Comprehensive SRE Interview Preparation Guide: FAANG Standards for Mid-Level Professionals
This guide is based on general FAANG interview practices and may not reflect specific company procedures.
FAANG companies typically conduct 7-8 interview rounds for mid-level SRE positions, beginning with recruiter screening and progressing through multiple technical rounds covering troubleshooting, system design, monitoring/observability, infrastructure automation, and behavioral assessment. The process emphasizes hands-on problem-solving, real-world incident scenarios, designing for reliability at scale, and demonstrating leadership qualities through collaboration and mentorship. Mid-level SREs are expected to own medium-to-large projects end-to-end, mentor junior engineers, and influence team technical decisions while showing growth potential toward senior/staff levels.
Interview Rounds
Recruiter Screen
What to Expect
Initial conversation with recruiter lasting 15-20 minutes. Focuses on verifying background, assessing cultural fit, understanding motivation for SRE role, confirming key qualifications, and answering logistics questions about interview process. This is not a technical assessment but a gatekeeping round to ensure candidate meets baseline requirements and has genuine interest in SRE work. Recruiter will have your resume and may ask about specific projects or experiences listed.
Tips & Advice
1) Be clear and concise about your SRE background and what attracted you to the role. 2) Prepare 1-2 minute stories about handling high-pressure incidents or automation wins. 3) Ask informed questions about team structure, on-call rotation, and incident culture showing you understand SRE responsibilities. 4) Mention specific SRE practices you're familiar with (SLOs, postmortems, error budgets) naturally in conversation. 5) Show enthusiasm for reliability engineering and systems thinking, not just cloud operations. 6) Confirm your availability for subsequent interview rounds and logistics (timezone, equipment for video calls).
Focus Topics
Key SRE Concepts Familiarity
Demonstrate basic familiarity with core SRE concepts: SLOs/SLIs/error budgets, incident management culture, automation mindset, and blameless postmortems. You don't need deep expertise yet, but should reference these naturally when discussing your experience.
Practice Interview
Study Questions
SRE Career Motivation & Background
Articulate why you're pursuing SRE, what appeals to you about reliability engineering versus pure DevOps or software engineering, and how your background (whether from DevOps, backend engineering, or infrastructure) has prepared you for this role. Be specific about projects or experiences that drove your interest in SRE practices.
Practice Interview
Study Questions
Specific Project Achievements
Prepare 2-3 concrete examples from your background: a major automation project you led, an incident you helped resolve, a monitoring system you improved, or reliability improvements you drove. For each, briefly describe the context, your role, the technical approach, and business impact (reduced outages, faster MTTR, team efficiency gains).
Practice Interview
Study Questions
Technical Screen: System Troubleshooting & Operational Challenges
What to Expect
90-minute technical interview conducted by a practicing SRE or senior engineer. Focuses on diagnosing and resolving real-world system issues through scenario-based questions and interactive troubleshooting. You'll be given realistic failure scenarios (application latency spike, pod crashes, deployment issues, database performance degradation) and asked to walk through your diagnostic approach, reasoning, and solutions. This round emphasizes practical operational thinking, knowledge of monitoring/observability tools, and systematic problem-solving methodology. Expect follow-up questions pushing you to consider edge cases, trade-offs, and prevention strategies.
Tips & Advice
1) When presented a scenario, verbalize your thinking process: what you'd check first, what tools you'd use, what logs/metrics matter most. Don't jump to conclusions. 2) Show familiarity with standard observability tools (Prometheus, Grafana, CloudWatch, DataDog, ELK stack) but focus on conceptual approach over tool syntax. 3) Ask clarifying questions: 'When did this start?', 'What changed recently?', 'Is this affecting all users or subset?'. 4) Discuss trade-offs: 'We could add more capacity now (cost) or investigate root cause (time)'. 5) Mention both immediate mitigation (restore service) and long-term prevention (prevent recurrence). 6) If you don't know an answer, say so and explain your reasoning for how you'd investigate. 7) Draw diagrams when helpful to show system architecture and data flow.
Focus Topics
Incident Response & Mitigation vs. Prevention
Distinguish between immediate mitigation (restore service, reduce blast radius) and root cause analysis (prevent recurrence). In scenarios, discuss both: 'We'd immediately roll back the bad deployment (fast MTTR) while investigating what caused the regression (long-term fix).' Understand incident severity levels and escalation paths.
