[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"prev-cloud-computing-fundamentals-security-and-reliability-reliability-and-operations-scalability-and-elasticity-en":4,"lesson-cloud-computing-fundamentals-security-and-reliability-reliability-and-operations-scalability-and-elasticity-en":364,"next-cloud-computing-fundamentals-security-and-reliability-reliability-and-operations-scalability-and-elasticity-en":637},null,{"locked":5,"reason":3,"meta":6,"item":17},false,{"title":7,"description":8,"isFree":9,"estimatedMinutes":10,"difficulty":11,"learningObjectives":12},"High Availability and Fault Tolerance","Why a system that recovers from a failure in under 2 minutes and a system that never blinks at all are solving the same problem with 2 different budgets, and the redundancy patterns cloud teams use to build each.",true,18,"beginner",[13,14,15,16],"Define high availability and fault tolerance and explain what separates them","Explain how spreading compute and database capacity across Availability Zones removes a single point of failure","Compare active-active and active-passive redundancy using a worked failover example","Apply the high-availability-versus-fault-tolerance distinction to a scenario and identify which pattern its requirement actually calls for",{"id":18,"title":7,"body":19,"description":8,"difficulty":11,"estimatedMinutes":10,"extension":286,"infographics":287,"isFree":9,"learningObjectives":298,"meta":299,"navigation":9,"path":300,"quiz":301,"seo":361,"stem":362,"__hash__":363},"courses/courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/01-high-availability-and-fault-tolerance.md",{"type":20,"value":21,"toc":272},"minimark",[22,27,31,35,38,42,45,48,52,55,58,63,67,70,137,140,144,147,196,199,203,206,210,262,265,269],[23,24,26],"h2",{"id":25},"the-2am-hardware-failure","The 2am hardware failure",[28,29,30],"p",{},"A retailer runs its checkout API on a single virtual machine. At 2am, the physical host underneath that instance fails, silently and completely, the way hardware eventually does no matter whose logo is on the rack. The instance is gone, and so is checkout, until someone notices and launches a replacement. That lone instance was a single point of failure: 1 resource whose failure alone took the whole system down with it. Removing single points of failure like it is what the rest of this lesson is about, and it turns out there are 2 different levels of \"removed,\" not 1.",[23,32,34],{"id":33},"recall-the-building-blocks-you-already-have","Recall: the building blocks you already have",[28,36,37],{},"You already have the 2 tools that make removal possible. Availability Zones give you physically separate locations to spread instances across, so 1 data center's bad night doesn't take out everything at once. A load balancer sits in front of those instances, health-checks each one, and routes traffic only to the ones still responding. Put the 2 together, instances spread across multiple AZs behind a load balancer, and a repeat of the 2am failure stops being catastrophic. What it becomes next, a brief hiccup or nothing at all, depends on how much spare capacity was already running the moment the failure hit.",[23,39,41],{"id":40},"high-availability-staying-up-through-a-brief-interruption","High availability: staying up through a brief interruption",[28,43,44],{},"Amazon RDS Multi-AZ deployments show this pattern clearly. A primary database instance handles every read and write, while a standby in a different Availability Zone stays synchronously updated in the background, ready but idle. If the primary fails, RDS detects it, promotes the standby, and repoints the database's DNS record to it, typically in 60 to 120 seconds, with 0 data loss. For those 60 to 120 seconds, though, connections to the database do drop or queue.",[28,46,47],{},"That is high availability: the system recovers automatically and quickly, but the recovery itself is a visible, if brief, gap. Most applications only need this. A minute of connection errors during a rare zone failure is a very different problem than the outright outage the single-instance retailer suffered.",[23,49,51],{"id":50},"fault-tolerance-staying-up-with-no-interruption-at-all","Fault tolerance: staying up with no interruption at all",[28,53,54],{},"Now go back to the checkout API, but redesigned. Say it needs 6 running instances to handle its normal load. Spread across 2 Availability Zones, 3 instances each, that is already highly available: lose 1 zone, and the surviving 3 instances keep checkout running, just at half capacity until new instances launch behind them. Spread across 3 Availability Zones instead, with 3 instances in each, 9 instances total, and losing any 1 zone still leaves 6 instances live, exactly the capacity checkout needs. Nothing about the user experience changes. That is fault tolerance: the system was never actually short of capacity, because the \"spare\" instances were already running and already serving traffic before the failure happened.",[28,56,57],{},"The comparison is the entire distinction in 1 image: a highly available design has enough redundancy to survive a failure, and a fault-tolerant design has enough redundancy that surviving it produces no dip to survive. Fault tolerance is closer to a self-sealing tire that keeps a car driving at full speed through a puncture than to a spare tire that gets the car moving again after a stop to change it, which is what high availability's recovery window looks like. Push the analogy further than that single point, though, and it breaks: a spare tire is cheap to carry unused, while fault-tolerant capacity bills you every hour it sits idle, waiting for a failure that may never come.",[59,60],"infographic",{"alt":61,"slug":62},"A side-by-side comparison showing a highly available setup of 6 instances across 2 zones losing half its capacity when 1 zone fails, next to a fault-tolerant setup of 9 instances across 3 zones keeping its full 6-instance capacity when 1 zone fails.","high-availability-and-fault-tolerance-redundancy-capacity",[23,64,66],{"id":65},"where-redundancy-has-to-reach-every-layer-not-just-compute","Where redundancy has to reach: every layer, not just compute",[28,68,69],{},"A system is only as available as its least redundant layer. Spreading the compute tier across zones does nothing if the database, the load balancer, or DNS is still a single point of failure underneath it.",[71,72,73,89],"table",{},[74,75,76],"thead",{},[77,78,79,83,86],"tr",{},[80,81,82],"th",{},"Layer",[80,84,85],{},"Single point of failure",[80,87,88],{},"Redundancy pattern",[90,91,92,104,115,126],"tbody",{},[77,93,94,98,101],{},[95,96,97],"td",{},"Compute",[95,99,100],{},"1 instance",[95,102,103],{},"Multiple instances across AZs behind a load balancer",[77,105,106,109,112],{},[95,107,108],{},"Load balancer",[95,110,111],{},"1 load balancer node",[95,113,114],{},"Managed load balancers run redundantly across AZs by default",[77,116,117,120,123],{},[95,118,119],{},"Database",[95,121,122],{},"1 database instance",[95,124,125],{},"Multi-AZ deployment with automatic failover",[77,127,128,131,134],{},[95,129,130],{},"DNS",[95,132,133],{},"1 static record with no health awareness",[95,135,136],{},"Health-checked DNS routing that stops pointing at a failed target",[28,138,139],{},"Cloud providers build managed load balancers and managed DNS health checks to already be redundant, so the layers a team usually has to design for explicitly are compute and database.",[23,141,143],{"id":142},"active-active-versus-active-passive","Active-active versus active-passive",[28,145,146],{},"The 2 redundancy patterns above have names, and the difference between them shows up again later in this topic. Active-active means every replica is serving traffic at the same time, the way a load balancer spreads requests across all healthy compute instances right now, not just when something fails. Active-passive means 1 side does the work while the other stays synchronized and idle, ready to take over, the way an RDS Multi-AZ standby behaves.",[71,148,149,161],{},[74,150,151],{},[77,152,153,155,158],{},[80,154],{},[80,156,157],{},"Active-active",[80,159,160],{},"Active-passive",[90,162,163,174,185],{},[77,164,165,168,171],{},[95,166,167],{},"Who serves traffic normally",[95,169,170],{},"All replicas, simultaneously",[95,172,173],{},"1 primary only",[77,175,176,179,182],{},[95,177,178],{},"What a failure looks like",[95,180,181],{},"Remaining replicas absorb the load already",[95,183,184],{},"A failover promotes the standby",[77,186,187,190,193],{},[95,188,189],{},"Typical use",[95,191,192],{},"Load-balanced compute tiers",[95,194,195],{},"Multi-AZ managed databases",[28,197,198],{},"Neither pattern is inherently better. Compute tiers are usually active-active because splitting stateless requests across many identical instances is straightforward. Databases are more often active-passive because keeping every replica simultaneously writable raises hard consistency questions that a single active writer avoids.",[23,200,202],{"id":201},"misconception-multi-az-does-not-automatically-mean-fault-tolerant","Misconception: \"multi-AZ\" does not automatically mean \"fault-tolerant\"",[28,204,205],{},"It is tempting to hear \"deployed across multiple Availability Zones\" and assume that settles the reliability question. It settles only half of it. Multi-AZ tells you a failure in 1 zone will not take down every instance. It says nothing about whether the surviving capacity is enough to keep serving your full load without a dip. A team that spreads 6 instances across 2 zones has genuinely improved its availability over a single-AZ deployment, but it has not made itself fault-tolerant, and assuming otherwise is exactly the gap that shows up as an unplanned capacity shortage during the next zone-level event.",[23,207,209],{"id":208},"exam-cues-reading-a-high-availability-versus-fault-tolerance-question","Exam cues: reading a high-availability-versus-fault-tolerance question",[71,211,212,222],{},[74,213,214],{},[77,215,216,219],{},[80,217,218],{},"The scenario says...",[80,220,221],{},"Points to",[90,223,224,232,240,248,255],{},[77,225,226,229],{},[95,227,228],{},"\"Brief interruption is acceptable,\" \"automatic recovery,\" \"minimal downtime\"",[95,230,231],{},"High availability",[77,233,234,237],{},[95,235,236],{},"\"No interruption at all,\" \"seamless,\" \"users must never notice\"",[95,238,239],{},"Fault tolerance",[77,241,242,245],{},[95,243,244],{},"\"1 instance,\" \"1 data center,\" \"no redundancy\"",[95,246,247],{},"A single point of failure to remove first",[77,249,250,253],{},[95,251,252],{},"\"Standby,\" \"failover,\" \"promoted\"",[95,254,160],{},[77,256,257,260],{},[95,258,259],{},"\"All instances serve traffic simultaneously\"",[95,261,157],{},[28,263,264],{},"The trap to watch for: a scenario that describes a solid multi-AZ setup and asks whether it is fault-tolerant. Check the capacity math, not just the zone count, before answering.",[23,266,268],{"id":267},"where-this-leaves-you","Where this leaves you",[28,270,271],{},"Both patterns start from the same move, removing a single point of failure, and then diverge on how much spare capacity you are willing to run and pay for around the clock. High availability tolerates a short, automated recovery window; fault tolerance eliminates that window by keeping the redundant capacity already live. Most applications only need the first. The next lesson turns from staying up during a failure to a related but separate question: once you have decided how much capacity a system needs, who decides when to add more of it, and when to take it away again.",{"title":273,"searchDepth":274,"depth":274,"links":275},"",3,[276,278,279,280,281,282,283,284,285],{"id":25,"depth":277,"text":26},2,{"id":33,"depth":277,"text":34},{"id":40,"depth":277,"text":41},{"id":50,"depth":277,"text":51},{"id":65,"depth":277,"text":66},{"id":142,"depth":277,"text":143},{"id":201,"depth":277,"text":202},{"id":208,"depth":277,"text":209},{"id":267,"depth":277,"text":268},"md",[288],{"slug":62,"concept":289,"style":290,"aspectRatio":291,"labels":292},"A 2-panel side-by-side comparison. Left panel titled Highly Available shows 2 Availability Zone boxes, each holding 3 instance icons, 6 total, with 1 zone grayed out as failed and a capacity bar dropping from 6 to 3. Right panel titled Fault Tolerant shows 3 Availability Zone boxes, each holding 3 instance icons, 9 total, with 1 zone grayed out as failed and a capacity bar staying flat at 6. A footer strip carries the cost takeaway.","comparison","16:9",[293,294,295,296,297],"Highly Available: 6 instances across 2 zones","1 zone fails: capacity drops to 3 until new instances launch","Fault Tolerant: 9 instances across 3 zones","1 zone fails: 6 instances keep serving traffic, no drop","Fault tolerance means paying for capacity that sits idle, just to buy zero interruption.",[13,14,15,16],{},"/courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/01-high-availability-and-fault-tolerance",{"passingScore":302,"questions":303},70,[304,313,321,329,339,345,353],{"question":305,"type":306,"options":307,"correctAnswer":309,"explanation":312},"A retailer runs its checkout API on a single virtual machine. At 2am, the physical host underneath it fails and checkout goes down until someone notices and launches a replacement. What term describes the instance in this scenario?","single",[308,309,310,311],"A load balancer target","A single point of failure","A fault-tolerant resource","An Availability Zone","A single point of failure is any one resource whose failure alone takes the whole system down. The instance qualifies because nothing else was running to absorb checkout traffic when it disappeared. Removing single points of failure, not making any one resource perfectly reliable, is the actual goal of this lesson.",{"question":314,"type":306,"options":315,"correctAnswer":317,"explanation":320},"An Amazon RDS Multi-AZ database fails over from its primary to its standby in 90 seconds, with zero data loss but a brief drop in database connections during