Meta Staff Site Reliability Engineer Interview Preparation Guide
While search results confirm that Meta (Facebook) conducts SRE interviews, comprehensive company-specific interview process details were not available in the search results. This guide is based on industry-standard practices for Staff-level SRE positions at leading tech companies, adapted to Meta's technology stack and known requirements. The interview structure, round types, and evaluation criteria reflect typical patterns for Staff-level technical interviews in the SRE domain. For the most current and detailed information about Meta's specific interview process, candidates should consult Meta's official careers page.
Meta's Staff Site Reliability Engineer interview process is a rigorous, multi-round evaluation designed to assess technical depth, systems thinking, incident response capability, and leadership potential. The process combines technical depth assessment through systems design and distributed systems interviews, infrastructure expertise evaluation through practical scenarios, and behavioral evaluation focused on cross-functional impact and mentorship. Staff-level candidates are evaluated on their ability to architect large-scale reliable systems, lead technical initiatives across teams, and mentor senior engineers while demonstrating Meta values of moving fast and building impact.
Interview Rounds
Recruiter Screening
What to Expect
The initial conversation with Meta's recruiting team to validate your background, assess role fit, and determine if your experience aligns with the Staff-level SRE position. This combines initial phone screening and recruiter follow-up into a single call. The recruiter will discuss your career trajectory, understanding of the SRE discipline, and motivation for joining Meta. This round covers logistics, compensation expectations, and timeline. For Staff-level candidates, expect deeper questions about your impact in previous roles, your approach to scaling teams and systems, and your vision for reliability engineering.
Tips & Advice
Be prepared to articulate your career story with emphasis on the progression to Staff level and the key technical and leadership milestones. Clearly explain what excites you about the SRE role at Meta and how your background specifically prepares you for this position. Have specific examples ready showing how you've improved reliability at scale, led infrastructure initiatives, and mentored engineers. Ask thoughtful questions about Meta's SRE organization, their current reliability challenges, and how this role contributes to Meta's engineering organization. Be honest about compensation expectations but avoid anchoring too early. Show enthusiasm for Meta's mission and technical challenges.
Focus Topics
Motivation & Fit for Meta
Clearly articulate why you're interested in Meta specifically and why this Staff SRE role aligns with your career goals. Research Meta's scale, their infrastructure challenges, and where your skills can create impact. Mention specific aspects of Meta's technology or mission that attract you.
Practice Interview
Study Questions
Understanding of SRE Discipline & Philosophy
Demonstrate understanding of SRE as a discipline—the philosophy of treating operations as an engineering problem, the importance of reliability versus feature velocity balance, error budgets, and the SRE toolkit. Show familiarity with industry concepts like SLI/SLO/SLA, toil automation, incident response, and postmortems.
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Study Questions
Mentorship & Team Development Approach
Discuss your approach to developing engineers—how you mentor senior engineers, develop talent, create learning opportunities, and contribute to team culture. For Staff level, this is critical to the role.
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Examples of Technical Leadership & Impact
Prepare 3-4 concrete examples of times you've made significant technical decisions at scale, led cross-functional initiatives, influenced architecture direction, or improved reliability metrics substantially. For Staff level, focus on examples that affected multiple teams or had organization-wide impact.
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Study Questions
Career Narrative & Staff-Level Progression
Articulate your career journey emphasizing how you reached Staff level expertise in SRE. Highlight key inflection points where your impact scaled beyond individual contributions to team and organizational level. Prepare to discuss the progression from IC to senior IC roles, growth in technical depth, and increasing scope of responsibility.
Practice Interview
Study Questions
Technical Phone Screen 1: Infrastructure & Systems Knowledge
What to Expect
A focused technical discussion with a Meta SRE engineer exploring your deep knowledge of large-scale infrastructure, systems architecture, and reliability practices. This 50-60 minute interview assesses your ability to think about complex infrastructure problems, design scalable systems, and make informed decisions about reliability trade-offs. The interviewer will present scenarios related to Meta's scale and ask you to discuss architectural choices, tradeoffs, and implementation considerations. Expect questions covering distributed systems concepts, infrastructure automation, monitoring, and capacity planning—all grounded in real-world challenges at Meta's scale.