Practice Interview
Study Questions
Kubernetes & Container Orchestration Troubleshooting
Understand Kubernetes architecture (master, nodes, pods, services), common failure modes (CrashLoopBackOff, ImagePullBackOff, OutOfMemory, resource limits), and debugging commands (kubectl logs, describe, get events, exec). Know when to suspect Kubernetes vs. application issues. Understand rolling updates and deployment strategies.
Practice Interview
Study Questions
Cloud Infrastructure Knowledge (AWS/GCP/Azure)
Have working knowledge of at least one major cloud platform: common services (EC2/Compute Engine, RDS/Cloud SQL, S3/Cloud Storage, Load Balancers, VPCs, networking), typical failure modes, and how to troubleshoot within that platform. Understand concepts like availability zones, regions, and cross-region failover.
Practice Interview
Study Questions
Systematic Troubleshooting Methodology
Approach incidents methodically: establish baseline/expected state, identify deviation, collect relevant data, form hypotheses, test hypotheses, implement fix, verify resolution, document findings. Internalize frameworks like the Scientific Method for incident diagnosis. Avoid jumping to conclusions based on incomplete data.
Practice Interview
Study Questions
Monitoring, Observability & Metrics Analysis
Understand how to read and interpret metrics (latency, error rate, throughput, resource utilization), identify anomalies, and trace root causes. Know concepts: RED method (Rate, Errors, Duration), USE method (Utilization, Saturation, Errors), distributed tracing, log aggregation. Be comfortable with at least one metrics platform (Prometheus) and log platform (ELK/Loki).
Practice Interview
Study Questions
System Design: Designing for Reliability
What to Expect
90-minute system design interview conducted by a senior SRE or architect. Unlike software engineering system design (focused on features), this round emphasizes designing systems for reliability, availability, and operational excellence. You'll be given a scenario (e.g., 'Design a notification system that must maintain 99.99% uptime', 'Design a monitoring infrastructure for a distributed system', 'Design a CI/CD pipeline for high-velocity deployments') and must propose architecture considering reliability patterns, failure modes, monitoring strategy, deployment approach, and operational complexity. You'll discuss trade-offs between consistency/availability, latency/reliability, cost/redundancy, and automation/manual operation. This round tests whether you think about systems as an SRE (not just an engineer) and can design for production realities.
Tips & Advice
1) Start by clarifying requirements and constraints: scale (QPS, data size), SLO targets (uptime/latency/error rates), cost constraints, geography. 2) Draw system architecture clearly showing components, data flow, and communication patterns. 3) Identify potential failure points and how you'd mitigate each (redundancy, failover, graceful degradation). 4) Discuss monitoring strategy: what metrics/logs you'd collect to detect problems. 5) Explain operational aspects: deployment process, rollback strategy, on-call runbooks, incident response procedures. 6) Consider tradeoffs explicitly: 'We could add a cache layer (lower latency, higher complexity) or optimize queries (simpler but slower)'。7) For mid-level, you don't need to design complex distributed consensus algorithms, but you should think operationally about real production constraints.
Focus Topics
Deployment & Operational Complexity Trade-offs
Discuss deployment strategies: blue-green, canary, rolling updates and their reliability implications. Consider operational complexity vs. capability: 'Simple deployment (easy to operate, fast recovery) vs. complex multi-region active-active (high availability, hard to troubleshoot).' Understand cost vs. reliability trade-offs and when simpler is better.
Practice Interview
Study Questions
Monitoring, Observability & Alerting Architecture
Design comprehensive monitoring and observability: what metrics you'd collect (application, infrastructure, business), where you'd store them, how you'd query them for troubleshooting, and what alerts you'd set. Discuss cost/benefit of different telemetry collection strategies. Mention distributed tracing for understanding request paths in complex systems.
Practice Interview
Study Questions
Reliability Patterns & High Availability Design
Understand and apply reliability patterns: redundancy (replicas across zones/regions), failover mechanisms, circuit breakers, bulkheads, graceful degradation, timeouts, retries with exponential backoff. Know when to use each pattern and tradeoffs. Understand concepts like RPO (Recovery Point Objective) and RTO (Recovery Time Objective).
Practice Interview
Study Questions
SLO-Driven Design & Error Budgets
Design systems with specific reliability targets in mind (e.g., 99.9% availability = 43 minutes downtime/month). Understand error budgets and how they inform feature velocity vs. stability trade-offs. Discuss how you'd monitor against SLOs and what alerts matter (exhausting error budget, approaching SLO boundary).
