engineering for Resilient Systems

Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system's capability to withstand turbulent conditions in production. It's not about breaking things randomly; rather, it is a methodical, controlled approach to identifying systemic weaknesses before they manifest as user-facing outages. The process involves forming a hypothesis about how the system will react to a specific failure (e.g., "the service will remain available if one database replica goes down"), injecting that failure in a controlled environment, and observing the outcome. If the system behaves as expected, confidence in its resilience increases. If it doesn't, the experiment has successfully revealed a critical weakness that can be fixed. For SREs, Chaos Engineering is a powerful tool that shifts the focus from reactive incident response to proactive reliability improvement. It helps build more robust systems, validates monitoring and alerting, and prepares on-call engineers for real-world failures, ultimately leading to higher availability and a better user experience.

The Rise of FinOps in SRE

As organizations increasingly migrate to the cloud, managing costs has become a significant challenge. The pay-as-you-go model offers flexibility but can lead to spiraling expenses if not managed carefully. This has led to the emergence of FinOps, a cultural practice that brings financial accountability to the variable spend model of the cloud. For SREs, FinOps is becoming an integral part of their a role. Their deep understanding of system architecture, performance, and capacity planning puts them in a unique position to drive cost efficiency. SREs contribute to FinOps by optimizing resource utilization, implementing auto-scaling policies, identifying and eliminating waste (e.g., zombie instances or oversized databases), and selecting cost-effective service tiers. By correlating performance metrics with cost data, SREs can make informed decisions that balance reliability, performance, and budget. This skillset is increasingly sought after, as it directly ties engineering efforts to the financial health of the business, proving the SRE function's value beyond just uptime.

10 Typical SRE Interview Questions

Question 1：How do you define and measure reliability? Explain SLOs, SLIs, and SLAs.

Points of Assessment: Assesses your understanding of core SRE principles, your ability to think in terms of user experience, and your data-driven approach to reliability.
Standard Answer: "Reliability is the measure of a system's ability to consistently meet user expectations. We measure it quantitatively using a hierarchy of metrics. SLIs (Service Level Indicators) are the direct measurements, like request latency or error rate. Based on these, we define SLOs (Service Level Objectives), which are internal targets for reliability, such as '99.95% of requests should be served in under 200ms.' SLOs are what we promise to our users and what guides our engineering decisions. An SLA (Service Level Agreement) is a formal, often legally binding, contract with a customer that defines the consequences, typically financial, if our SLOs are not met. As an SRE, my focus is on defining meaningful SLIs and meeting our SLOs, which in turn ensures we uphold our SLAs."
Common Pitfalls: Confusing the definitions of SLI, SLO, and SLA; providing vague, non-quantitative definitions of reliability.
Potential Follow-up Questions:
- How would you choose an appropriate SLI for a new service?
- What happens when a service is about to breach its SLO?
- Can you give an example of a good SLO vs. a bad SLO?

Question 2：Describe an incident you managed. What was the issue, how did you resolve it, and what did you learn from the post-mortem?

Points of Assessment: Evaluates your hands-on experience with incident response, your troubleshooting methodology, and your commitment to learning from failure.
Standard Answer: "In a previous role, our e-commerce checkout service experienced a 50% error rate. As the on-call engineer, I was paged and immediately joined the incident call. My first step was to assess the blast radius and communicate the impact. I checked our monitoring dashboards and saw a spike in database connection timeouts. The quick fix was to scale up the database replica pool, which restored service within 15 minutes. The post-mortem investigation revealed that a recent code deployment had introduced an inefficient query that exhausted the connection pool under peak load. Our long-term fixes included adding a code-level circuit breaker, improving our database query monitoring to catch anomalies pre-deployment, and updating our deployment runbook. The key learning was the need for better collaboration between development and SRE during the design phase of new features."
Common Pitfalls: Blaming others for the incident; focusing only on the technical fix without mentioning process improvements or learnings.
Potential Follow-up Questions:
- How do you ensure post-mortem action items are completed?
- What is the role of a blameless post-mortem?
- How did this incident change your on-call process?

Question 3：How would you design a monitoring and alerting system for a new microservice?

Points of Assessment: Tests your system design skills, knowledge of monitoring tools and philosophies (e.g., the four golden signals), and ability to think proactively.
Standard Answer: "I would start by focusing on the four golden signals: latency, traffic, errors, and saturation. For instrumentation, I'd export metrics in the Prometheus format from the application. I would set up a Prometheus server to scrape these metrics and use Grafana for visualization dashboards. For alerting, I'd use Alertmanager, configured with rules that trigger on SLO breaches, not on simple thresholds. For example, I'd alert if 'the 5-minute error rate exceeds 1%' rather than 'CPU is at 80%.' I would also integrate structured logging (e.g., JSON format) sent to an ELK stack for deep debugging. Finally, I would implement distributed tracing using a tool like Jaeger to understand request flows across services. This combination provides comprehensive observability."
Common Pitfalls: Listing tools without explaining the 'why'; designing overly noisy alerts based on causes (like CPU) instead of symptoms (like user-facing errors).
Potential Follow-up Questions:
- How do you avoid alert fatigue?
- What's the difference between monitoring and observability?
- How would you monitor the cost of this new service?