the switch. This is an example of which pattern?",[316,317,318,319],"Fault tolerance, since no data was lost","High availability, since the interruption was brief rather than absent","Elasticity, since capacity changed automatically","A single point of failure, since only 1 instance was serving traffic beforehand","High availability tolerates a brief interruption while a standby takes over. The RDS standby was not already serving traffic, so the switch itself, however fast, is a visible gap. Fault tolerance requires that spare capacity already be live and absorbing load, so a failure produces no gap at all, not just a short one.",{"question":322,"type":306,"options":323,"correctAnswer":325,"explanation":328},"A team needs 6 running instances to handle its normal checkout load. Which of the following setups is fault-tolerant against the loss of any single Availability Zone, not just highly available?",[324,325,326,327],"6 instances split across 2 Availability Zones, 3 in each","9 instances split across 3 Availability Zones, 3 in each","6 instances all placed in a single Availability Zone","6 instances split across 2 Availability Zones is already fault-tolerant, since it uses more than 1 zone","Losing 1 of 3 zones in the 9-instance setup still leaves 6 instances running, exactly the capacity the checkout load needs, so nothing about the user experience changes. The 6-instance, 2-zone setup is highly available (it survives the failure) but not fault-tolerant: losing 1 zone drops capacity to 3, a visible degradation until new instances launch.",{"question":330,"type":331,"options":332,"correctAnswers":337,"explanation":338},"Which of the following are true about fault tolerance compared to high availability? (Select all that apply.)","multiple",[333,334,335,336],"Fault tolerance requires redundant capacity that is already running and absorbing load before a failure happens","Fault tolerance always costs less than high availability, since it prevents outages entirely","High availability allows a brief, automatic recovery window that users may notice","Both patterns require removing single points of failure as a starting point",[333,335,336],"Fault tolerance costs more, not less, because it means running extra capacity around the clock that only earns its keep during a failure. High availability's defining trait is that a failure is survivable but not invisible. Neither pattern is possible without first removing the single point of failure that a lone instance or lone AZ represents.",{"question":340,"type":306,"options":341,"correctAnswer":343,"explanation":344},"A highly available architecture and a fault-tolerant architecture both always use exactly the same number of running instances.",[342,343],"True","False","A fault-tolerant architecture needs enough spare, already-running capacity that losing 1 zone still leaves full capacity in place, which typically means more total instances than a highly available setup built for the same normal load. The 6-versus-9 instance comparison in this lesson is exactly that gap.",{"question":346,"type":306,"options":347,"correctAnswer":349,"explanation":352},"In an active-passive database setup like Amazon RDS Multi-AZ, what is the standby instance doing while the primary is healthy?",[348,349,350,351],"Serving read traffic to reduce load on the primary","Nothing visible to users; it stays synchronized but does not serve read or write traffic","Running a separate, unrelated workload to avoid wasting capacity","Actively load-balancing writes with the primary","Active-passive means exactly 1 side is doing the work at any moment. The standby stays synchronously updated so it can take over quickly, but it sits idle from a traffic standpoint until a failover promotes it. Active-active is the pattern where multiple replicas serve traffic simultaneously, which is what a load balancer already does across your compute instances.",{"question":354,"type":306,"options":355,"correctAnswer":357,"explanation":360},"An exam scenario describes a payment-processing system that cannot show users any interruption, even a few seconds, during a single Availability Zone outage. Which pattern does the scenario call for?",[356,357,358,359],"High availability, since a short automated recovery is normally acceptable","Fault tolerance, since the requirement rules out any visible gap at all","Elasticity, since the system needs to grow with demand","A backup and restore strategy, since data loss is the main concern","The phrase \"cannot show users any interruption, even a few seconds\" is the exact keyword pattern that rules out high availability, which by definition tolerates a brief automated recovery. Only a fault-tolerant design, with spare capacity already live before the failure, meets a zero-visible-gap requirement.",{"title":7,"description":8},"courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/01-high-availability-and-fault-tolerance","5Xh9o9PQt0Yepp0K4AUTkTESOZ1VsXntBg5JLaS6IlI",{"locked":5,"reason":3,"meta":365,"item":374},{"title":366,"description":367,"isFree":5,"estimatedMinutes":10,"difficulty":368,"learningObjectives":369},"Scalability and Elasticity","Why a system that can grow and a system that grows and shrinks itself automatically are 2 different skills, and how an Auto Scaling group's capacity settings and scaling policies deliver the second one.","intermediate",[370,371,372,373],"Distinguish scalability from elasticity and explain why they answer different questions","Explain how an Auto Scaling group's minimum, desired, and maximum capacity settings work together","Compare target tracking, step, and scheduled scaling policies and identify which fits a given demand pattern","Apply elasticity to a worked example that shows the cost impact of matching capacity to real-time demand",{"id":375,"title":366,"body":376,"description":367,"difficulty":368,"estimatedMinutes":10,"extension":286,"infographics":566,"isFree":5,"learningObjectives":576,"meta":577,"navigation":9,"path":578,"quiz":579,"seo":634,"stem":635,"__hash__":636},"courses/courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/02-scalability-and-elasticity.md",{"type":20,"value":377,"toc":554},[378,382,385,389,392,396,399,403,406,409,413,416,419,423,427,477,480,484,487,491,494,498,549,551],[23,379,381],{"id":380},"the-2-ways-a-plan-for-more-traffic-can-go-wrong","The 2 ways a plan for more traffic can go wrong",[28,383,384],{},"A retailer sizes its checkout servers for an average shopping day and gets crushed the first time a flash sale doubles traffic overnight; requests queue, pages time out, and the sale becomes the outage story instead of the revenue story. A different team overcorrects: it provisions enough servers to survive its single biggest sale day of the year, then pays for that same fleet the other 364 days, most of them running at a fraction of their capacity. Neither team has solved the actual problem, which has 2 separate parts: can the system grow at all, and does it grow (and shrink) itself to match what is actually happening right