Tips & Advice
Think out loud and explain your reasoning. For infrastructure problems, start by clarifying requirements and constraints. Discuss tradeoffs explicitly—reliability versus cost, consistency versus availability, automation complexity versus manual effort. Draw diagrams if helpful using a whiteboard or shared document. Show familiarity with monitoring and observability—discuss how you'd measure whether your solution works. Be specific about tools and technologies you'd use, such as Kubernetes, service mesh, and monitoring tools. Don't just describe what you would do; explain why. If you're unsure about something, say so and explain how you'd approach learning it. Show that you understand Meta's scale challenges—billions of users, real-time requirements, global infrastructure.
Focus Topics
Reliability Engineering Principles & SLOs
Deep understanding of SRE philosophy—error budgets, the tension between reliability and velocity, SLI/SLO/SLA definitions, and using these concepts to make engineering decisions. How to instrument systems to track reliability, how to use error budget to guide prioritization.
Practice Interview
Study Questions
Capacity Planning & Scalability
Predicting infrastructure needs based on growth, designing systems to scale linearly or better, understanding resource constraints, and planning expansions. Discuss how to forecast demand, identify bottlenecks, plan database scaling, network scaling, etc. Understanding growth metrics and their implications.
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Study Questions
Large-Scale System Architecture Design
Design systems that operate at Meta scale—billions of daily active users, global infrastructure, real-time requirements. Discuss multi-region architecture, failover strategies, consistency models under failure conditions, and architectural patterns for resilience. For Staff level, go deeper into architecture decisions, tradeoffs between consistency and availability, geographic distribution, and handling partial failures.
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Study Questions
Infrastructure Automation Frameworks & Tooling
Deep understanding of infrastructure automation—infrastructure as code using tools like Terraform or CloudFormation, configuration management, deployment automation, and rollback strategies. Discuss how to automate infrastructure changes safely, versioning approaches, testing automation, and handling state.
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Study Questions
Performance Monitoring & Observability Design
Designing comprehensive monitoring systems for large-scale distributed systems. Understanding metrics, logs, traces, and their tradeoffs. SLI/SLO instrumentation, alerting strategies that minimize false positives, and designing dashboards for different audiences. Knowledge of observability tools and how to architect monitoring for reliability.
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Study Questions
Technical Phone Screen 2: Incident Response & Troubleshooting
What to Expect
A technical interview focused on incident response, troubleshooting methodology, root cause analysis, and operational decision-making under pressure. The interviewer will present realistic incident scenarios from Meta's scale and ask how you'd respond—diagnostics, decision-making, communication, and resolution. This assesses your ability to remain calm under pressure, apply systematic troubleshooting, identify root causes, and drive incidents to resolution. For a Staff-level candidate, focus is also on how you'd lead response, mentor others during incidents, and conduct effective post-mortems.
Tips & Advice
Treat this like a real incident—ask clarifying questions about what's happening, what symptoms you're seeing, and what services/systems are affected. Work methodically through diagnosis rather than jumping to conclusions. Explain your thought process as you investigate. For each hypothesis, explain how you'd test it and what tools you'd use. Show systematic debugging approaches—understand logs, metrics, traces, customer impact. Don't fixate on one theory; generate and test multiple hypotheses. When you identify the root cause, discuss how to fix it quickly while minimizing risk. Think about rollback options and communication during incidents. For Staff level, discuss how you'd coordinate with other teams, communicate with leadership, and structure the response. Talk about post-incident review—what lessons would you capture, how would you prevent recurrence.
Focus Topics
Post-Incident Review & Learning Process
Designing effective post-incident reviews that extract lessons without blame. Identifying systemic issues versus one-time failures. Creating actionable follow-up work. Understanding how to use incidents as learning opportunities for the team.
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Study Questions
Communication & Coordination During Incidents
Communicating clearly during crisis situations—keeping stakeholders informed, avoiding false updates, coordinating across teams, managing customer communication. For Staff level, this includes leading the response, delegating work, and escalating appropriately.
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Decision Making & Trade-offs Under Pressure
Making sound technical decisions during incidents when under time pressure and with incomplete information. Balancing speed of recovery versus risk of making things worse. Knowing when to rollback versus rollforward, when to apply bandaids versus permanent fixes, and when to escalate.
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Incident Triage & Root Cause Analysis
Systematically diagnosing incidents at scale. Understanding how to triage severity, identify which systems are affected, correlate symptoms, and work toward root cause. Creating effective hypotheses and testing them methodically. Using logs, metrics, traces, and other signals to build understanding.
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Troubleshooting Methodology for Distributed Systems
Applying structured troubleshooting approaches to distributed systems where issues can be subtle and hard to reproduce. Understanding cascading failures, partial failures, and how to isolate problems. Knowledge of debugging tools and how to use them systematically.
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Study Questions
Onsite Round 1: Systems Design Interview
What to Expect
An in-depth systems design interview where you architect a large-scale system from scratch. You'll be given a system design problem likely related to Meta's infrastructure or services and asked to design a complete system considering reliability, scalability, performance, monitoring, and operational aspects. The interviewer will probe your design decisions, ask about tradeoffs, and explore how you'd handle edge cases, failures, and operational requirements. For a Staff-level candidate, expect higher bar—deeper discussion of reliability patterns, failure scenarios, operational automation, and how this system would be monitored and maintained at scale.
Tips & Advice
Start by clarifying requirements and constraints—scale, latency requirements, consistency requirements, failure modes you need to handle. Think about both the happy path and failure scenarios. Discuss reliability—what happens when components fail, how do you detect failures, how do you recover. Design monitoring and alerting as part of the system, not an afterthought. Think about operational concerns—how would you deploy this, how would you scale it, how would you debug issues. Use diagrams liberally. For each component, discuss tradeoffs—consistency versus availability, strong versus eventual consistency, synchronous versus asynchronous, etc. Discuss data modeling and storage considerations. Show familiarity with patterns used in large-scale systems—load balancing, caching, replication, sharding. For Staff level, go deeper—discuss how the team would be organized, how you'd approach operational automation, how you'd handle geographic distribution, etc.
Focus Topics
SLO-Driven Design & Error Budgets
Designing systems with explicit SLOs and using error budgets to guide reliability investment. Understanding how to instrument systems to track SLIs, how to use error budget to prioritize work, and how design decisions impact achievable SLOs.
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Study Questions
Observability & Monitoring Architecture
Designing comprehensive observability into the system—metrics that track business and technical health, logs that help with debugging, traces that help understand distributed flow. SLI instrumentation, alerting strategy.
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Multi-region & Geographic Architecture
Designing systems across geographic regions—handling latency, consistency across regions, failover strategies, and traffic routing. Understanding tradeoffs between replication and network costs.
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Operational Automation & Deployment Strategy
Designing systems to be operationally simple—automation of common operational tasks, safe deployment strategies such as canary, rolling, or blue-green, rollback capabilities, and monitoring for operational health.
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Distributed System Trade-offs & Consistency Models
Understanding fundamental tradeoffs in distributed systems—strong versus eventual consistency, availability versus consistency (CAP theorem), synchronous versus asynchronous communication. When to use each pattern and implications for operations.
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Design for Reliability & Failure Handling
Designing systems that continue operating under partial failures. Understanding circuit breakers, bulkheads, graceful degradation, and failure modes. Designing for geographic redundancy, data replication strategies, and consistency models that handle failures. How to design systems so that individual component failures don't cascade.
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Onsite Round 2: Distributed Systems & Architecture
What to Expect
A deep-dive technical interview focusing on distributed systems concepts, algorithms, and architecture patterns used in large-scale systems. The interviewer will discuss specific distributed systems challenges—consensus, replication, failure detection, data consistency, load balancing—and explore your understanding at depth. Expect questions about specific algorithms (Raft, Paxos concepts, etc.), when to use them, tradeoffs, and failure scenarios. This round assesses theoretical understanding combined with practical application to real systems.
Tips & Advice
Show deep understanding of distributed systems principles. Be familiar with consensus algorithms, replication strategies, and consistency models—understand the concepts even if you can't write code for them. Discuss specific systems you've worked with—how they handle certain challenges, what tradeoffs they make. When discussing algorithms, explain the intuition and tradeoffs, not just mechanical details. Relate concepts to real operational scenarios—what happens when you lose a majority, what happens during network partitions, how does this impact operations. Be precise with terminology—understand the difference between strong consistency, eventual consistency, causal consistency, etc. Show awareness of practical concerns—latency implications, failure impact, recovery time. For Staff level, discuss how you'd design systems using these concepts, mentor others on these principles, and make architecture decisions based on deep understanding.