Practice Interview
Study Questions
Monitoring, Observability & Incident Response Deep Dive
What to Expect
75-minute technical interview focusing on observability practices, monitoring architecture, and incident response procedures. You'll discuss how to design monitoring systems, alert effectively (avoiding false positives and alert fatigue), use observability for rapid diagnosis, and structure incident response. May include concrete scenarios like 'Design alerting for a database performance SLO', 'How would you investigate this latency spike?', or 'Walk through your incident response playbook for a critical outage.' This round tests both technical knowledge (metrics, logging, tracing tools) and SRE practices (runbooks, playbooks, blameless postmortems, on-call culture).
Tips & Advice
1) Understand the difference between monitoring (measuring service health against SLO) and observability (ability to understand system behavior through external outputs). Discuss both. 2) When discussing alerts, emphasize signal-to-noise ratio: too many false alerts = alert fatigue = ignored alerts. Focus on alerts that indicate user impact or risk of user impact. 3) Be familiar with at least one monitoring stack (Prometheus + Grafana, Datadog, New Relic, CloudWatch). Know basic queries and dashboard design principles. 4) Discuss logging strategy: what to log, log levels, log aggregation, structured logging benefits. 5) Understand distributed tracing concepts and tools (Jaeger, Zipkin) for understanding requests across services. 6) Have concrete incident response procedures ready: escalation paths, war room structure, communication during incident, blameless postmortem process.
Focus Topics
On-Call Culture & Runbook Development
Understand on-call responsibilities: on-call engineer is responsible for incident response for their services. Discuss runbooks and playbooks: step-by-step procedures for common incidents allowing quick diagnosis and mitigation. Know how to structure on-call rotations, define escalation paths, and support on-call engineers with good runbooks and documentation.
Practice Interview
Study Questions
Monitoring Tools & Stack Familiarity
Have hands-on familiarity with at least one modern monitoring platform (Prometheus, Grafana, Datadog, CloudWatch, etc.). Understand how to write queries, design dashboards, set up alerts, and integrate with incident management (PagerDuty, Opsgenie). Know basics of time-series databases and query languages.
Practice Interview
Study Questions
Observability Best Practices: Metrics, Logs, Traces
Understand the three pillars of observability: metrics (quantitative measurements over time), logs (event-level details), and traces (request flow through distributed system). Know when to use each and how they complement each other. Discuss cardinality issues, cost of high-cardinality metrics, and structured logging benefits. Understand sampling strategies for high-volume data.
Practice Interview
Study Questions
Alert Design & Alert Fatigue Prevention
Understand alert design principles: alert on symptoms (user-facing impact or risk of impact), not symptoms of symptoms. Use composite alerting and correlation to reduce noise. Discuss alert severity levels and routing (which team gets paged). Know concept of error budgets and alerting when budget is exhausted. Practice designing alerts that page only when action is needed.
Practice Interview
Study Questions
Incident Management & Postmortem Process
Understand incident lifecycle: detection (alerts), triage (severity assessment), mitigation (restore service), diagnosis (root cause analysis), remediation (prevent recurrence). Know your company's incident severity levels and escalation procedures. Discuss blameless postmortem culture: goal is learning, not blame. Understand how to drive follow-ups and track improvements.
Practice Interview
Study Questions
Infrastructure Automation & Deployment Reliability
What to Expect
75-minute technical interview on infrastructure automation, infrastructure-as-code, deployment pipelines, and preventing configuration drift. You'll discuss how to automate infrastructure provisioning and changes, design CI/CD pipelines for reliability, manage configuration at scale, and ensure reproducible deployments. Scenarios might include 'Design a CI/CD pipeline for deploying services to Kubernetes', 'How do you manage infrastructure changes across 100s of servers?', or 'How would you implement canary deployments?' This round tests your understanding of modern deployment practices, automation frameworks, and operational complexity of running code at scale.
Tips & Advice
1) Emphasize Infrastructure-as-Code (IaC) principles: infrastructure defined in version control, reproducible, auditable, testable. Discuss tools like Terraform, CloudFormation, Ansible. 2) Design CI/CD for safety: automated testing (unit, integration, security scans), staged rollout (canary), rollback capabilities, artifact versioning. 3) Discuss deployment strategies and their reliability implications: rolling updates (zero downtime, harder to debug), blue-green (easy rollback, requires double capacity), canary (safe for large changes). 4) Address operational concerns: how to handle failed deployments, rollback procedures, managing database migrations with code changes. 5) Know how to balance automation with human validation: what should be automatic vs. require approval. 6) For mid-level, you should have hands-on experience with at least one IaC tool and basic CI/CD concepts.
