Site Reliability Interview Questions:Mock Interviews

Advancing as a Site Reliability Engineer

The career path for a Site Reliability Engineer (SRE) is a journey of increasing scope and impact, moving from tactical execution to strategic influence. Initially, an SRE might focus on monitoring, responding to incidents, and automating specific operational tasks. As they progress to a senior level, their responsibilities broaden to include designing scalable and resilient systems, defining reliability standards, and mentoring junior engineers. The primary challenges in this progression involve moving beyond fixing individual problems to preventing entire classes of them. This requires a deep shift in mindset from reactive to proactive. A key hurdle is learning to influence development teams and product management to prioritize reliability features. Overcoming this involves mastering the language of business impact, using data from SLOs and error budgets to justify engineering effort. The most critical breakthrough points are mastering automation at a systemic level to eliminate toil and developing the architectural foresight to lead reliability decisions early in the design lifecycle. Ultimately, the path can lead to principal SRE roles, focusing on the most complex technical challenges, or management tracks, guiding the organization's overall reliability strategy.

Site Reliability Job Skill Interpretation

Key Responsibilities Interpretation

A Site Reliability Engineer (SRE) is fundamentally a software engineer tasked with ensuring that a service or system meets its user's expectations for reliability, performance, and availability. Their core mission is to apply software engineering principles to solve infrastructure and operations problems. This involves a blend of proactive design and reactive incident management. SREs spend a significant portion of their time automating operational tasks to reduce manual, repetitive work (toil) and improve system efficiency. They are also responsible for establishing and monitoring Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to create a data-driven approach to reliability. When incidents occur, SREs lead the response, troubleshoot complex issues across the stack, and conduct blameless post-mortems to learn from failures and prevent recurrence. Ultimately, an SRE's value is in building and maintaining large-scale systems that are not only stable but can also evolve and scale rapidly.

Must-Have Skills

Software Engineering & Scripting: You must be proficient in at least one high-level language like Python, Go, or Java. This is not just for writing small scripts but for building robust automation tools and making code-level changes to improve system reliability. These skills are essential for treating operations as a software problem.
Cloud Platforms: Deep expertise in a major cloud provider like AWS, Google Cloud Platform (GCP), or Azure is non-negotiable. Modern systems are built on the cloud, and you need to understand how to architect, deploy, and manage highly available services within these environments. This includes knowledge of their core compute, storage, networking, and database services.
Containerization and Orchestration: Mastery of Docker and Kubernetes is fundamental for managing modern, cloud-native applications. You need to understand how to build, deploy, and scale containerized services efficiently. This includes knowledge of pod scheduling, networking, storage, and managing the entire Kubernetes cluster lifecycle.
Infrastructure as Code (IaC): You must be skilled with tools like Terraform or Ansible to manage infrastructure through code. This practice is crucial for creating consistent, repeatable, and automated environments, which significantly reduces manual errors and improves deployment speed.
Monitoring and Observability: Proficiency with monitoring tools (like Prometheus) and visualization platforms (like Grafana) is essential. More importantly, you need a strong grasp of observability principles, using logs, metrics, and traces to gain deep insights into system behavior and diagnose complex issues.
CI/CD Pipelines: A solid understanding of continuous integration and continuous delivery (CI/CD) is required. SREs are deeply involved in the software delivery lifecycle to ensure that new code can be deployed safely and reliably without impacting production.
Linux/Unix Systems Administration: A deep understanding of the Linux operating system is a foundational requirement. You need to be comfortable with the command line, understand system internals, and be able to debug performance issues at the OS level. This knowledge is critical as most cloud infrastructure runs on Linux.
Networking Fundamentals: You must have a strong grasp of core networking concepts, including TCP/IP, DNS, HTTP/S, and load balancing. SREs frequently troubleshoot issues that span across multiple services and network layers. This knowledge is vital for diagnosing latency, connectivity, and configuration problems in distributed systems.

Preferred Qualifications

Chaos Engineering: Experience with chaos engineering involves proactively injecting failures into a system to test its resilience. This skill is a significant plus because it demonstrates a mature, offensive approach to reliability, helping to identify weaknesses before they cause real outages. It shows you think about building systems that are designed to fail gracefully.
Distributed Systems Design: A strong theoretical and practical understanding of distributed systems is a powerful differentiator. This includes concepts like consensus, replication, and partitioning. It allows you to contribute to architectural decisions, ensuring that systems are designed for scalability and fault tolerance from the ground up.
Security Best Practices: Knowledge of security engineering (DevSecOps) is increasingly valuable. An SRE who can identify and mitigate security vulnerabilities within the infrastructure and deployment pipeline is highly sought after. This skill ensures that reliability efforts are not undermined by security incidents.