now.",[23,386,388],{"id":387},"recall-growth-you-already-know-how-to-do","Recall: growth you already know how to do",[28,390,391],{},"The compute lesson earlier in this course covered horizontal scaling, adding more instances of the same size behind a load balancer, as one answer to running out of capacity. What it did not cover is who decides when to add those instances, or when to take them away again once the load passes. That gap, the decision-making layer sitting on top of horizontal scaling, is exactly what this lesson fills in.",[23,393,395],{"id":394},"scalability-the-capacity-to-grow","Scalability: the capacity to grow",[28,397,398],{},"Scalability is a system's ability to handle more load by adding resources, whether that means resizing 1 machine (vertical scaling) or adding more machines of the same size (horizontal scaling). Nothing in that definition says the growth has to be automatic. A team that notices rising traffic and manually launches 3 more instances has scaled the system. So has an on-premises IT department that orders and racks 2 new physical servers over the course of a month. Both improved the system's capacity to handle load; neither one did it by itself, in real time, or gave any capacity back once it stopped being needed.",[23,400,402],{"id":401},"elasticity-matching-capacity-to-demand-automatically","Elasticity: matching capacity to demand, automatically",[28,404,405],{},"Elasticity is what happens when that decision-making gets automated in both directions: capacity increases as demand rises and decreases as demand falls, without a person watching a dashboard and clicking a button each time. It is a genuinely cloud-native idea. A physical server you already own and paid for doesn't shrink your bill when it sits idle overnight; a cloud instance that gets terminated during a quiet period stops costing you anything the moment it's gone. Elasticity is the reason \"pay only for what you use\" is more than a slogan: it requires the system to actually notice when \"what you use\" has changed and to act on that, continuously, without waiting for a human.",[28,407,408],{},"Picture a highway that engineers can widen with a construction crew over several months versus a highway with a reversible lane that opens automatically when sensors detect rush-hour traffic and closes again once it clears. Widening the highway is scalability: real capacity, real effort, and no reason to ever undo it. The reversible lane is elasticity: the same road, resized instantly and automatically to the traffic actually on it right now, and closed back up the moment it isn't needed. Push the comparison further and it breaks, a reversible lane is a fixed piece of infrastructure that gets toggled, not created and destroyed, while a cloud instance is provisioned and torn down entirely each time, which is exactly what makes it cost nothing while it's gone.",[23,410,412],{"id":411},"worked-example-1-day-inside-an-auto-scaling-group","Worked example: 1 day inside an Auto Scaling group",[28,414,415],{},"An Auto Scaling group is the AWS mechanism that automates this. Say a group is configured with a minimum of 4 instances, a desired capacity of 6, and a maximum of 12. Minimum and maximum are hard boundaries the group will never cross in either direction; desired capacity is the starting point a scaling policy adjusts from within that range.",[28,417,418],{},"At 9am, traffic climbs and average CPU utilization across the group rises past its target. A target-tracking policy launches new instances, 1 or 2 at a time, until CPU utilization settles back near target, capacity landing around 10 instances by mid-morning. Traffic holds through the day and eases off by 6pm; the same policy now terminates instances as CPU utilization drops below target, easing capacity back down toward 6, and further toward the 4-instance minimum overnight. The group never touches its 12-instance ceiling that day, and it never runs below 4. The team pays for roughly 10 instances during the 9 busy hours that needed them and far fewer overnight, instead of running 12 (or even a flat 10) for all 24.",[59,420],{"alt":421,"slug":422},"A chart showing customer traffic rising and falling over a 24-hour day as a smooth curve, with an Auto Scaling group's stepped capacity line tracking it automatically between a minimum and a maximum boundary.","scalability-and-elasticity-capacity-vs-demand",[23,424,426],{"id":425},"scaling-policies-3-ways-to-tell-the-group-when-to-act","Scaling policies: 3 ways to tell the group when to act",[71,428,429,442],{},[74,430,431],{},[77,432,433,436,439],{},[80,434,435],{},"Policy",[80,437,438],{},"How it decides",[80,440,441],{},"Best fit",[90,443,444,455,466],{},[77,445,446,449,452],{},[95,447,448],{},"Target tracking",[95,450,451],{},"Continuously adjusts capacity to hold a chosen metric, like average CPU utilization, near a target value",[95,453,454],{},"Live, somewhat unpredictable demand",[77,456,457,460,463],{},[95,458,459],{},"Step scaling",[95,461,462],{},"Adds or removes capacity in defined steps sized to how far a metric has moved past its threshold",[95,464,465],{},"Demand that can spike sharply and needs a proportionally larger response",[77,467,468,471,474],{},[95,469,470],{},"Scheduled scaling",[95,472,473],{},"Sets capacity ahead of a known time, independent of any live metric",[95,475,476],{},"Predictable patterns, like a retailer's daily 9am traffic or a monthly batch job",[28,478,479],{},"These are not mutually exclusive. A retail team might run scheduled scaling to pre-warm capacity just before its known 9am spike, then let target tracking handle the rest of the day's smaller fluctuations on top of that head start.",[23,481,483],{"id":482},"elasticity-also-means-self-healing","Elasticity also means self-healing",[28,485,486],{},"Auto Scaling groups do more than resize for demand. They continuously health-check every instance and replace any that fail, automatically, to keep the group at its desired capacity even when nothing about traffic has changed. That connects directly back to the previous lesson: an Auto Scaling group spread across multiple Availability Zones is part of how a highly available architecture gets built in the first place, not a separate concern from it.",[23,488,490],{"id":489},"misconception-auto-scaling-is-not-just-about-scaling-up","Misconception: \"Auto Scaling\" is not just about scaling up",[28,492,493],{},"It's tempting to hear \"Auto Scaling\" and picture only the exciting half: automatically adding capacity to survive a traffic spike. The half that actually saves money is scaling in, removing capacity once demand drops. A group with no minimum worth respecting or no scale-in policy attached just becomes an expensive, self-launching version of plain scalability: it grows on its own, but it never gives anything back, and the cost advantage elasticity is supposed to deliver never shows up on the bill.",[23,495,497],{"id":496},"exam-cues-reading-a-scalability-versus-elasticity-question","Exam cues: reading a scalability-versus-elasticity