Site Reliability Engineering Interview Questions:Mock Interviews

Advancing Your SRE Career Journey

A career in Site Reliability Engineering often begins in a role like systems administration or software development, providing a strong foundation. From a junior SRE focusing on incident response and monitoring, the path progresses to senior and principal levels, where the emphasis shifts to architectural design, complex automation, and strategic planning. Key challenges along this journey include keeping pace with evolving technologies like cloud-native tools and AI, and shifting from a reactive to a proactive mindset. Overcoming these hurdles requires a commitment to continuous learning and the ability to influence cross-functional teams. The most critical breakthroughs involve mastering automation to eliminate manual toil and leading complex incident post-mortems to drive systemic improvements. This evolution transforms an engineer from a problem-solver into a strategic leader who ensures the resilience and scalability of the entire system.

Site Reliability Engineering Job Skill Interpretation

Key Responsibilities Interpretation

A Site Reliability Engineer (SRE) acts as a crucial bridge between software development and IT operations, applying software engineering principles to solve operational problems. The core mission is to create scalable, automated, and highly reliable software systems. Key responsibilities include monitoring system performance, managing incidents, automating operational tasks, and ensuring system availability and scalability. SREs are accountable for the entire lifecycle of services—from design and deployment to operations and refinement. They work closely with development teams to embed reliability into the software design process from the very beginning. A primary value they bring is balancing the velocity of new feature releases with the non-negotiable need for system stability. This is achieved through the strategic use of concepts like Service Level Objectives (SLOs) and error budgets. SREs are ultimately responsible for ensuring services meet defined reliability targets through proactive engineering and building automation to manage and remediate issues in complex, large-scale systems.

Must-Have Skills

Coding and Scripting: Proficiency in languages like Python, Go, or Java is essential for automating operational tasks, developing internal tools, and managing configurations. This skill allows SREs to solve problems with code, reducing manual intervention and improving system efficiency.
Linux/Unix Systems Administration: A deep understanding of Linux/Unix operating systems is fundamental, as they form the backbone of most modern infrastructure. Mastery is required for troubleshooting, performance tuning, and managing system resources effectively.
Monitoring and Observability: Expertise in using tools like Prometheus, Grafana, and the ELK stack is critical for gaining insights into system behavior. Observability, the ability to understand a system's internal state from its external outputs, is key to diagnosing and preventing issues.
CI/CD Pipelines: Knowledge of continuous integration and deployment practices is necessary to ensure that new code can be released quickly and reliably. SREs help build and maintain these pipelines to automate the software delivery process.
Cloud Computing Platforms: Hands-on experience with major cloud providers such as AWS, GCP, or Azure is a must-have, as most companies have moved their infrastructure to the cloud. SREs need to know how to deploy, manage, and optimize services in these environments.
Containerization and Orchestration: Proficiency with Docker and Kubernetes is essential for managing modern, microservices-based applications. These technologies are central to building scalable and resilient systems.
Infrastructure as Code (IaC): Experience with tools like Terraform or Ansible is crucial for managing infrastructure in an automated and repeatable way. This practice increases reliability and makes infrastructure management more scalable.
Networking Fundamentals: A solid understanding of networking concepts like TCP/IP, DNS, and load balancing is vital for troubleshooting connectivity issues and optimizing traffic flow. This knowledge is often key to resolving complex incidents.
Incident Management and Response: The ability to effectively manage and respond to production incidents is a core SRE competency. This includes participating in on-call rotations and leading blameless post-mortems to learn from failures.
System Design and Architecture: SREs must be able to design and analyze large-scale distributed systems for reliability, scalability, and performance. This skill is crucial for proactively building fault-tolerant systems from the ground up.

Preferred Qualifications

Advanced Cloud Certifications: Holding certifications like AWS Certified DevOps Engineer or Google Cloud Professional Cloud Architect demonstrates a deep level of expertise and commitment. This validates advanced skills in designing, deploying, and managing applications on specific cloud platforms, making you a more attractive candidate.
Experience with Chaos Engineering: Practical experience with chaos engineering—the practice of intentionally injecting failures into a system to test its resilience—is a significant plus. It shows a proactive approach to identifying weaknesses and improving system reliability before an actual outage occurs.
Security Engineering Experience: As systems become more complex, the line between reliability and security blurs. An SRE with a background in security can better identify and mitigate vulnerabilities, contributing to a more robust and trustworthy system.

