FreeCourse Logo
FreeCourse.io
Verified CouponsFree CoursesJobsBlog
Categories
Home/Courses/SRE Interview Practice Tests: 300+ Questions with Answers
SRE Interview Practice Tests: 300+ Questions with Answers
IT & Software100% OFF

SRE Interview Practice Tests: 300+ Questions with Answers

Santosh Harge
0(19 students)
Self-paced
Intermediate

About this course

This course provides practice tests for Site Reliability Engineering interviews, covering the topics most frequently assessed in technical screens at product companies and MNCs. A note on the explanationsEvery question includes a detailed explanation of why the correct answer is correct, why the distractors are plausible, and what distinguishes an adequate answer from a strong one in an interview setting. This reflects my work as an interviewer: I conduct technical and mock interviews through platforms MNC clients.

In my experience, candidates rarely fail because they don't know a definition — they fail on the distinctions. Topics coveredSLI, SLO and SLA — the distinctions between them, availability targets and their time costs, the relationship between internal objectives and contractual agreements, measurement placement, and why latency is measured at percentiles rather than averagesError budgets and burn rate — calculating budgets from objectives, error budget policy and its organisational purpose, burn rate as a leading indicator, and multi-window burn-rate alertingToil and SRE principles — the defining characteristics of toil, the 50% guideline, the relationship between SRE, DevOps and Platform Engineering, and blameless postmortem practiceObservability and Prometheus — the Four Golden Signals, the RED and USE methods, Prometheus metric types, PromQL fundamentals, histograms versus summaries, and cardinality considerationsIntended audienceEngineers preparing for Site Reliability Engineering, DevOps or Platform Engineering interviews. Cloud and DevOps engineers transitioning into SRE roles.

Practising SREs assessing their readiness before interviewing. Familiarity with Linux and general web application architecture is assumed; prior SRE experience is not required. Suggested approachAttempt a test without preparation first.

The pattern of incorrect answers is more informative than the score itself, and indicates where to focus. Reviewing the explanations for correctly answered questions is also recommended, as the reasoning is often more instructive than the outcome. About the instructorI am a Lead Site Reliability Engineer with over 14 years of experience in IT, currently responsible for a Tier-1 mobile platform serving millions of users at a global healthcare technology organization.

My background includes regulated environments in banking and financial services. The material in these questions is drawn from production experience and from interviewing candidates for SRE roles.

Skills you'll gain

Other IT & Softwareen

Available Coupons

Loading...

Course Information

Level: Intermediate

Suitable for learners at this level

Duration: Self-paced

Total course content

Instructor: Santosh Harge

Expert course creator

This course includes:

  • 📹Video lectures
  • 📄Downloadable resources
  • 📱Mobile & desktop access
  • 🎓Certificate of completion
  • ♾️Lifetime access
$0$88.99

Save $88.99 today!

Enroll Now - Free

Redirects to Udemy • Limited free enrollments

Share this course

https://freecourse.io/courses/sre-interview-practice-tests-300-questions-with-answers

You May Also Like

Explore more courses similar to this one

LLMOps: How LLMs Are Deployed and Scaled in Production
IT & Software
0% OFF

LLMOps: How LLMs Are Deployed and Scaled in Production

Abay Assenov

This course contains the use of artificial intelligence.A chatbot demo takes an afternoon. Keeping that same model fast, affordable and trustworthy for thousands of real users is a different job, and it has a name: LLMOps. This course explains that job from end to end. After it, you understand what happens between the moment someone types a question and the moment the answer streams back, why that path costs what it costs, and what teams actually change when their LLM product becomes too slow, too expensive or too unpredictable. You will be able to follow a conversation about KV cache, continuous batching, quantization, time to first token, autoscaling, evals and guardrails, and you will know why each of them matters rather than just what the letters stand for. You will also see how the decisions fit together, so that a new tool or a new model on the market does not confuse you: you will know which layer it touches and which problem it is meant to solve.The course is for people who meet large language models in production and want the whole picture in plain words. That includes software engineers who are about to work next to an LLM platform, DevOps and SRE people curious about GPU serving, data and ML engineers who have trained models but never served one at scale, and product managers, founders and analysts who sit in meetings where these words fly around and have to make decisions about cost and quality. No prior experience with machine learning is needed. If you know roughly what an API is, you have enough to start. The course is not for you if what you want is to sit at a terminal and deploy a model step by step with your own hands. Hands-on LLMOps courses and the documentation of the inference servers do that well, and this course points you to them at the end.What makes this course different is one example carried from the first lesson to the last. You follow HelpDesk AI, a customer support assistant at a mid-size online retailer. It is a composite case built from common public patterns, and the course says so openly, with numbers that are illustrative rather than taken from one company. It starts on a hosted API, works in a pilot, and then runs into a projected bill of tens of thousands of dollars a month and answers that take six seconds at peak. From there you watch the team choose an open-weight model with an eval set of real tickets, serve it with vLLM on GPUs in Kubernetes, crash on memory in the first week, fix latency with batching, caching and quantization, add evals and guardrails before launch, watch the service with tracing and dashboards, and survive a traffic spike six times larger than normal. Every new term appears exactly when the team needs it, so you learn it as the answer to a problem rather than as a definition. Diagrams drawn for the course show the layers, the timelines and the memory on the GPU, so you can see the mechanism instead of imagining it.Inside there are five modules. Module 1 explains what LLMOps is, how it differs from classic MLOps, what the serving stack is made of, and how one request travels through it. Module 2 is the vocabulary of LLM deployment: TTFT, latency and throughput, KV cache and batching, quantization and model size, what inference servers such as vLLM, TGI and Triton actually do, and the cost trade-off between a hosted API and self-hosting. Module 3 is the heart of the course: the HelpDesk AI story in eight lessons, from the exploding API bill to the platform the team ended up with six months later. Module 4 shows LLMOps in daily work: what an engineer's week looks like, how model and prompt changes ship safely through evals, canaries and rollbacks, and where the money goes in an LLM service along with the most common mistakes that waste it. Each module opens with a short key-terms lesson and closes with a printable memo you can keep at hand, plus an optional quiz of two or three questions that you can skip freely.The last module looks forward. It describes the roles around an LLM in production, including the LLMOps or platform engineer, the applied ML engineer, the SRE, the platform team and the product owner, and it helps you see which of them fits your background and interests. It then names what this course deliberately skipped, such as fine-tuning, training at scale and step-by-step setup on a specific cloud, and it lays out a sensible order for going deeper if you decide this is your direction. You finish with a clear map of the field and a short list of the sources worth reading next, starting with the inference server documentation and the PagedAttention paper.Be clear about what this course is not. It does not teach you to deploy, configure or debug a model with your own hands. There are no labs, no coding exercises and no homework. You watch, you understand, and you come away able to reason about LLM serving, read an architecture diagram, ask the right questions in a design review and judge the trade-offs a team is making. If your goal is to type the commands yourself, take this course first for the picture, and then pick a hands-on course for the practice. Two lessons are open as free previews, the first key-terms lesson and the first lesson of the HelpDesk AI story, so you can see the style before you decide.