Focus Topics
Deployment Strategies & Rolling Updates
Understand deployment strategies: rolling updates (gradual replacement, zero downtime but harder to debug), blue-green (easy rollback but requires extra capacity), canary (small traffic to new version, detect issues early), and feature flags. Know when to use each and trade-offs. Understand how Kubernetes handles rolling updates natively.
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Study Questions
Automation Frameworks & Operational Scripting
Be comfortable with basic scripting (bash, Python) for operational tasks: log analysis, system monitoring, routine maintenance. Understand when to automate vs. leave manual (consider frequency, risk, and time required). Use configuration management to enforce standards across fleet. Know how to handle failures in automated systems.
Practice Interview
Study Questions
Deployment Risk Management & Rollback Strategy
Discuss strategies for managing deployment risk: canary deployments (detect issues in subset), feature flags (quick disable without rollback), smoke testing post-deployment. Understand rollback procedures: full rollback (restore previous version), partial rollback (revert specific services). Know how to handle complications like database schema changes that can't simply rollback.
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Study Questions
CI/CD Pipeline Design for Reliability
Design CI/CD pipelines that catch problems early: automated testing (unit, integration, end-to-end), security scanning, code quality checks, artifact management. Discuss how to structure pipelines for different change types (configuration vs. code vs. infrastructure). Understand staged deployments: dev -> staging -> prod, with appropriate gates at each stage.
Practice Interview
Study Questions
Infrastructure-as-Code & Configuration Management
Understand IaC principles and tools (Terraform, CloudFormation, Ansible, etc.). Design reproducible, version-controlled infrastructure. Discuss state management (how to track what infrastructure exists), idempotency (applying same configuration multiple times produces same result), and preventing configuration drift. Know how to test infrastructure changes before deploying.
Practice Interview
Study Questions
Leadership, Collaboration & Behavioral Assessment
What to Expect
60-minute behavioral interview with a manager or senior engineer assessing leadership potential, collaboration style, communication skills, and alignment with company values. For mid-level SREs, this evaluates your ability to own projects end-to-end, mentor junior colleagues, influence decisions through technical credibility, and handle conflict/disagreement constructively. You'll discuss challenging situations: difficult technical decisions with trade-offs, situations where you had to say 'no', times you influenced skeptical teammates, examples of mentoring juniors, and how you handle on-call stress. This round assesses cultural fit and growth trajectory toward senior/leadership roles. Expect FAANG-specific leadership principles (Amazon: Ownership, Customer Focus; Google: Collaboration, Technical Depth; Microsoft: Growth Mindset, Diversity).
Tips & Advice
1) Research and internalize your target company's leadership principles or cultural values. Use them as framework for structuring answers. 2) Use STAR method: describe Situation, Task you were responsible for, Action you took, Result/Outcome. Be specific with numbers and impact. 3) Prepare stories demonstrating: ownership (taking initiative, following through), collaboration (working across teams, listening to others), mentorship (helping junior grow), influence (driving change through credibility not authority), decision-making (trade-offs, stakeholder considerations). 4) Discuss a situation where you had to admit mistake and what you learned. 5) Have examples of resilience: on-call incident that went wrong, how you recovered, what you improved. 6) Show self-awareness: acknowledge areas for growth, describe concrete improvements you're making.
Focus Topics
Communication & Stakeholder Management
Demonstrate ability to communicate technical topics to different audiences: explain complex infrastructure issue to non-technical stakeholder, present incident timeline to executive leadership, collaborate with skeptical teammates, write clear runbooks for on-call engineers. Show how you balance transparency (sharing bad news early) with confidence.
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Study Questions
Mentorship & Developing Others
At mid-level, you should be mentoring more junior engineers. Describe specific examples: junior you helped grow, what you taught them, how they've progressed. Discuss your mentorship philosophy: how you balance pushing growth with support, how you handle different learning styles, how you make time for mentoring while managing your own work.