Focus Topics
Load Balancing & Traffic Distribution Strategies
Understanding different load balancing approaches, consistent hashing, request routing, handling of uneven load, and stateful load balancing challenges. Tradeoffs between different strategies.
Practice Interview
Study Questions
Network Reliability & Timeout Handling
Understanding network failure modes—packet loss, latency, partitions. How systems should handle timeouts, retries, and idempotency. Understanding failure detection challenges caused by network complexity.
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Failure Detection & Recovery Mechanisms
Understanding how systems detect failures using health checks, heartbeats, etc., challenges in detection such as false positives and slow detection, recovery mechanisms, and how to design systems robust to different failure scenarios.
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Data Consistency Models & Guarantees
Understanding different consistency models—strong consistency, eventual consistency, causal consistency, etc. Understanding what each guarantees and implies for application behavior and operational complexity. Understanding how to maintain consistency under failures.
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Consensus & Replication Algorithms
Understanding consensus algorithms and their role in distributed systems. Knowledge of algorithm families such as Paxos and Raft, their properties, failure modes, and operational implications. Understanding quorum systems, split-brain scenarios, and recovery from failures.
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Study Questions
Onsite Round 3: Coding Interview (Systems-Focused)
What to Expect
A coding interview focused on systems programming and infrastructure-related coding challenges. You'll solve 1-2 coding problems, likely involving systems concepts like thread safety, resource management, or performance optimization. Problems may relate to building infrastructure components, optimization challenges, or distributed systems algorithms. For a Staff-level SRE, the bar is higher—clean, optimized code with good design. You should be able to discuss efficiency implications, tradeoffs, and how this code would behave in production.
Tips & Advice
Even at Staff level, coding interviews expect clean, working code. But the bar is higher for design and optimization. Think about edge cases and how your code would behave at scale. Consider memory usage, CPU efficiency, and how code would perform under load. Use appropriate data structures and algorithms. For systems-focused problems, think about concurrency, resource limits, and operational aspects. Be ready to optimize based on feedback. Discuss tradeoffs—is correctness more important than performance, or vice versa? How would you test this code? Write code that others on the team would want to maintain.
Focus Topics
Resource Management & Error Handling
Writing code that manages resources correctly—memory, files, connections, etc. Proper cleanup and error handling. Code that doesn't leak resources or crash ungracefully.
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Study Questions
Infrastructure as Code & Automation Scripts
Writing code for infrastructure automation—scripts for deployment, monitoring, or operational tasks. Clean, maintainable code that others can understand and modify. Proper error handling and logging.
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Performance-Critical Code & Optimization
Writing and optimizing code for performance—understanding algorithmic complexity, data structure choices, memory efficiency, cache efficiency. Profiling and identifying bottlenecks. Code that performs well at scale.
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Systems Programming & Concurrency
Writing code that handles concurrency correctly—thread safety, synchronization primitives, avoiding deadlocks and race conditions. Understanding performance implications of different concurrency approaches. Coding problems may involve multi-threaded systems or concurrent processing.
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Onsite Round 4: Infrastructure Automation & Tooling
What to Expect
A technical interview focused on infrastructure automation, tooling, and operational practices. The interviewer will discuss how you design and implement infrastructure automation—tools and frameworks you'd use, how you'd structure automation for maintainability and safety, how you'd approach rolling out infrastructure changes, and how you'd handle operational tooling needs. This may include discussion of container orchestration like Kubernetes, CI/CD systems, configuration management, and monitoring tools. You may be asked to architect an automation solution for a specific operational challenge.
Tips & Advice
Discuss automation in concrete terms—which tools would you use and why? How would you structure the automation to be maintainable? Show familiarity with modern infrastructure tools. For Kubernetes, understand the concepts beyond just deployment—how would you handle upgrades, what about monitoring, how would you handle failures. Discuss how you'd make infrastructure changes safely—testing, gradual rollout, rollback capability. Show that you think about operational concerns—visibility into what's running, debugging when things go wrong, capacity planning. For Staff level, discuss architecture decisions—how you'd organize automation, how you'd scale it, how you'd make it work across teams. Show that you understand tradeoffs—flexibility versus simplicity, automated versus manual, etc.
Focus Topics
Monitoring, Alerting & Observability Tooling
Designing and implementing comprehensive monitoring and alerting—metrics collection, log aggregation, distributed tracing. Understanding observability tools and platforms. How to design monitoring that's actually useful for operations.