question",[71,499,500,508],{},[74,501,502],{},[77,503,504,506],{},[80,505,218],{},[80,507,221],{},[90,509,510,518,526,534,541],{},[77,511,512,515],{},[95,513,514],{},"\"Handle a growing amount of load by adding resources\"",[95,516,517],{},"Scalability",[77,519,520,523],{},[95,521,522],{},"\"Automatically adjusts to real-time demand,\" \"pay only for what you use,\" \"scales in when idle\"",[95,524,525],{},"Elasticity",[77,527,528,531],{},[95,529,530],{},"\"Minimum,\" \"desired,\" \"maximum capacity\"",[95,532,533],{},"Auto Scaling group settings",[77,535,536,539],{},[95,537,538],{},"\"Known, predictable spike at a specific time\"",[95,540,470],{},[77,542,543,546],{},[95,544,545],{},"\"Maintain a target CPU utilization\"",[95,547,548],{},"Target tracking scaling",[23,550,268],{"id":267},[28,552,553],{},"Scalability answers whether a system can grow at all; elasticity answers whether it grows, and shrinks, itself, automatically, in step with demand that changes minute to minute. Every cloud workload needs the first. Only workloads with genuinely variable demand get real value from the second, which is most of them, since traffic that never varies is rare outside a handful of steady internal systems. The next lesson assumes all of this, redundancy, health checks, right-sized capacity, and asks what happens anyway: when a failure is bigger than any of it can absorb, and a team has to fall back on backups and a disaster recovery plan.",{"title":273,"searchDepth":274,"depth":274,"links":555},[556,557,558,559,560,561,562,563,564,565],{"id":380,"depth":277,"text":381},{"id":387,"depth":277,"text":388},{"id":394,"depth":277,"text":395},{"id":401,"depth":277,"text":402},{"id":411,"depth":277,"text":412},{"id":425,"depth":277,"text":426},{"id":482,"depth":277,"text":483},{"id":489,"depth":277,"text":490},{"id":496,"depth":277,"text":497},{"id":267,"depth":277,"text":268},[567],{"slug":422,"concept":568,"style":569,"aspectRatio":291,"labels":570},"A single quantitative chart over a 24-hour horizontal axis. A smooth curved line shows customer traffic rising through the morning, peaking at midday, and falling overnight. A stepped line overlays it, showing Auto Scaling group capacity rising and falling in blocks to track the traffic curve, bounded above and below by dashed horizontal lines marking maximum and minimum capacity. A footer strip carries the cost takeaway.","diagram",[571,572,573,574,575],"Traffic: rises in the morning, peaks midday, falls overnight","Auto Scaling group capacity: steps up and down to track traffic","Maximum capacity: 12 instances","Minimum capacity: 4 instances","Elasticity means paying for the stepped line, not a flat line held at the maximum all day.",[370,371,372,373],{},"/courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/02-scalability-and-elasticity",{"passingScore":302,"questions":580},[581,589,597,605,614,622,626],{"question":582,"type":306,"options":583,"correctAnswer":585,"explanation":588},"A team manually doubles the size of its database server after a product launch permanently increases traffic. What is this an example of?",[584,585,586,587],"Elasticity, since capacity changed to meet demand","Scalability, since the system's capacity to handle load grew","Fault tolerance, since the change prevents future outages","A single point of failure being removed","Scalability is the ability to grow, whether by resizing 1 machine or adding more of them, and whether that growth is manual, scripted, or automatic. This example is scalability because the capacity grew; it is not elasticity because nothing here happened automatically in response to a live demand signal, and the change was not reversed once it was no longer needed.",{"question":590,"type":306,"options":591,"correctAnswer":593,"explanation":596},"Which of the following best distinguishes elasticity from scalability?",[592,593,594,595],"Elasticity only applies to compute, while scalability applies to storage and databases as well","Scalability is the system's capacity to grow at all; elasticity is that growth (and shrinkage) happening automatically in response to real-time demand","Scalability requires downtime, while elasticity never does","They are the same concept, described with 2 different marketing terms","A system can be scalable without being elastic, for example an on-premises data center that can add more physical servers, slowly and manually, but can never give any of them back once demand drops. Elasticity specifically adds automation in both directions, scaling out and scaling back in, which is why it is a cloud-era concept more than an on-premises one.",{"question":598,"type":306,"options":599,"correctAnswer":602,"explanation":604},"An Auto Scaling group is configured with a minimum of 4, a desired capacity of 6, and a maximum of 12. What does the maximum of 12 guarantee?",[600,601,602,603],"The group will always run exactly 12 instances","The group will never run fewer than 12 instances","The group will never scale beyond 12 instances, no matter how high demand climbs","The group will terminate all instances once traffic exceeds 12 requests per second","Maximum capacity is a ceiling, not a target: the group can run anywhere from its minimum up to its maximum, and scaling policies decide the actual count moment to moment within that range. Desired capacity, 6 in this example, is the starting point a scaling policy adjusts from, not a fixed value the group is locked to.",{"question":606,"type":331,"options":607,"correctAnswers":612,"explanation":613},"Which of the following are true about Amazon EC2 Auto Scaling? (Select all that apply.)",[608,609,610,611],"It automatically replaces instances that fail their health checks to maintain the desired capacity","It balances instances across the Availability Zones configured for the group","It can only scale a group up; scaling back in requires manual intervention","Target tracking scaling adjusts capacity to hold a chosen metric, such as average CPU utilization, near a target value",[608,609,611],"Auto Scaling groups scale in both directions automatically once a policy is attached; a scale-out-only group would keep adding instances forever and never realize the cost savings elasticity is meant to deliver. Health-check-based replacement and AZ balancing are both automatic parts of what makes an Auto Scaling group reliable, not just elastic.",{"question":615,"type":306,"options":616,"correctAnswer":618,"explanation":621},"A scheduled scaling policy is best suited for which situation?",[617,618,619,620],"Traffic spikes that are unpredictable and happen at random times","A demand pattern that is known in advance, such as a retailer's traffic spiking every day at 9am","Keeping a single metric, like CPU utilization, at a constant target at all times","Replacing an unhealthy instance immediately after it fails a health check","Scheduled scaling sets capacity ahead of a known pattern, so instances are already warmed up before the predictable spike hits, instead of reacting after the fact. Target tracking is the