5.0•110•Self-paced
FREE$87.99
Enroll
Software Testing Concepts + 500 Practice Questions (2026)
IT & Software
0% OFF

Software Testing Concepts + 500 Practice Questions (2026)

Yogesh Dhiman

Are you ready to build a strong foundation in Software Testing Concepts and boost your confidence for any testing exam or interview? This course, Software Testing Concepts + 500 Practice Questions (2026), is designed to help you master manual testing principles, processes, and terminology from scratch.To strengthen your understanding, this course includes 500 carefully designed practice questions covering all major manual testing concepts. These questions are perfect for students preparing for ISTQB, QA certifications, or job interviews, ensuring you understand both the fundamentals and practical applications.This comprehensive Software Testing course provides participants with the knowledge and skills needed to ensure software applications are robust, efficient, and free from critical errors. Designed for beginners and professionals alike, the course covers the fundamental principles of software testing, test planning, and the execution of various testing techniques.You will learn how to identify and address potential bugs in applications, as well as the best practices for validating software functionality. Topics include test case design, automated testing, performance testing, regression testing, and user acceptance testing. Participants will also be introduced to basic automation testing concepts.Throughout the course, you'll gain hands-on experience through real-world projects and case studies, ensuring you're prepared to handle the challenges of a software testing role. By the end of the course, you will have the practical expertise to conduct efficient testing cycles and ensure the delivery of high-quality software products.Key Highlights:Understanding of software testing life cycle and methodologies.Proficiency in manual and automated testing techniques.Expertise in test design and test management.Familiarity with industry-standard testing tools and environments.Practical experience through exercises and real-world case studies.By the end of this course, you will be confident in your testing knowledge, terminology, and process understanding — ready to crack any testing-related exam or interview.Whether you're looking to break into the field of quality assurance or enhance your existing skills, this course will equip you with the tools necessary to excel in software testing.

4.6•5.7K•Self-paced
FREE$91.99
Enroll
Kubernetes & Cloud Native Associate (KCNA) Practice Tests
IT & Software
0% OFF

Kubernetes & Cloud Native Associate (KCNA) Practice Tests

Santosh Harge

Prepare with confidence for the Kubernetes & Cloud Native Associate (KCNA) certification exam. This comprehensive practice test suite is meticulously designed to mirror the actual style, difficulty, and scope of the official Linux Foundation and CNCF exam, ensuring you gain foundational clarity and exam-day readiness.Why Choose These Practice Tests?Fully Aligned with Official CNCF Curriculum: Covers Kubernetes architecture, container orchestration, cloud native ecosystem tools, observability, and application delivery strategies.In-Depth Explanations: Every question includes a detailed technical rationale explaining why the correct option is right and why distractors are incorrect, supported by official Kubernetes and CNCF documentation references.Scenario-Based Realism: Questions reflect real-world container concepts, microservice architectures, service mesh setups, GitOps workflows, and telemetry pipelines.Timed Exam Simulator: Practice under realistic exam time constraints to build speed, accuracy, and test-taking stamina.Exam Domains Covered:Kubernetes Fundamentals (Pods, Workloads, Services, Ingress, Cluster Architecture, API Objects)Container Orchestration (Container runtimes, Storage, Networking, Security, Container standards)Cloud Native Architecture (Cloud native fundamentals, Autoscaling, Serverless, Service Mesh, Storage options)Cloud Native Observability (Prometheus monitoring, Telemetry, Logging, Distributed Tracing, Cost Management)Cloud Native Application Delivery (GitOps workflows, CI/CD, Helm package management, Argo CD, Flux)Master cloud-native concepts, bridge critical knowledge gaps early, and step into your KCNA certification exam fully equipped to pass on your very first attempt.

0.0•0•Self-paced
FREE$83.99
Enroll
FreeCourse LogoFreeCourse

Freecourse.io brings you high-quality online courses with free certificates to help you upskill, boost your career, and achieve your goals anytime, anywhere.

Resources

  • Courses
  • Jobs
  • Categories
  • Features

Company

  • About
  • Blog
  • Contact

Legal

  • Privacy
  • Terms
  • Cookies
  • Licenses

© 2026 FreeCourse. All rights reserved.