Question 4：Explain the role of automation in SRE. Give an example of 'toil' you've automated.

Points of Assessment: Checks your understanding of a fundamental SRE value: reducing manual, repetitive work to focus on long-term engineering projects.
Standard Answer: "Automation is central to SRE. Its role is to eliminate 'toil'—manual, repetitive, tactical work that scales linearly with service growth and has no enduring value. By automating toil, we reduce the risk of human error, improve response times, and free up engineers to work on projects that improve system reliability and scalability. A concrete example of toil I automated was the process for provisioning new user accounts. It was a manual 10-step process involving multiple systems. I wrote a Python script that used APIs to orchestrate the entire workflow, reducing the task from 15 minutes of manual work to a 30-second automated run. This not only saved time but also enforced consistency and eliminated provisioning errors."
Common Pitfalls: Giving a generic answer without a specific, personal example; misunderstanding the definition of toil.
Potential Follow-up Questions:
- How do you decide what to automate first?
- What is an 'error budget' and how does it relate to automation?
- Can you over-automate? What are the risks?

Question 5：A service is experiencing high latency. How would you troubleshoot it?

Points of Assessment: Assesses your systematic troubleshooting approach, from high-level observation down to specific components, under pressure.
Standard Answer: "I'd follow a systematic approach. First, I would check my monitoring dashboards to understand the scope: Is it affecting all users or a subset? Is it a specific endpoint? What is the trend of the latency increase? I'd look at the four golden signals. Next, I'd check for any recent deployments or configuration changes. Then, I would drill down the stack. I'd start at the load balancer, then the application servers, checking for resource saturation (CPU, memory, I/O). If the application seems healthy, I'd investigate its dependencies, particularly databases, caches, and external APIs. I'd use distributed tracing to pinpoint which part of the request lifecycle is slow. Throughout this process, I would communicate my findings to the incident response team."
Common Pitfalls: Jumping to conclusions without gathering data; not having a structured method for investigation.
Potential Follow-up Questions:
- What tools would you use for this investigation?
- How would you differentiate between a network issue and an application issue?
- At what point would you consider rolling back a recent change?

Question 6：What is Kubernetes, and why is it important for SRE?

Points of Assessment: Tests your knowledge of modern cloud-native infrastructure and your ability to connect a technology to the goals of SRE.
Standard Answer: "Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. For SREs, it's a game-changer for several reasons. First, it provides a declarative API for infrastructure, allowing us to manage our environment with code (IaC), which improves consistency and reduces manual errors. Second, its self-healing capabilities, like restarting failed containers, automatically handle common failures, improving system reliability. Third, features like Horizontal Pod Autoscaling allow services to adapt to traffic changes automatically, ensuring performance and optimizing costs. Finally, it provides a standardized platform for running applications, which simplifies our monitoring, logging, and deployment tooling across the entire company."
Common Pitfalls: Only defining what Kubernetes is without explaining its relevance to the SRE role; showing only a surface-level understanding of its features.
Potential Follow-up Questions:
- Describe the main components of the Kubernetes control plane.
- How would you troubleshoot a CrashLoopBackOff error for a pod?
- What are some security best practices for a Kubernetes cluster?

Question 7：How do you approach capacity planning for a rapidly growing system?

Points of Assessment: Evaluates your forward-thinking and data-driven approach to ensuring a system can handle future load without compromising performance or reliability.
Standard Answer: "My approach to capacity planning is proactive and data-driven. First, I identify the organic growth metric that drives load, such as daily active users or transactions per second. I then correlate this metric with key system resources like CPU, memory, and database capacity. Using historical trend analysis, I project future demand for at least the next 6-12 months. Based on these projections, I model the required infrastructure. I also conduct regular load tests to validate these models and find non-linear scaling bottlenecks. The goal is to always have enough capacity to handle projected load plus a buffer for unexpected spikes, while also optimizing for cost by avoiding excessive over-provisioning."
Common Pitfalls: Suggesting a purely reactive approach (i.e., "add more servers when things get slow"); failing to mention the importance of data and trend analysis.
Potential Follow-up Questions:
- How do you account for seasonal spikes in traffic?
- What tools can you use for load testing?
- How does capacity planning differ in a cloud environment versus on-premise?

Question 8：Describe your experience with Infrastructure as Code (IaC) tools like Terraform or Ansible.