better fit for unpredictable, live demand, since it continuously adjusts to hold a metric near its target regardless of when the change happens.",{"question":623,"type":306,"options":624,"correctAnswer":343,"explanation":625},"A well-configured Auto Scaling group only ever adds capacity in response to rising demand; it never removes it once traffic drops.",[342,343],"Scaling in, removing instances as demand falls, is where elasticity actually pays for itself. A group with no minimum-respecting scale-in behavior is not elastic, it is just an expensive, manually-triggered version of scalability that happens to launch itself but never shrinks back down.",{"question":627,"type":306,"options":628,"correctAnswer":630,"explanation":633},"A team sizes its Auto Scaling group's maximum capacity for its historical worst-case traffic day, but sets a low minimum and a target-tracking policy for everyday demand. What is the main cost benefit of this setup compared to running the maximum capacity permanently?",[629,630,631,632],"None; AWS charges the same regardless of how many instances are actually running","The team pays only for the capacity it actually needs at each moment, instead of paying for peak capacity around the clock","It removes the need for a load balancer entirely","It guarantees the system becomes fault-tolerant","This is the entire economic case for elasticity: instances cost money by the hour they run, so a group that tracks real demand and scales back in during quiet periods avoids paying for idle peak-day capacity 24 hours a day. A fixed fleet sized for the worst case would handle the same traffic but at a much higher steady-state bill.",{"title":366,"description":367},"courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/02-scalability-and-elasticity","0PTUatXygnuSsmAEr9ZZClZQBUy9H7qc9Z4RJq5xNXs",{"locked":5,"reason":3,"meta":638,"item":647},{"title":639,"description":640,"isFree":5,"estimatedMinutes":641,"difficulty":368,"learningObjectives":642},"Backup and Disaster Recovery","What Recovery Time Objective and Recovery Point Objective actually bound, and the 4 disaster recovery strategies cloud teams choose between when redundancy inside a region was not enough.",20,[643,644,645,646],"Define Recovery Time Objective and Recovery Point Objective and explain what each one bounds","Explain how AWS Backup automates data protection through backup plans, schedules, and retention rules","Compare backup and restore, pilot light, warm standby, and multi-site active/active by cost, complexity, and recovery speed","Apply a scenario's RTO and RPO requirements to choose the appropriate disaster recovery strategy",{"id":648,"title":639,"body":649,"description":640,"difficulty":368,"estimatedMinutes":641,"extension":286,"infographics":879,"isFree":5,"learningObjectives":880,"meta":881,"navigation":9,"path":882,"quiz":883,"seo":946,"stem":947,"__hash__":948},"courses/courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/03-backup-and-disaster-recovery.md",{"type":20,"value":650,"toc":867},[651,655,658,662,665,672,678,681,685,688,692,695,771,774,778,781,785,788,792,795,799,802,806,862,864],[23,652,654],{"id":653},"when-redundancy-inside-a-region-was-not-enough","When redundancy inside a region was not enough",[28,656,657],{},"The last 2 lessons covered redundancy inside a single AWS Region: spreading instances and databases across Availability Zones so that 1 data center's failure doesn't take the whole system down. That protects against a hardware failure or a power outage. It does nothing if a bad deployment corrupts a database table, or if an entire geographic region goes dark, the exact gap the regions-and-Availability-Zones lesson flagged earlier in this course. This lesson is about the plan for exactly that: when the failure is bigger than AZ-level redundancy was ever built to absorb.",[23,659,661],{"id":660},"two-questions-before-any-recovery-plan-how-much-data-how-long-down","Two questions before any recovery plan: how much data, how long down",[28,663,664],{},"Every disaster recovery plan starts by answering 2 separate questions, and mixing them up is the most common mistake teams make.",[28,666,667,671],{},[668,669,670],"strong",{},"Recovery Point Objective (RPO)"," bounds acceptable data loss. It answers \"how much data can we afford to lose,\" measured as the maximum acceptable time between now and the last point you could recover from. An RPO of 15 minutes means your backups or replication have to run at least that often, or you risk losing more than the business can tolerate.",[28,673,674,677],{},[668,675,676],{},"Recovery Time Objective (RTO)"," bounds acceptable downtime. It answers \"how long can we be down,\" measured as the maximum acceptable delay between an outage starting and service being restored. An RTO of 1 hour means the entire recovery process, detecting the failure, standing up infrastructure, restoring data, has to finish inside that hour.",[28,679,680],{},"An e-commerce order database with a 15-minute RPO and a 1-hour RTO is promising 2 very different things: at most 15 minutes of lost orders, and at most 1 hour before the store is taking orders again. Meeting both takes deliberate engineering, and meeting them more tightly always costs more.",[23,682,684],{"id":683},"backups-the-foundation-under-every-strategy","Backups: the foundation under every strategy",[28,686,687],{},"No disaster recovery strategy works without reliable backups underneath it. AWS Backup centralizes that work across services, EC2, RDS, DynamoDB, EFS, and more, through backup plans: policies that define schedules and retention. A typical plan might pair a daily rule, backing up every night and keeping each backup for 1 month, with a monthly rule, backing up once a month and keeping that copy for a full year. Resources get assigned to a plan simply by tagging them, and backups can copy automatically to a different AWS Region on the same schedule, which is what turns a routine backup into the raw material a disaster recovery strategy can actually use.",[23,689,691],{"id":690},"the-4-disaster-recovery-strategies-cheapest-to-most-expensive","The 4 disaster recovery strategies, cheapest to most expensive",[28,693,694],{},"With backups as the foundation, the question becomes how much infrastructure you keep running in a secondary location, and readiness costs money whether or not disaster ever strikes.",[71,696,697,713],{},[74,698,699],{},[77,700,701,704,707,710],{},[80,702,703],{},"Strategy",[80,705,706],{},"What runs in the secondary Region before a disaster",[80,708,709],{},"Relative cost",[80,711,712],{},"Relative RTO/RPO",[90,714,715,729,743,757],{},[77,716,717,720,723,726],{},[95,718,719],{},"Backup and restore",[95,721,722],{},"Nothing; only backups exist there",[95,724,725],{},"Lowest",[95,727,728],{},"Slowest, highest data loss risk",[77,730,731,734,737,740],{},[95,732,733],{},"Pilot light",[95,735,736],{},"Core data replicating live; compute stays off until failover",[95,738,739],{},"Low",[95,741,742],{},"Faster, but infrastructure still has