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Study Questions
Handling Failure, Resilience & Learning
Discuss a significant failure or incident you were involved in: what went wrong, your role, how you responded, what you learned, what you improved afterward. Demonstrate blameless thinking and focus on systemic improvement not blame. Discuss how you handle on-call stress, pressure, and maintain learning mindset even when things go wrong.
Practice Interview
Study Questions
Technical Ownership & Project Execution
Demonstrate ability to own projects end-to-end: identifying problem, proposing solution, rallying support, implementing through to completion, and measuring impact. Discuss how you break down large problems, manage timeline, handle blockers, and communicate progress. At mid-level, you should have multiple examples of meaningful projects you led where you drove decisions.
Practice Interview
Study Questions
Collaboration & Cross-Functional Influence
Show how you work effectively with other teams (backend engineers, platform teams, security, operations). Discuss situations where you had to influence without authority, build consensus on technical decisions, and handle disagreement productively. Describe your communication style and how you tailor it to different audiences (executives, technical peers, operational teams).
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Study Questions
Hiring Manager Round
What to Expect
30-45 minute final assessment with hiring manager or senior leadership. This is less formal technical deep-dive and more comprehensive evaluation of fit: overall impression of your capabilities, alignment with team needs, growth potential, and cultural fit. Hiring manager will likely summarize their initial impressions and ask clarifying questions about your background, career goals, and how you see yourself contributing to their team. This is also your opportunity to ask substantive questions about team dynamics, technical challenges, on-call culture, and career growth opportunities. This round is mutual evaluation: company assessing you + you assessing company.
Tips & Advice
1) Research the team and its current challenges before the meeting. Reference specific technical initiatives or team needs you learned about. 2) Ask thoughtful questions showing you've done homework: 'What are the biggest reliability challenges your team faces?', 'Tell me about your on-call culture and how you support it?', 'What does success look like for this role in first 6 months?' 3) Discuss where you see yourself in 2-3 years and how this role fits that trajectory. 4) Be authentic: this is your chance to assess if you actually want to work here and if the team environment suits you. 5) Mention specific technical areas you want to develop (distributed systems, chaos engineering, whatever resonates) and ask how this role supports that growth. 6) Clarify logistics: start date, compensation expectations (if not discussed), team structure, and next steps.
Focus Topics
Technical Growth Opportunities & Learning Interests
Identify areas where you want to deepen expertise (Kubernetes, distributed systems, machine learning for anomaly detection, advanced cloud architecture) and ask how this role and team supports that growth. Discuss books you're reading, blogs you follow, topics you're excited about. Show intellectual curiosity.
Practice Interview
Study Questions
Team Dynamics & On-Call Culture Understanding
Ask specific questions about team structure, how on-call is run, what incidents are typical, how the team supports engineers under stress, and what success looks like for new hires. Show understanding that on-call sustainability and psychological safety are important SRE concerns.
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Study Questions
Career Goals & Role Fit Assessment
Articulate your career trajectory: where you've been, where you want to go, and how this specific role fits that plan. Discuss what appeals to you about this particular team and company. Show thoughtfulness about how SRE aligns with your technical interests and long-term career aspirations.
Practice Interview
Study Questions
Frequently Asked Site Reliability Engineer (SRE) Interview Questions
Sample Answer
Sample Answer
import time
from collections import defaultdict, OrderedDict, deque
# Parameters
MAX_INCIDENTS = 50000
GROUP_WINDOW = 5.0 # seconds grouping latency
HOLD_WINDOW = 300.0 # keep incidents for 5 minutes for late events
# Data structures
incidents = {} # incident_id -> {key, first_ts, last_ts, alerts_count, members}
key_index = {} # key -> incident_id
lru = OrderedDict() # incident_id -> last_update_ts for eviction
time_buckets = defaultdict(set) # minute -> set(incident_id) for coarse eviction
def normalize_key(alert):
# deterministic key combining service, signal, sorted tag pairs
tags = tuple(sorted(alert.get('tags', {}).items()))