Observability Beyond Traditional Monitoring

The shift from monitoring to observability represents a crucial evolution in how we manage complex systems. Traditional monitoring focuses on predefined metrics and known failure modes; we watch for CPU spikes or disk space alerts because we've been burned by them before. Observability, on the other hand, is about having the ability to ask arbitrary questions about your system's behavior without having to predict those questions in advance. It's the capacity to infer a system's internal state from its external outputs, which are typically categorized into three pillars: metrics, logs, and traces. In today's world of microservices and distributed architectures, the number of "unknown unknowns" has exploded. A simple user request might traverse dozens of services, making it impossible to pre-configure a dashboard for every potential failure. Observability gives engineers the tools to explore and understand emergent, unpredictable problems, moving from "the system is broken" to "the system is broken in this specific way for these specific users because of a cascading failure that started here." This deeper understanding is fundamental to the SRE goal of building truly resilient systems.

The Importance of Error Budgets

Error budgets are a core SRE practice that provides a data-driven framework for balancing reliability with the pace of innovation. An error budget is the inverse of a Service Level Objective (SLO); if your availability SLO is 99.9%, your error budget is the remaining 0.1% of time where the service is allowed to fail. This seemingly simple concept is revolutionary because it reframes the conversation between development and operations. Instead of a zero-tolerance policy on failure, which stifles innovation, the error budget gives product teams a clear, quantifiable amount of risk they can take. As long as the service is meeting its SLO and the error budget is not depleted, developers are free to launch new features and make changes. However, if failures cause the error budget to be spent, a pre-agreed policy kicks in—often a freeze on new releases, with all engineering efforts redirected to improving reliability until the budget is back in the green. This creates shared ownership of reliability, aligning the incentives of both developers and SREs. It transforms reliability from a vague goal into a finite resource that must be managed collaboratively.

Embracing Infrastructure as Code Automation

Infrastructure as Code (IaC) is a foundational practice for modern Site Reliability Engineering, treating infrastructure configuration and management as a software development problem. By defining infrastructure—servers, networks, databases, and load balancers—in machine-readable definition files (using tools like Terraform or Ansible), SREs can automate provisioning and management at scale. This approach is critical for reliability because it eliminates manual configuration, a major source of human error and inconsistency across environments. With IaC, every change to the infrastructure is version-controlled, peer-reviewed, and tested before deployment, just like application code. This creates an auditable history of changes and enables rapid, repeatable deployments. The true power of IaC lies in its ability to facilitate automated, self-healing systems and disaster recovery. If an entire region goes down, an SRE team can use their IaC definitions to rebuild the entire infrastructure stack in a new region within minutes, not hours or days. This transforms infrastructure from a fragile, handcrafted entity into a robust, disposable, and reproducible asset.

10 Typical Site Reliability Interview Questions

Question 1：Explain the difference between SLIs, SLOs, and SLAs.

Points of Assessment: Assesses the candidate's understanding of the foundational principles of SRE. Evaluates their ability to define and measure reliability in concrete, user-centric terms. Determines if they can differentiate between internal goals and external promises.
Standard Answer: "SLIs, SLOs, and SLAs are the building blocks of how we measure and manage reliability. An SLI, or Service Level Indicator, is a direct measurement of a service's performance, like latency, error rate, or availability. An SLO, or Service Level Objective, is the target value we set for an SLI over a period of time; for example, '99.9% of requests will be served in under 200ms over a 30-day window'. SLOs are internal goals that guide our engineering decisions. Finally, an SLA, or Service Level Agreement, is a formal contract with a customer that defines the reliability they can expect, often with financial penalties if we fail to meet it. SLAs are typically looser than our internal SLOs to give us a buffer. In essence, we use SLIs to measure performance, set SLOs as our internal targets, and commit to SLAs as our external promises."
Common Pitfalls: Confusing the terms, particularly SLOs and SLAs. Describing them in purely technical terms without connecting them to user experience or business impact. Not being able to provide a clear, practical example of each.
Potential Follow-up Questions:
- How would you choose the right SLIs for a new microservice?
- Describe a time you had to renegotiate an SLO. What was the reason?
- What happens when an error budget tied to an SLO is depleted?