to be switched on and scaled",[77,744,745,748,751,754],{},[95,746,747],{},"Warm standby",[95,749,750],{},"A scaled-down but fully running copy of the whole stack",[95,752,753],{},"Moderate",[95,755,756],{},"Faster still; only needs to scale up, not deploy from scratch",[77,758,759,762,765,768],{},[95,760,761],{},"Multi-site active/active",[95,763,764],{},"The full stack, already serving live production traffic",[95,766,767],{},"Highest",[95,769,770],{},"Fastest; failover is rerouting traffic, not building anything",[28,772,773],{},"Backup and restore rebuilds everything, data and infrastructure, from scratch after a disaster is declared, usually through Infrastructure as Code, which is why it takes the longest but costs the least day to day. Pilot light keeps the data layer continuously replicated but leaves the rest of the stack dark, needing to be switched on and scaled before it can serve traffic. Warm standby goes further, keeping a smaller version of the entire application already running, so recovery means scaling an existing deployment up rather than deploying one for the first time. Multi-site active/active removes the switching step entirely: more than 1 Region is already handling production traffic, so a Region-level failure just means rerouting requests away from the Region that can't serve them.",[23,775,777],{"id":776},"worked-example-matching-the-strategy-to-the-requirement","Worked example: matching the strategy to the requirement",[28,779,780],{},"An internal reporting dashboard can tolerate several hours of downtime and losing up to a day of data without hurting the business in any measurable way; backup and restore is not just acceptable here, it's the correct engineering choice, since paying for a warmer strategy would be spending money the requirement never asked for. A payment-authorization service is the opposite case: a minute of downtime or any lost transaction is unacceptable, which justifies the cost and complexity of multi-site active/active. Neither team is wrong. Each has matched the strategy to the RTO and RPO the business actually needs, which is the entire point of having 4 strategies instead of reaching for the most expensive one by default.",[23,782,784],{"id":783},"boundary-backup-and-restore-versus-pilot-light","Boundary: backup and restore versus pilot light",[28,786,787],{},"These 2 are the pair most often confused, because both keep your data safe in a second Region and both look, from the outside, like \"we have backups somewhere else.\" The difference is what happens to compute. Backup and restore has no infrastructure standing by at all; everything gets provisioned from Infrastructure as Code only after a disaster is declared. Pilot light keeps a minimal core, often just the data tier, continuously live and replicating, while the rest of the stack (application servers, for instance) stays completely dark until failover triggers it. That single difference, whether anything besides data is already there waiting, is what separates the cheapest strategy from the next one up.",[23,789,791],{"id":790},"active-active-reappears-at-a-larger-radius","Active-active reappears, at a larger radius",[28,793,794],{},"Multi-site active/active is not a new idea introduced for disaster recovery specifically. It's the active-active redundancy pattern from the previous lesson, where every replica serves traffic simultaneously, stretched from the scale of Availability Zones inside 1 Region to the scale of entire Regions. The mechanism is the same; only the geography and the stakes are bigger.",[23,796,798],{"id":797},"misconception-backups-alone-are-not-a-disaster-recovery-plan","Misconception: backups alone are not a disaster recovery plan",[28,800,801],{},"It's tempting to treat \"we have automated nightly backups in a second Region\" as the same thing as \"we have a disaster recovery plan.\" It isn't. A backup that has never been restored from is a hypothesis about how recovery would go, not evidence that it will. A real plan has a documented, rehearsed restore procedure and a known RTO that leadership has actually signed off on as acceptable. Backups are necessary for every one of the 4 strategies above; they are sufficient for none of them on their own.",[23,803,805],{"id":804},"exam-cues-matching-a-scenario-to-a-strategy","Exam cues: matching a scenario to a strategy",[71,807,808,816],{},[74,809,810],{},[77,811,812,814],{},[80,813,218],{},[80,815,221],{},[90,817,818,825,832,839,846,854],{},[77,819,820,823],{},[95,821,822],{},"\"Cost-sensitive,\" \"can tolerate hours or days of downtime\"",[95,824,719],{},[77,826,827,830],{},[95,828,829],{},"\"Core data must stay current, but full infrastructure cost is a concern\"",[95,831,733],{},[77,833,834,837],{},[95,835,836],{},"\"Needs faster recovery than pilot light, already-running but scaled-down\"",[95,838,747],{},[77,840,841,844],{},[95,842,843],{},"\"Cannot tolerate any noticeable downtime, cost is secondary\"",[95,845,761],{},[77,847,848,851],{},[95,849,850],{},"\"Maximum acceptable data loss\"",[95,852,853],{},"RPO",[77,855,856,859],{},[95,857,858],{},"\"Maximum acceptable downtime\"",[95,860,861],{},"RTO",[23,863,268],{"id":267},[28,865,866],{},"Choosing a disaster recovery strategy is choosing a point on a dial between steady-state cost and recovery speed, not searching for a single best answer; the right point is wherever the business's actual RTO and RPO land, no more expensive than that. Backups make every strategy possible, but only a tested restore procedure turns them into an actual plan. The next lesson closes out this topic by asking a different question: how do you actually find out a failure happened at all, and what does a cloud provider's SLA really promise when it does?",{"title":273,"searchDepth":274,"depth":274,"links":868},[869,870,871,872,873,874,875,876,877,878],{"id":653,"depth":277,"text":654},{"id":660,"depth":277,"text":661},{"id":683,"depth":277,"text":684},{"id":690,"depth":277,"text":691},{"id":776,"depth":277,"text":777},{"id":783,"depth":277,"text":784},{"id":790,"depth":277,"text":791},{"id":797,"depth":277,"text":798},{"id":804,"depth":277,"text":805},{"id":267,"depth":277,"text":268},[],[643,644,645,646],{},"/courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/03-backup-and-disaster-recovery",{"passingScore":302,"questions":884},[885,893,901,909,918,926,930,938],{"question":886,"type":306,"options":887,"correctAnswer":889,"explanation":892},"A company's Recovery Point Objective for its order database is 15 minutes. What does this actually specify?",[888,889,890,891],"The system must be back online within 15 minutes of an outage","The company can afford to lose at most 15 minutes of data since the last recoverable backup or replica","Backups must be tested every 15 minutes","The database must respond to queries within 15 minutes","RPO bounds acceptable data loss, measured backward in time from the moment of failure to the last point you could recover from. A 15-minute RPO means backups or replication have to happen at least every 15 