Practice Interview
Study Questions
Infrastructure as Code & Configuration Management
Treating infrastructure as code—versioning infrastructure, reviewing changes, testing infrastructure changes, documenting infrastructure. Using IaC tools effectively, handling secrets, managing drift.
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CI/CD Pipeline Design & Implementation
Designing comprehensive CI/CD systems—build automation, testing, deployment automation, rollback capabilities. Understanding different deployment strategies such as canary, rolling, or blue-green, how to make deployments safe, and how to handle failure scenarios.
Practice Interview
Study Questions
Infrastructure Automation Framework & Tooling
Understanding and designing infrastructure automation systems using tools like Terraform, Ansible, or custom solutions. How to make automation maintainable, testable, and safe. How to handle state management, versioning, and rollback in infrastructure automation.
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Study Questions
Container Orchestration & Kubernetes Architecture
Deep understanding of Kubernetes architecture, deployment strategies, resource management, networking, storage, and operational practices. Understanding how to design reliable Kubernetes deployments, handle node failures, perform upgrades safely. Understanding security and isolation in containerized environments.
Practice Interview
Study Questions
Onsite Round 5: Behavioral & Leadership Interview
What to Expect
A behavioral interview assessing cultural fit, values alignment, and leadership capability. The interviewer will ask about your experience with cross-functional collaboration, how you lead technical initiatives, how you handle ambiguity and conflict, and how you've grown as a leader. At Staff level, focus is on your ability to influence across teams, mentor senior engineers, communicate with stakeholders at different levels, and drive complex technical decisions to completion. You'll be asked about specific situations you've handled and how they demonstrate Meta values—moving fast, being intellectually honest, building impact, etc.
Tips & Advice
Use the STAR method for behavioral questions—Situation, Task, Action, Result. Focus on your role and impact specifically, not just team achievements. Show how you handle ambiguity—when you don't have all information, how do you proceed? Demonstrate learning and growth by sharing examples of challenges you've overcome. Show emotional intelligence—how you handle disagreement, support teammates, navigate organizational dynamics. For Staff level, emphasize leadership—how you've influenced decisions, driven changes across teams, developed junior engineers. Share examples of times you've made unpopular but necessary technical decisions. Show how you balance speed and quality, individual contribution and team development. Discuss how you've handled failure and what you learned. Align your stories with Meta values—moving fast safely, being direct and honest, building things that matter.
Focus Topics
Technical Decision Making & Ownership
How you approach complex technical decisions—gathering data, involving stakeholders, making decisions with incomplete information. Examples of decisions you've made and owned, including decisions that were later changed (showing humility). How you think about tradeoffs.
Practice Interview
Study Questions
Meta Values Alignment (Move Fast, Honesty, Impact, etc.)
Demonstrating alignment with Meta values—examples of moving fast while maintaining quality, being intellectually honest even when it's unpopular, building things that create real impact. Showing how you'd fit into Meta culture.
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Communication & Stakeholder Management
How you communicate complex technical concepts to different audiences—engineers, managers, executives. How you handle misalignment and different perspectives. Examples of times you've communicated through crisis or ambiguity.
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Cross-Functional Leadership & Influence
Demonstrating ability to lead initiatives that span multiple teams without direct authority. How you influence technical decisions, build consensus, and drive changes across organizational boundaries. Examples of successfully navigating complex technical or organizational situations.
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Mentoring & Technical Talent Development
Your approach to developing engineers—how you've mentored junior and senior staff, helped teammates grow, created learning opportunities. Examples of engineers you've developed and their growth trajectory. How you approach coaching on technical and professional development.