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

Points of Assessment: Evaluates the candidate's systematic troubleshooting methodology. Assesses their ability to think broadly across a distributed system (frontend, backend, database, network). Tests their knowledge of common performance bottlenecks and diagnostic tools.
Standard Answer: "My first step would be to quickly assess the blast radius: is it affecting all users or a specific subset? I'd start by looking at our high-level dashboards in Grafana or Datadog to check key SLIs like latency and error rates across the whole system. Assuming the issue is widespread, I'd follow the request path. I'd check the load balancers for uneven distribution, then move to the web servers to check for CPU or memory saturation. Next, I'd investigate the application servers, looking at application logs and traces to see if a specific endpoint or microservice is slow. If the application layer seems healthy, I'd investigate the database, checking for slow queries, high connection counts, or replication lag. Simultaneously, I'd check for any recent deployments or configuration changes that correlate with the start of the slowdown. The goal is to systematically narrow down the problem from the user's entry point through to the backend dependencies."
Common Pitfalls: Jumping to a specific cause without a structured approach. Naming tools without explaining what they would look for. Forgetting to consider external dependencies or recent changes as potential causes.
Potential Follow-up Questions:
- What specific metrics would you look at on a Linux web server?
- How would you determine if it's a network issue versus an application issue?
- Let's say you find a slow database query. What are your next steps?

Question 3：How do you define and reduce "toil" in an operational environment?

Points of Assessment: Tests the candidate's understanding of a core SRE principle. Assesses their commitment to automation. Evaluates their ability to identify and prioritize tasks that should be automated.
Standard Answer: "Toil is the kind of operational work that is manual, repetitive, automatable, tactical, and devoid of long-term value. An example would be manually restarting a server every time a specific alert fires. My approach to reducing toil is to first measure it; we need to know how much time the team is spending on it. Once we identify the most time-consuming toil, we prioritize it based on its impact and the effort required to automate it. The solution is always to apply software engineering principles—we write code to automate the task away. This could mean building a tool to handle the task automatically, improving the underlying system to be more resilient so the task is no longer needed, or improving our monitoring to make the task self-service for developers. The ultimate goal is to free up engineering time for long-term projects that improve reliability and scalability."
Common Pitfalls: Providing a vague definition of toil. Only suggesting simple scripting as a solution without considering deeper system improvements. Not mentioning the importance of measuring toil to justify automation efforts.
Potential Follow-up Questions:
- Give an example of a time you successfully automated a toilsome task.
- What is a reasonable percentage of time for an SRE to spend on toil?
- How do you get buy-in from management to spend time on automation instead of new features?

Question 4：Describe the architecture of a highly available and scalable system you have worked on.

Points of Assessment: Assesses practical experience with system design and architecture. Evaluates understanding of reliability and scalability patterns. Tests the ability to communicate complex technical concepts clearly.
Standard Answer: "In a previous role, I worked on a large-scale e-commerce platform designed for high availability. At the edge, we used a global CDN and DNS-based load balancing to route users to the nearest healthy region. Within each region, we had multiple availability zones. Traffic entered through a layer of elastic load balancers that distributed requests to a fleet of auto-scaling web servers running in Kubernetes. These stateless web servers communicated with backend microservices via a service mesh, which handled service discovery and retries. For data persistence, we used a managed relational database with a primary instance in one AZ and a hot standby replica in another for failover, along with read replicas to handle query load. Asynchronous jobs were managed using a message queue to decouple services and handle load spikes gracefully. This multi-layered, redundant architecture ensured that the failure of any single component or even an entire availability zone would not bring down the entire service."
Common Pitfalls: Describing a system without explaining why certain architectural choices were made. Using buzzwords without demonstrating a deep understanding of the concepts. Failing to mention monitoring, deployment strategies, or data management.
Potential Follow-up Questions:
- How did you handle data consistency in that distributed environment?
- What was the biggest reliability challenge you faced with that architecture?
- How did you perform capacity planning for that system?

Question 5：What is the role of a blameless post-mortem, and what are the key components of one?