minutes; anything less frequent would risk losing more data than the objective allows. It says nothing about how fast the system comes back online, which is what RTO measures instead.",{"question":894,"type":306,"options":895,"correctAnswer":897,"explanation":900},"Which of the following correctly matches a disaster recovery strategy to its defining characteristic?",[896,897,898,899],"Pilot light: multiple regions actively serve production traffic at the same time","Backup and restore: infrastructure is rebuilt from backups and Infrastructure as Code only after a disaster is declared","Warm standby: no infrastructure exists in the secondary region until failover begins","Multi-site active/active: a minimal, idle copy of core infrastructure sits ready in a secondary region","Backup and restore is the cheapest and slowest strategy precisely because nothing runs in the secondary region until a disaster actually happens; everything, data and infrastructure alike, gets rebuilt on demand. Pilot light keeps core data live with idle infrastructure ready to switch on; warm standby keeps a scaled-down version already running; multi-site active/active keeps multiple regions actively serving traffic simultaneously.",{"question":902,"type":306,"options":903,"correctAnswer":905,"explanation":908},"Rank the 4 disaster recovery strategies from lowest cost and slowest recovery to highest cost and fastest recovery.",[904,905,906,907],"Multi-site active/active, warm standby, pilot light, backup and restore","Backup and restore, pilot light, warm standby, multi-site active/active","Pilot light, backup and restore, multi-site active/active, warm standby","Warm standby, backup and restore, multi-site active/active, pilot light","The 4 strategies form a spectrum: backup and restore is cheapest and slowest because nothing runs until disaster strikes, and each strategy after it keeps progressively more infrastructure already running in the secondary region, trading higher steady-state cost for progressively lower RTO and RPO, until multi-site active/active reaches the fastest recovery by already serving live traffic from more than 1 region.",{"question":910,"type":331,"options":911,"correctAnswers":916,"explanation":917},"Which of the following are true about AWS Backup? (Select all that apply.)",[912,913,914,915],"A backup plan can define multiple rules, such as a daily rule with a 1-month retention and a monthly rule with a 1-year retention","It only supports Amazon EC2 instances, not managed services like RDS or DynamoDB","Resources are commonly assigned to a backup plan by tagging them","It can copy backups to a different AWS Region as part of a scheduled plan",[912,914,915],"AWS Backup centralizes backup policy across many AWS services, including RDS, DynamoDB, EFS, and Storage Gateway alongside EC2, so limiting it to EC2 alone is incorrect. Tag-based assignment, multi-rule plans with independent retention periods, and cross-Region copy are all real features, and cross-Region copy specifically is what makes AWS Backup useful as the data layer underneath a disaster recovery strategy, not just routine data protection.",{"question":919,"type":306,"options":920,"correctAnswer":922,"explanation":925},"A company has automated backups running every night and stores them safely in a second AWS Region. Its leadership considers this a complete disaster recovery plan. What is missing from that conclusion?",[921,922,923,924],"Nothing; backups alone are sufficient for any disaster recovery plan","A tested, documented restore procedure with a known RTO, not just the existence of backups","The backups need to be stored in the same Region to count as a disaster recovery plan","Disaster recovery plans cannot use automated backups at all","Backups are the foundation every disaster recovery strategy sits on, but a backup nobody has ever restored from is a hypothesis, not a plan. A real plan has a tested restore procedure, a documented and rehearsed set of steps, and a known RTO that leadership has actually agreed is acceptable, not just confidence that data exists somewhere safe.",{"question":927,"type":306,"options":928,"correctAnswer":342,"explanation":929},"Multi-site active/active disaster recovery is the same concept as active-active redundancy across Availability Zones from the previous lesson, applied at a larger scale.",[342,343],"The active-active pattern doesn't change between the 2 lessons, only its radius does. Within a Region, active-active means multiple Availability Zones simultaneously serve traffic. Multi-site active/active stretches the same idea across multiple Regions, so that even losing an entire Region leaves other Regions already serving requests, with failover reduced to rerouting traffic rather than starting anything from scratch.",{"question":931,"type":306,"options":932,"correctAnswer":934,"explanation":937},"An internal reporting dashboard can tolerate several hours of downtime and the loss of up to a day's data without meaningfully hurting the business. A payment-authorization service cannot tolerate more than a minute of downtime or any data loss at all. Which strategy pairing best fits each system?",[933,934,935,936],"Both systems should use multi-site active/active, since it is always the safest choice","The dashboard fits backup and restore; the payment service fits multi-site active/active","The dashboard fits multi-site active/active; the payment service fits backup and restore","Both systems should use pilot light, since it is a balanced middle option","Matching the strategy to the actual RTO and RPO the business needs, not defaulting to the most expensive option everywhere, is the point of having 4 strategies instead of 1. The dashboard's generous tolerances make the cheapest strategy the right engineering choice; the payment service's near-zero tolerance justifies the cost and complexity of running live in more than 1 Region at once.",{"question":939,"type":306,"options":940,"correctAnswer":942,"explanation":945},"Why is a low RTO and RPO more expensive to achieve than a higher one?",[941,942,943,944],"Cloud providers charge a fixed penalty fee for choosing a low RTO","Achieving a low RTO and RPO generally requires running more infrastructure continuously, in more than 1 location, rather than building it only after a disaster occurs","Low RTO and RPO values are only a marketing distinction with no real infrastructure difference","It is not more expensive; all 4 strategies cost the same to operate","Every step from backup and restore toward multi-site active/active trades a lower steady-state cost for a slower recovery, or a higher steady-state cost for a faster one. There is no way to get instant, lossless recovery without something already running and ready to take over, and that something bills you whether or not a disaster ever happens.",{"title":639,"description":640},"courses/cloud-computing-fundamentals/en/domains/03-security-and-reliability/02-reliability-and-operations/03-backup-and-disaster-recovery","AsZCE0m54MMgw_6Y7GFMwlnG2NgZ1_KgsnK7mzsHq3Y"]