The Rise of Platform Engineering

The emergence of Platform Engineering is a significant evolution in the DevOps and SRE landscape, focusing on enhancing developer experience and efficiency. While SREs are primarily concerned with the reliability, performance, and scalability of production systems, platform engineers build and maintain the underlying Internal Developer Platform (IDP) that developers use. This platform provides a standardized, self-service set of tools and infrastructure that streamlines the entire software development lifecycle. SRE and Platform Engineering are not mutually exclusive; they are highly complementary roles. Platform engineers can leverage SRE principles to build more reliable and robust platforms, while SRE teams benefit from the standardized tools and infrastructure provided by the platform to improve overall system reliability. Essentially, platform engineering builds the "paved road" for developers, and SREs ensure that road can handle production traffic safely and efficiently.

Observability-Driven Development

Observability-Driven Development (ODD) represents a crucial "shift-left" trend, embedding observability principles early in the software development lifecycle. Instead of treating monitoring as an afterthought, ODD encourages developers to build applications that are inherently observable from the start. This means instrumenting code with high-quality telemetry—logs, metrics, and traces—during the development phase, not just before production. By doing so, teams can gain deep insights into system behavior in pre-production environments, making it easier to detect and resolve anomalies before they impact users. This proactive approach transforms observability from a reactive troubleshooting tool into a core tenet of software quality and design, ultimately leading to more resilient and maintainable systems.

Integrating AI into SRE Practices

The integration of Artificial Intelligence, specifically AIOps (AI for IT Operations), is revolutionizing how SRE teams manage complex systems. AIOps leverages machine learning and big data analytics to automate and enhance critical IT operations tasks, such as anomaly detection, event correlation, and root cause analysis. Instead of manually sifting through alerts, SREs can rely on AI-powered platforms to predict failures, proactively detect issues, and even automate incident responses. This allows teams to move from a reactive "firefighting" mode to a proactive and predictive stance on reliability. By analyzing vast amounts of operational data, AIOps can identify subtle patterns that precede major outages, significantly reducing downtime and freeing up engineers to focus on strategic, high-value work.

10 Typical Site Reliability Engineering Interview Questions

Question 1：Explain the difference between SLI, SLO, and SLA. How do they relate to an error budget?

Points of Assessment: Assesses the candidate's understanding of the foundational concepts of Site Reliability Engineering. Evaluates their ability to articulate the business and technical importance of reliability metrics. Tests their grasp of how error budgets are derived and used to balance innovation with stability.
Standard Answer: "An SLI, or Service Level Indicator, is a quantitative measure of some aspect of the service, like request latency or system uptime. An SLO, or Service Level Objective, is a target value or range for an SLI that we promise to our users. For example, an SLI could be 'the percentage of successful HTTP requests,' and the corresponding SLO might be '99.9% of requests will be successful over a 28-day window.' An SLA, or Service Level Agreement, is a formal contract with a customer that defines the SLOs and outlines the consequences, often financial, if those SLOs are not met. The error budget is simply 100% minus the SLO. For a 99.9% SLO, the error budget is 0.1%. This budget represents the acceptable amount of unreliability and empowers the team to take calculated risks, such as launching new features, as long as they stay within the budget."
Common Pitfalls: Confusing the definitions of SLI, SLO, and SLA. Failing to explain the practical purpose of an error budget. Describing the concepts in purely academic terms without connecting them to real-world decision-making (e.g., feature releases vs. stability work).
Potential Follow-up Questions:
- How would you choose appropriate SLIs for a new microservice?
- Describe a time you had to make a decision based on the remaining error budget.
- What happens when an error budget is completely consumed before the end of the measurement period?

Question 2：You receive an alert at 3 AM that your web application is running slowly. How would you troubleshoot this issue?