Points of Assessment: Evaluates the candidate's understanding of SRE culture. Assesses their approach to learning from failure. Tests their ability to facilitate a constructive process for incident review.
Standard Answer: "A blameless post-mortem is a critical process for learning from incidents without pointing fingers. The core philosophy is that people don't cause failures; inadequate systems and processes do. The goal is to understand the contributing factors that led to an outage and identify concrete action items to prevent it from happening again. The key components include a detailed timeline of events from detection to resolution, a clear root cause analysis, an assessment of the incident's impact on users, a list of actions taken during the incident, and, most importantly, a set of actionable follow-up tasks with assigned owners and deadlines. These tasks should focus on improving tooling, processes, or system resilience. The process should be a collaborative discussion, not an interrogation, focused entirely on improvement."
Common Pitfalls: Describing it as a process to find who made a mistake. Listing the components without explaining the cultural importance of the "blameless" aspect. Not emphasizing the importance of actionable follow-up items.
Potential Follow-up Questions:
- How do you ensure a post-mortem remains blameless, especially when a human error was involved?
- Describe an incident where a post-mortem led to a significant improvement in reliability.
- Who should be involved in a post-mortem meeting?

Question 6：How would you design a monitoring system for a new microservice?

Points of Assessment: Assesses knowledge of monitoring and observability best practices. Evaluates their ability to think about what metrics are truly important. Tests their understanding of the different pillars of observability (metrics, logs, traces).
Standard Answer: "For a new microservice, I'd design a monitoring system based on the principles of observability. First, I would instrument the application code to export key metrics, focusing on the 'Four Golden Signals': latency, traffic, errors, and saturation. These would be collected by a time-series database like Prometheus. Second, I would ensure the service produces structured logs in a format like JSON, which can be shipped to a central logging platform like the ELK stack for analysis. Third, I would integrate distributed tracing using a framework like OpenTelemetry, allowing us to trace a single request as it flows through multiple services. Finally, I would create a Grafana dashboard visualizing the key SLIs and set up alerts in Alertmanager that trigger on SLO violations, not just on simple thresholds. This multi-faceted approach ensures we can not only detect problems but also have the context needed to debug them quickly."
Common Pitfalls: Only mentioning one aspect, like metrics, and forgetting logs or traces. Suggesting alerting on noisy, non-actionable metrics (like high CPU) instead of user-impacting signals. Not connecting the monitoring strategy back to SLOs.
Potential Follow-up Questions:
- What's the difference between monitoring and observability?
- How do you avoid "alert fatigue"?
- How would you monitor the dependencies of this microservice?

Question 7：Explain the concept of Infrastructure as Code (IaC) and why it's important for SRE.

Points of Assessment: Tests knowledge of a fundamental DevOps and SRE practice. Evaluates the candidate's understanding of automation and configuration management. Assesses their ability to articulate the benefits of IaC for reliability and scalability.
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 or Ansible are used to declare the desired state of the infrastructure as code. This code is then version-controlled in Git, peer-reviewed, and applied automatically. It's critically important for SRE for several reasons. First, it ensures consistency and eliminates configuration drift between environments. Second, it makes deployments repeatable and scalable. Third, it provides an audit trail of all infrastructure changes. Finally, it enables automated disaster recovery; we can redeploy our entire infrastructure in a new environment from code in a fraction of the time it would take manually."
Common Pitfalls: Describing IaC as just "running scripts." Failing to mention the benefits of version control, peer review, and repeatability. Not being able to name any specific IaC tools.
Potential Follow-up Questions:
- What are the differences between a declarative tool like Terraform and a procedural tool like Ansible?
- How do you manage secrets (like API keys) in an IaC workflow?
- Describe a time IaC helped you recover from a failure.

Question 8：What is Chaos Engineering and why would you use it?

Points of Assessment: Assesses familiarity with advanced reliability practices. Evaluates the candidate's proactive mindset towards identifying system weaknesses. Tests their understanding of how to build resilient systems.
Standard Answer: "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. The goal is to proactively identify weaknesses before they manifest as user-facing outages. It involves intentionally injecting controlled failures—like terminating virtual machines, introducing network latency, or blocking access to a dependency—into a production or pre-production environment. By observing how the system responds, we can validate our assumptions about its resilience and fix issues we find. For example, we might believe our service will fail over gracefully if a database replica goes down. Chaos Engineering allows us to test that hypothesis in a controlled way rather than waiting for it to happen at 3 AM."
Common Pitfalls: Describing it as just "breaking things randomly." Failing to mention the importance of running controlled experiments with a clear hypothesis. Not emphasizing the goal of building confidence and uncovering hidden weaknesses.
Potential Follow-up Questions:
- How would you start implementing Chaos Engineering in an organization that has never done it before?
- What safety precautions must you take before running chaos experiments in production?
- Can you name any popular Chaos Engineering tools?