Points of Assessment: Assesses your practical experience with key automation tools and your understanding of their benefits in a modern operations environment.
Standard Answer: "I have extensive experience using Terraform to manage our cloud infrastructure on AWS. We used it to codify everything from our VPC networking and security groups to our Kubernetes clusters and database instances. This approach provided several key benefits. It made our infrastructure repeatable and consistent across environments, eliminating 'configuration drift.' It enabled peer review for infrastructure changes through pull requests, which improved quality and caught potential issues early. It also created a version-controlled history of our infrastructure, making it easy to understand changes over time and to roll back if necessary. I've also used Ansible for configuration management to ensure our virtual machines had the correct software packages and settings."
Common Pitfalls: Simply naming the tools without explaining how they were used to solve a specific problem; confusing the roles of provisioning (Terraform) and configuration management (Ansible).
Potential Follow--up Questions:
- What are some challenges you've faced when using Terraform at scale?
- When would you choose Ansible over Terraform, or vice-versa?
- How do you manage state in Terraform in a team environment?

Question 9：What is a "blameless post-mortem," and why is it a crucial part of SRE culture?

Points of Assessment: Evaluates your understanding of SRE culture, focusing on continuous improvement and psychological safety.
Standard Answer: "A blameless post-mortem is a process for analyzing an incident with the core belief that individuals are not the root cause of failures; systemic issues are. The focus is on understanding the contributing factors—in technology, process, and communication—that allowed the incident to happen, not on assigning blame. This is crucial for SRE culture because it fosters psychological safety. When engineers know they won't be punished for making a mistake, they are more willing to be open and honest about what happened. This transparency is essential for uncovering the true, often complex, root causes of an outage. By focusing on systemic flaws, we can implement more effective, long-lasting fixes that make the entire system more resilient for everyone."
Common Pitfalls: Describing it as a process where "no one is held accountable"; failing to explain why it is so important for reliability.
Potential Follow-up Questions:
- How do you facilitate a post-mortem to ensure it remains blameless?
- What is the difference between a proximate cause and a root cause?
- How do you handle a situation where clear human error was a factor?

Question 10：You've been paged at 3 AM for a critical alert. Walk me through your first 15 minutes.

Points of Assessment: Tests your ability to act calmly and logically under high pressure, your communication skills, and your immediate troubleshooting instincts.
Standard Answer: "First, I would acknowledge the page immediately so the team knows I'm on it. My next action is to understand the alert: what service is it, what is the symptom, and what is its priority? I'd then open our primary monitoring dashboard for that service to assess the blast radius—is it impacting all users or just a fraction? Within the first five minutes, I'd post a brief status update in our incident response channel stating that I'm investigating. I would then check for recent changes, like deployments or feature flag toggles, as they are a common cause. Concurrently, I'd start looking at logs and metrics to form a hypothesis. My goal in the first 15 minutes is not necessarily to solve the problem, but to stabilize the situation if possible (e.g., by rolling back a change), understand the impact, and communicate effectively to escalate for more help if needed."
Common Pitfalls: Describing a panic-driven, disorganized process; forgetting the importance of communication to the rest of the team.
Potential Follow-up Questions:
- At what point do you decide to escalate and wake up another engineer?
- What information do you include in your initial communication?
- How do you balance fixing the issue versus communicating about it?

AI Mock Interview

It is recommended to use AI tools for mock interviews, as they can help you adapt to high-pressure environments in advance and provide immediate feedback on your responses. If I were an AI interviewer designed for this position, I would assess you in the following ways:

Assessment One：System Design and Architecture

As an AI interviewer, I will assess your ability to design reliable and scalable systems. For instance, I may ask you "How would you design a highly available, multi-region web service from the ground up?" to evaluate your thought process on load balancing, data replication, and failure domains. This process typically includes 3 to 5 targeted questions.

Assessment Two：Incident Response and Troubleshooting

As an AI interviewer, I will assess your problem-solving skills under pressure. For instance, I may present a scenario like, "A key API is responding with intermittent 503 errors. Your monitoring shows no CPU or memory pressure. How would you investigate?" to evaluate your logical troubleshooting methodology for complex, multi-component systems. This process typically includes 3 to 5 targeted questions.

Assessment Three：Automation and Tooling Proficiency

As an AI interviewer, I will assess your practical application of SRE principles to reduce operational load. For instance, I may ask you "Describe a tedious operational task you've had to perform and explain how you would design an automated solution for it, including the tools you would choose and why," to evaluate your fit for the role. This process typically includes 3 to 5 targeted questions.

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Authorship & Review

This article was written by Michael Carter, Principal Site Reliability Engineer,
and reviewed for accuracy by Leo, Senior Director of Human Resources Recruitment.
Last updated: 2025-07

References

SRE Fundamentals & Concepts

Job Descriptions & Responsibilities

Site Reliability Engineer Job Description Template - LinkedIn Talent Solutions

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