Points of Assessment: Evaluates the candidate's systematic approach to troubleshooting under pressure. Assesses their ability to diagnose issues across different layers of the tech stack (networking, application, database). Tests their knowledge of common monitoring and diagnostic tools.
Standard Answer: "My first step would be to quickly assess the blast radius—is the slowness affecting all users or a specific subset? I'd start by checking our primary monitoring dashboard to look at key metrics like latency, error rates, and traffic volume. I would look for any recent changes, like a new deployment or configuration change, that correlates with the start of the issue. Next, I'd dive deeper into the application layer, checking application performance monitoring (APM) traces to identify slow transactions or database queries. Simultaneously, I'd check system-level metrics like CPU, memory, and I/O on our servers. If the issue points towards the database, I would investigate long-running queries or connection pool exhaustion. Throughout the process, I would maintain clear communication with the incident response team, documenting my findings and actions in our incident management tool."
Common Pitfalls: Jumping to conclusions without gathering data. Failing to mention communication and documentation. Describing a chaotic, non-structured approach. Not considering recent changes as a likely cause.
Potential Follow-up Questions:
- What specific metrics would you look at first on your dashboard?
- How would you determine if it was a network issue versus an application issue?
- What tools would you use to analyze database performance?

Question 3：Describe a time you used automation to reduce "toil." What was the problem, what did you build, and what was the impact?

Points of Assessment: Assesses the candidate's understanding of the core SRE principle of eliminating repetitive, manual work. Evaluates their practical scripting and automation skills. Tests their ability to quantify the impact of their work.
Standard Answer: "In a previous role, our team spent several hours each week manually provisioning and configuring new virtual machines for developers, which was slow and prone to human error. To solve this, I developed a set of Ansible playbooks and wrapped them in a Jenkins pipeline. This allowed developers to request a new environment through a simple Jenkins job, which would then automatically provision the VM, apply the correct configuration, install necessary software, and run a suite of validation tests. The impact was significant: we reduced the time to provision a new environment from 4 hours to under 15 minutes. This not only saved the SRE team approximately 10 hours of toil per week but also dramatically improved developer productivity and ensured all environments were configured consistently."
Common Pitfalls: Providing a vague example without specific details. Failing to explain the "before" and "after" state clearly. Not being able to quantify the time saved or the reduction in errors. Describing a one-off script rather than a robust, reusable automation solution.
Potential Follow-up Questions:
- How did you test your automation before deploying it?
- What challenges did you face while developing this solution?
- How would you improve or expand upon this automation today?

Question 4：What is chaos engineering, and why is it important for reliability?

Points of Assessment: Tests the candidate's knowledge of proactive reliability practices. Assesses their understanding of how to build resilient systems. Evaluates their ability to explain a complex concept clearly.
Standard Answer: "Chaos engineering is the practice of proactively and intentionally injecting failures into a production or pre-production system to test its resilience and identify weaknesses. For example, we might randomly terminate virtual machines, introduce network latency, or block access to a dependency to see how the system behaves. The goal isn't to break things, but to uncover hidden dependencies and unknown failure modes in a controlled environment before they cause a real outage. It's important because it helps us build confidence in our system's ability to withstand turbulent, real-world conditions. By regularly running these experiments, we can validate our monitoring, alerting, and auto-remediation mechanisms, ultimately building a more fault-tolerant system."
Common Pitfalls: Describing chaos engineering as just "breaking things randomly." Failing to mention the importance of running experiments in a controlled manner with a clear hypothesis. Not connecting the practice back to the business goal of improving reliability and user experience.
Potential Follow-up Questions:
- What are some tools used for chaos engineering?
- How would you design a chaos experiment to test a specific component's resilience?
- What safety mechanisms would you put in place before running a chaos experiment in production?

Question 5：Explain the difference between blue-green and canary deployments. In what situations would you choose one over the other?