Question 9：How do you handle being on-call? What makes for a good on-call experience?

Points of Assessment: Evaluates the candidate's ability to handle pressure and stress. Assesses their understanding of the human factors in reliability. Tests their thoughts on process, documentation, and tooling for on-call rotations.
Standard Answer: "I view being on-call as a critical responsibility for protecting the user experience. When an alert comes in, my first step is to acknowledge it so the team knows I'm on it. I then focus on stabilizing the system and mitigating the impact as quickly as possible, even if it's a temporary workaround. A good on-call experience is built on three pillars: clear, actionable alerts; comprehensive documentation in the form of runbooks; and a supportive team culture. Alerts should only fire for urgent, user-impacting issues and should link directly to a runbook that outlines diagnostic and remediation steps. The culture should encourage collaboration and make it easy to escalate for help without fear of judgment. Ultimately, the goal of on-call should be to make itself unnecessary by fixing the underlying causes of alerts."
Common Pitfalls: Focusing only on their technical ability to fix problems. Complaining about being on-call without offering constructive ideas for improvement. Not mentioning the importance of documentation, clear alerting, or team support.
Potential Follow-up Questions:
- What, in your opinion, is the single most important factor for reducing on-call burden?
- How do you ensure runbooks stay up-to-date?
- Describe a particularly challenging on-call incident and how you handled it.

Question 10：How do you balance the need for new features with the need for reliability?

Points of Assessment: Assesses strategic thinking and understanding of business trade-offs. Evaluates their ability to use SRE principles to drive data-informed decisions. Tests their collaboration and communication skills.
Standard Answer: "This is the classic tension that SRE was designed to solve, and the primary tool we use to manage it is the error budget. The error budget, derived from our SLOs, provides an objective, data-driven framework for making this trade-off. As long as we are meeting our reliability targets and have a healthy error budget, the development team has the green light to prioritize and ship new features. However, if we start burning through our error budget and risk violating our SLO, we have a pre-agreed policy that development velocity slows down, and engineering efforts are redirected to focus on reliability work. This approach removes emotion and opinion from the decision-making process and creates a shared ownership of reliability between the SRE and development teams."
Common Pitfalls: Giving a generic answer like "we need to find a balance." Not mentioning the concept of error budgets or SLOs. Portraying it as a conflict (SRE vs. Dev) rather than a collaborative process.
Potential Follow-up Questions:
- How do you get buy-in from product managers to use an error budget policy?
- What do you do if one specific feature is responsible for consuming the entire error budget?
- Can you have too much reliability?

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：Systematic Troubleshooting and Problem-Solving

As an AI interviewer, I will assess your ability to diagnose complex technical issues under pressure. For instance, I may present a scenario like, "A key availability SLO has been breached, and latency is spiking for 10% of your users. Initial dashboards show no obvious cause. What are your first five steps?" to evaluate your logical process, your knowledge of diagnostic tools, and your ability to systematically isolate a fault in a distributed system.

Assessment Two：Reliability-Focused System Design

As an AI interviewer, I will assess your proficiency in designing resilient and scalable systems. For instance, I may ask you "Design a system for a real-time notification service that must deliver 1 million messages per minute with 99.99% reliability. What are the key architectural components, how would you ensure fault tolerance, and what SLIs would you track?" to evaluate your understanding of redundancy, failover mechanisms, and proactive reliability planning.

Assessment Three：SRE Principles and Cultural Fit

As an AI interviewer, I will assess your alignment with core SRE philosophies like automation, blamelessness, and data-driven decision-making. For instance, I may ask you "A recent outage was caused by a manual configuration error during a deployment. How would you lead the post-mortem process, and what kind of long-term solutions would you propose to prevent recurrence?" to evaluate your commitment to eliminating toil and fostering a culture of continuous improvement.

Start Your Mock Interview Practice

Click to start the simulation practice 👉 OfferEasy AI Interview – AI Mock Interview Practice to Boost Job Offer Success

Whether you're a recent graduate 🎓, switching careers 🔄, or targeting that dream job 🌟 — our tool empowers you to practice effectively and shine in every interview.

Authorship & Review

This article was written by Ethan Carter, Principal Site Reliability Engineer,
and reviewed for accuracy by Leo, Senior Director of Human Resources Recruitment.
Last updated: 2025-08

References

(SRE Principles and Best Practices)

(SRE Career and Role Information)

(SRE Interview Preparation)

(Learning Resources and Guides)