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Frequently Asked Site Reliability Engineer (SRE) Interview Questions
Sample Answer
# nodes have: deploy_time, compatibility_check(targets)
sccs = tarjan_scc(graph)
dag = condense_graph(sccs)
for n in dag:
n.critical = longest_path(n) # DFS DP using deploy_time
ready = [n for n in dag if n.indegree==0]
# max_concurrency, resource_limits defined
time = 0
running = [] # heap of (finish_time, node)
batches = [] # list of (start_time, [nodes])
while ready or running:
# try to fill slots
slots = max_concurrency - len(running)
candidates = sorted(ready, key=lambda x: -x.critical)
to_start = []
for c in candidates:
if slots<=0: break
if c.compatible_with_deployed():
to_start.append(c); slots-=1
if to_start:
for n in to_start:
ready.remove(n)
heapq.heappush(running, (time + n.deploy_time, n))
batches.append((time, [n.id for n in to_start]))
continue
# advance time to next finish
finish_time, finished = heapq.heappop(running)
time = finish_time
mark_deployed(finished)
for nb in finished.out_neighbors:
nb.indegree -= 1
if nb.indegree==0:
ready.append(nb)Sample Answer
Sample Answer
Sample Answer
import math
import numpy as np
from typing import List, Optional
def propose_instances(timeseries_cpu_percent: List[List[Optional[float]]],
per_instance_cpu_capacity_percent: float,
target_p95_util_percent: float) -> int:
"""
timeseries_cpu_percent: list over instances -> list of timestamped samples (None for missing)
per_instance_cpu_capacity_percent: usually 100.0
target_p95_util_percent: desired p95 per-instance utilization (e.g., 70.0)
Returns: proposed number of identical instances
"""
# Flatten contemporaneous snapshots: for each timestamp, sum across instances to get total load at that time.
# Assumes equal-length inner lists; if not, align by index and treat missing as NaN.
arr = np.array([[np.nan if x is None else x for x in inst] for inst in timeseries_cpu_percent], dtype=float)
# per-timestamp total utilization (%) = mean per-instance * num_instances_present
num_instances_current = arr.shape[0]
# For each column (timestamp), compute total load as sum of available samples scaled to full set:
# If some instances missing, compute mean of available and multiply by num_instances_current.
col_means = np.nanmean(arr, axis=0) # ignores NaN
# If an entire column is NaN -> drop that timestamp
valid = ~np.isnan(col_means)
if not np.any(valid):
return max(1, num_instances_current)
total_load_percent = col_means[valid] * num_instances_current
# compute per-instance utilization under current topology as total_load / current_instances (same as col_means)
# To compute p95 of per-instance utilization, use the per-timestamp per-instance mean values
per_instance_samples = col_means[valid]
current_p95 = np.percentile(per_instance_samples, 95)
# If capacity per instance < target threshold, need to ensure we don't propose > capacity
# find smallest N such that current_p95 * (num_instances_current / N) <= target_p95
# rearrange: N >= num_instances_current * current_p95 / target_p95
required = (num_instances_current * current_p95) / max(1e-6, target_p95_util_percent)
N = max(1, math.ceil(required))
# also cap by instance CPU capacity (can't exceed per-instance capacity percent)
if target_p95_util_percent > per_instance_cpu_capacity_percent:
# target unrealistic; instead aim for capacity
N = max(1, math.ceil((num_instances_current * current_p95) / per_instance_cpu_capacity_percent))
return int(N)Sample Answer
apiVersion: apps/v1
kind: Deployment
metadata: {name: my-batch}
spec:
template:
spec:
tolerations:
- key: "spot"
operator: "Equal"
value: "spot-instance"
effect: "NoSchedule"
containers:
- name: worker
image: my-batch:latestSample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Recommended Additional Resources
- Designing Data-Intensive Applications by Martin Kleppmann—Essential reading for understanding distributed systems and their application to real-world architectures
- The SRE Book and The Site Reliability Workbook (Google)—Foundational SRE philosophy, practices, and case studies from Google
- Kubernetes in Action—Deep understanding of container orchestration, deployments, and operational patterns
- Release It! by Michael T. Nygard—Understanding operational and architectural concerns in system design
- Staff Engineer by Will Larson—Guidance on navigating and succeeding at staff-level positions in tech
- Radical Candor by Kim Scott—Leadership and communication approach valuable for Staff-level interviews
- Meta Engineering blog and papers—Understand Meta's infrastructure challenges, solutions, and technical direction
- LeetCode and SystemDesign.pub—Practice coding and system design problems at Staff level difficulty
- Glassdoor, Levels.fyi, Blind—Recent interview experiences, compensation data, and company culture feedback
- GitHub SRE Interview Prep Guide—Comprehensive resource collection for SRE-specific interview preparation
- Incident response simulation exercises—Practice handling real operational crises and decision-making under pressure
- Performance Optimization and Scalability Workshop materials—Deep dives into optimization techniques and capacity planning at scale
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