Points of Assessment: Assesses the candidate's knowledge of safe deployment strategies. Evaluates their understanding of the trade-offs between different deployment models. Tests their ability to apply theoretical knowledge to practical scenarios.
Standard Answer: "Both are strategies to reduce the risk of deploying new software. In a blue-green deployment, you have two identical production environments: 'blue' (the current version) and 'green' (the new version). You deploy the new version to the green environment and can run tests against it. Once confident, you switch the router to send all traffic to the green environment, which then becomes the new blue. A canary deployment is more gradual. You roll out the new version to a small subset of users or servers first. You then closely monitor its performance and error rates. If everything looks good, you gradually increase the percentage of traffic going to the new version until it's fully deployed. I would choose blue-green for applications where I need to switch versions quickly and have a simple rollback plan. I'd choose a canary deployment for riskier changes or large-scale systems where I want to validate the new version's performance with real production traffic before committing to a full rollout."
Common Pitfalls: Mixing up the definitions. Failing to explain the key difference in traffic shifting (all at once vs. gradual). Not being able to articulate the specific advantages or disadvantages of each approach.
Potential Follow-up Questions:
- How do you monitor the health of a canary release?
- What are the infrastructure cost implications of a blue-green deployment?
- How would you automate the rollback process for a failed canary deployment?

Question 6：What is the purpose of a blameless post-mortem?

Points of Assessment: Evaluates the candidate's understanding of SRE culture. Assesses their ability to learn from failure and focus on systemic improvements. Tests their communication and collaboration mindset.
Standard Answer: "A blameless post-mortem is a process for analyzing an incident with the primary goal of understanding the contributing factors and preventing its recurrence, without assigning blame to any individual or team. The core belief is that people are not the problem; the system is. We assume everyone involved acted with the best intentions given the information they had at the time. The purpose is to create a psychologically safe environment where engineers can openly share details about the incident, including any mistakes made, without fear of punishment. This open dialogue is crucial for uncovering the true, systemic root causes of the failure. The outcome is a set of actionable steps to improve the system's resilience, tooling, or processes."
Common Pitfalls: Suggesting that "blameless" means "no accountability." Focusing on the individual actions that led to the incident. Failing to emphasize the importance of creating actionable follow-up items to improve the system.
Potential Follow-up Questions:
- Describe the key components you would include in a post-mortem document.
- How would you handle a situation where an individual's mistake was a direct cause of an incident?
- How do you ensure that the action items from a post-mortem are actually completed?

Question 7：How would you design a highly available and scalable system for a popular e-commerce website?

Points of Assessment: Assesses the candidate's system design and architectural skills. Evaluates their knowledge of key components like load balancers, databases, and caches. Tests their ability to think about scalability, reliability, and performance trade-offs.
Standard Answer: "To design a highly available and scalable e-commerce site, I would start with a multi-tiered architecture. At the edge, I'd use a Content Delivery Network (CDN) to cache static assets like images and CSS, reducing latency for users globally. The incoming traffic would hit a load balancer, which distributes requests across a fleet of stateless web servers. These web servers would be in an auto-scaling group to handle traffic spikes. For data storage, I would use a managed relational database service (like Amazon RDS) in a primary-replica configuration for high availability and read scalability. To further reduce database load and improve performance, I would implement multiple layers of caching, including an in-memory cache like Redis for session data and frequently accessed product information. All components would be deployed across multiple availability zones to ensure resilience against single-zone failures."
Common Pitfalls: Providing a very generic or overly simplistic design. Forgetting key components like caching or a CDN. Not considering fault tolerance (e.g., deploying in a single availability zone). Failing to explain the purpose of each component in the design.
Potential Follow-up Questions:
- How would you handle user session data in this distributed environment?
- What kind of database would you choose and why?
- How would you monitor the health and performance of this entire system?

Question 8：What is Infrastructure as Code (IaC) and why is it a cornerstone of SRE?

Points of Assessment: Tests the candidate's understanding of modern infrastructure management. Assesses their familiarity with tools like Terraform or Ansible. Evaluates their ability to explain the benefits of managing infrastructure programmatically.
Standard Answer: "Infrastructure as Code, or IaC, is the practice of managing and provisioning infrastructure through machine-readable definition files, rather than through manual configuration. Tools like Terraform, Ansible, or CloudFormation allow you to define your servers, networks, and databases in code. This code can then be versioned, tested, and deployed just like application code. It's a cornerstone of SRE for several key reasons. First, it ensures consistency and eliminates configuration drift, as every environment is created from the same source of truth. Second, it makes infrastructure changes repeatable and automated, reducing manual effort and the risk of human error. Finally, it enables disaster recovery and scaling, as you can quickly and reliably recreate your entire infrastructure from code in a new region if needed."
Common Pitfalls: Only naming tools without explaining the underlying concept. Failing to connect IaC to core SRE goals like reliability and automation. Not mentioning version control as a key part of the process.
Potential Follow-up Questions:
- What is the difference between a declarative tool like Terraform and a procedural tool like Ansible?
- How do you manage sensitive data, like passwords or API keys, in your IaC code?
- Describe a workflow for reviewing and applying changes to infrastructure using IaC.