return f"{alert['service']}|{alert['signal']}|{tags}"
def process(alert):
now = alert.get('timestamp', time.time())
key = normalize_key(alert)
# Approximate deduplication: keep only one alert per id and per (key, rounded_ts)
rounded = int(now) # 1-second granularity
dedup_token = (alert['id'], key, rounded)
# simple in-memory dedup store bounded by time using LRU via lru_dedup
if dedup_seen(dedup_token):
return
# assign to existing incident if key exists and last_ts within GROUP_WINDOW
iid = key_index.get(key)
if iid and (now - incidents[iid]['last_ts']) <= GROUP_WINDOW:
upd_incident(iid, alert, now)
else:
iid = create_incident(key, alert, now)
# eviction if over capacity
if len(incidents) > MAX_INCIDENTS:
evict()
def dedup_seen(token):
# simplified bounded dedup using a deque + set with time-based expiry
# implementation omitted: assume returns True if duplicate recently seen
pass
def create_incident(key, alert, now):
iid = f"inc-{int(now*1000)}-{len(incidents)%1000}"
incidents[iid] = {'key': key, 'first_ts': now, 'last_ts': now,
'alerts_count': 1, 'members': [alert]}
key_index[key] = iid
lru[iid] = now
time_buckets[int(now//60)].add(iid)
return iid
def upd_incident(iid, alert, now):
inc = incidents[iid]
inc['last_ts'] = now
inc['alerts_count'] += 1
inc['members'].append(alert)
lru.move_to_end(iid)
lru[iid] = now
def evict():
# evict oldest by last_update_ts until under limit; also remove stale (>HOLD_WINDOW)
cutoff = time.time() - HOLD_WINDOW
# first remove stale
for iid, inc in list(incidents.items()):
if inc['last_ts'] < cutoff:
remove_incident(iid)
# if still over capacity, pop LRU oldest
while len(incidents) > MAX_INCIDENTS:
iid, _ = lru.popitem(last=False)
remove_incident(iid)
def remove_incident(iid):
inc = incidents.pop(iid, None)
if not inc: return
key_index.pop(inc['key'], None)
lru.pop(iid, None)
time_buckets[int(inc['last_ts']//60)].discard(iid)
# Handling late-arriving events:
# If a late event arrives and maps to an incident still retained (within HOLD_WINDOW), attach and update incident.
# If incident evicted, treat as new incident or send to backend for reconciliation.Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
from typing import List
import math
import numpy as np
import collections
def detect_anomalies(series: List[float], window_size: int, z_threshold: float) -> List[int]:
"""
Returns indices of points considered anomalous based on z-score relative to previous window_size points.
- series: list of floats (may contain math.nan)
- window_size: number of previous points to use (exclude current)
- z_threshold: threshold for |z-score|
Behavior:
- For i < 1: no anomaly (need at least one prior)
- For initial indices with fewer than window_size valid prior values, use available valid points
- Points that are NaN are ignored (not flagged), and do not contribute as anomalies
"""
anomalies = []
window = collections.deque() # store valid numeric values
# maintain running sum and sumsq for numeric stability
s = 0.0
ss = 0.0
for i, x in enumerate(series):
# compute stats from previous values in window
if len(window) >= 1:
n = len(window)
mean = s / n
var = (ss / n) - mean * mean
std = math.sqrt(var) if var > 0 else 0.0
if not math.isnan(x):
if std > 0:
z = (x - mean) / std
if abs(z) > z_threshold:
anomalies.append(i)
# if std == 0: all prior values equal -> treat only if x != mean?
else:
if x != mean:
anomalies.append(i)
# update window with current value for future points
if not math.isnan(x):
window.append(x)
s += x
ss += x * x
# keep window size <= window_size
if len(window) > window_size:
old = window.popleft()
s -= old
ss -= old * old
return anomaliesSample Answer
Sample Answer
Recommended Additional Resources
- Site Reliability Engineering books: 'SRE: Google's approach to production engineering', 'The Site Reliability Workbook', 'Observability Engineering' by O'Reilly
- System design resources: 'Designing Data-Intensive Applications' by Martin Kleppmann, System Design Primer GitHub repo, Alex Xu's system design interview book
- Incident management: Google's Postmortem Culture document, Blameless.io incident postmortem best practices
- Cloud platforms: AWS SRE courses, Google Cloud Platform reliability engineering guides, Azure Well-Architected Framework
- Tools & monitoring: Prometheus documentation and tutorial videos, Grafana dashboarding guides, Kubernetes official documentation
- Interview practice: LeetCode for coding fundamentals (if your company emphasizes coding), Exponent.dev for system design mock interviews, Pramp for peer mock interviews
- SRE practices: The SRE Weekly newsletter, Observability engineering blogs, ChatOps and incident management best practices documentation
- Company-specific: Research your target company's engineering blog, incident postmortems they've published, and their specific technology stack
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