Question 9：How do you approach capacity planning for a service?

Points of Assessment: Evaluates the candidate's ability to think proactively about future growth. Assesses their analytical skills and use of data to make decisions. Tests their understanding of resource management and cost optimization.
Standard Answer: "My approach to capacity planning is data-driven and iterative. I start by analyzing historical data on key performance metrics like traffic volume, CPU utilization, and memory usage to understand growth trends. Based on this trend analysis and input from the business about upcoming product launches or marketing campaigns, I create a forecast for future resource needs. I then perform load testing to determine the breaking point of our current infrastructure and validate our scaling mechanisms. This helps us set thresholds for when we need to add more capacity. The plan should also define auto-scaling policies to handle unexpected traffic spikes automatically. Finally, capacity planning is not a one-time event; it's a continuous process of monitoring, forecasting, and adjusting to ensure we have enough resources to meet demand without over-provisioning and wasting money."
Common Pitfalls: Describing a purely reactive approach (i.e., "we add more servers when things get slow"). Forgetting to mention the importance of business context and load testing. Ignoring the cost aspect of capacity planning.
Potential Follow-up Questions:
- What specific metrics would you track to inform your capacity planning?
- How do you balance the cost of excess capacity against the risk of an outage due to insufficient capacity?
- How would you load-test a service to find its limits?

Question 10：What is the difference between monitoring and observability?

Points of Assessment: Tests if the candidate is up-to-date with modern SRE terminology and concepts. Evaluates their ability to explain the nuanced shift from reactive alerting to proactive system understanding. Assesses their grasp of the "three pillars of observability."
Standard Answer: "Monitoring and observability are related but distinct concepts. Monitoring is about collecting and analyzing data from a system to watch for predefined failure modes. We set up alerts based on known thresholds—for example, 'alert me if CPU usage is above 90%.' It tells us whether a system is working. Observability, on the other hand, is about having a system that is transparent enough that you can understand its internal state and debug novel problems without having to ship new code. It allows you to ask arbitrary questions about your system's behavior and get answers. Observability is often described by its 'three pillars': metrics (numeric data), logs (structured event records), and traces (which show the lifecycle of a request as it moves through a distributed system). While monitoring tells you that something is wrong, a truly observable system helps you understand why it's wrong."
Common Pitfalls: Saying they are the same thing. Being unable to define the three pillars of observability. Failing to explain the key difference: monitoring is for known unknowns, while observability is for unknown unknowns.
Potential Follow-up Questions:
- Can you give an example of a problem that would be difficult to solve with monitoring alone but easier with observability?
- How does distributed tracing help in debugging microservices?
- How do you ensure that the logs your applications produce are useful for debugging?

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 resilient and scalable systems. For instance, I may ask you "Walk me through the design of a globally distributed caching layer for a dynamic content website. How would you ensure high availability and low latency?" to evaluate your fit for the role.

Assessment Two：Live Troubleshooting and Incident Response

As an AI interviewer, I will assess your systematic approach to problem-solving under pressure. For instance, I may ask you "You see a 50% increase in 5xx error rates for a critical service, but no alerts have fired for CPU or memory. What are your immediate steps to investigate the root cause?" to evaluate your fit for the role.

Assessment Three：Automation and Coding Proficiency

As an AI interviewer, I will assess your practical ability to automate operational tasks. For instance, I may ask you "Describe the process and write a Python script to parse an application log file, identify all unique error messages, and send a summary report to a Slack channel." to evaluate your fit for the role.

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Authorship & Review

This article was written by David Miller, Principal Site Reliability Engineer,
and reviewed for accuracy by Leo, Senior Director of Human Resources Recruitment.
Last updated: October 2025

References

SRE Fundamentals & Responsibilities

Interview Preparation & Questions

Skills and Career Path

Industry Trends (AIOps, Platform Engineering, Observability)