[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"me":3,"catalog:en:devops-cloud\u002Flinux-performance-monitoring-resource-limits":4,"config":256},null,{"field_key":5,"field_name":6,"seniority":7,"topic_key":8,"topic_name":9,"spec_key":7,"spec_name":7,"locale":10,"cell_total":11,"field_total":12,"seniorities":13,"topics":17,"specs":151,"samples":171},"devops-cloud","DevOps \u002F Cloud","","linux-performance-monitoring-resource-limits","Linux Performance Monitoring Resource Limits","en",75,3375,[14,15,16],"junior","mid","senior",[18,21,24,27,30,33,36,39,42,45,48,51,54,57,60,63,66,69,72,75,78,81,84,87,90,93,96,99,102,105,108,111,112,115,118,121,124,127,130,133,136,139,142,145,148],{"key":19,"name":20,"count":11},"aws-compute-ec2-lambda","Aws Compute Ec2 Lambda",{"key":22,"name":23,"count":11},"aws-databases-rds-dynamodb","Aws Databases Rds Dynamodb",{"key":25,"name":26,"count":11},"aws-iam-security","Aws Iam Security",{"key":28,"name":29,"count":11},"aws-messaging-eventing","Aws 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Cicd",[152,156,159,162,165,168],{"key":153,"name":154,"count":155},"aws","AWS",450,{"key":157,"name":158,"count":155},"azure","Azure",{"key":160,"name":161,"count":155},"gcp","GCP",{"key":163,"name":164,"count":155},"kubernetes","Kubernetes",{"key":166,"name":167,"count":155},"linux","Linux",{"key":169,"name":170,"count":155},"terraform","Terraform",[172,190,203,217,230,243],{"id":173,"topic":9,"difficulty":174,"body":175,"options":176,"correct_key":178,"explanation":189},"019f913c-22b0-741e-819d-d9d8ba2520f6",1,"In the output of `uptime` or `top`, three load average numbers are shown, e.g. `load average: 2.15, 1.80, 1.42`. What do these three numbers represent?",[177,180,183,186],{"key":178,"text":179},"a","The average number of processes runnable or waiting on I\u002FO over the last 1, 5, and 15 minutes, in that order",{"key":181,"text":182},"b","The CPU temperature readings taken 1, 5, and 15 minutes ago. In practice, this framing does not match how the mechanism is documented to behave.",{"key":184,"text":185},"c","The percentage of total RAM used 1, 5, and 15 minutes ago. This description sounds intuitive but conflicts with how the underlying system actually operates.",{"key":187,"text":188},"d","The number of users logged into the system in the last 1, 5, and 15 minutes. Many engineers assume this at first, but it does not hold up once the tool's real behavior is checked.","The three load average figures are exponentially-weighted moving averages of the number of processes in the runnable or uninterruptible-sleep (I\u002FO-wait) state, sampled over the last 1, 5, and 15 minutes respectively. Comparing the three lets an operator see whether load is rising, falling, or steady.",{"id":191,"topic":9,"difficulty":174,"body":192,"options":193,"correct_key":181,"explanation":202},"019f913c-22b0-7e7c-8a66-d225354c9b77","A server has 8 CPU cores and `uptime` reports a load average of `4.00, 3.80, 3.50`. What is the most reasonable interpretation?",[194,196,198,200],{"key":178,"text":195},"The system is critically overloaded, since any load average above 1.0 means the CPU is saturated. This description sounds intuitive but conflicts with how the underlying system actually operates.",{"key":181,"text":197},"The system has headroom, since load averages roughly track demand for runnable\u002Fwaiting work relative to the number of cores, and 4 is well below the 8 cores available",{"key":184,"text":199},"The load average is meaningless on multi-core systems and should always be ignored. Many engineers assume this at first, but it does not hold up once the tool's real behavior is checked.",{"key":187,"text":201},"The server is definitely swapping heavily, since load average only rises due to memory pressure. This is a common shortcut explanation that breaks down under closer technical scrutiny.","As a rule of thumb, a load average close to (or below) the core count means demand is roughly keeping up with available CPU capacity; here 4 out of 8 cores suggests moderate, non-saturating demand. (a) wrongly applies a single-core rule of thumb to an 8-core box; (c) and (d) both misstate what the metric captures.",{"id":204,"topic":9,"difficulty":205,"body":206,"options":207,"correct_key":184,"explanation":216},"019f913c-22b7-76e0-a882-39b34f9f77af",2,"In `top`'s CPU summary line: `%Cpu(s): 22.0 us, 5.0 sy, 0.0 ni, 70.0 id, 3.0 wa, 0.0 hi, 0.0 si, 0.0 st`, what do `us` and `sy` represent?",[208,210,212,214],{"key":178,"text":209},"`us` is time spent in the USB subsystem, `sy` is time spent syncing disks. Many engineers assume this at first, but it does not hold up once the tool's real behavior is checked.",{"key":181,"text":211},"`us` is uptime seconds, `sy` is system boot count. This is a common shortcut explanation that breaks down under closer technical scrutiny.",{"key":184,"text":213},"`us` is the percentage of CPU time spent running user-space processes, `sy` is the percentage spent running kernel (system) code on their behalf",{"key":187,"text":215},"`us` and `sy` are two different ways of naming the exact same idle-time measurement. It is a plausible-sounding guess rather than something confirmed by the kernel or tool documentation.","`us` (user) tracks CPU cycles spent executing normal user-space program code, while `sy` (system) tracks cycles spent in the kernel handling system calls, interrupts, and other kernel-side work triggered by those processes. Together with `id` (idle) and `wa` (iowait) they should sum to roughly 100%.",{"id":218,"topic":9,"difficulty":174,"body":219,"options":220,"correct_key":187,"explanation":229},"019f913c-22b9-7886-a9ad-0ad8ebc7de1d","In `top`, what does the `%wa` (iowait) figure in the CPU summary line indicate?",[221,223,225,227],{"key":178,"text":222},"The percentage of CPU cores that are physically powered off. This is a common shortcut explanation that breaks down under closer technical scrutiny.",{"key":181,"text":224},"The percentage of processes waiting for a user to type input on the keyboard. It is a plausible-sounding guess rather than something confirmed by the kernel or tool documentation.",{"key":184,"text":226},"The percentage of time the CPU spends running virtualization\u002Fhypervisor code. In practice, this framing does not match how the mechanism is documented to behave.",{"key":187,"text":228},"The percentage of time the CPU was idle while at least one process was waiting on a pending disk (or other block) I\u002FO operation to complete","`%wa` measures idle CPU time during which the CPU had nothing to execute specifically because outstanding I\u002FO requests hadn't completed yet. A high and sustained `%wa` is a classic sign that disk (or network storage) I\u002FO, not CPU, is the bottleneck.",{"id":231,"topic":9,"difficulty":174,"body":232,"options":233,"correct_key":178,"explanation":242},"019f913c-22ba-7404-86be-fcf5d1b99777","What is the most commonly cited practical difference between `top` and `htop` for a Linux operator?",[234,236,238,240],{"key":178,"text":235},"`htop` provides an interactive, scrollable, color-coded interface (per-core bars, easier process search\u002Fkill\u002Fsort) built on top of the same underlying data `top` shows",{"key":181,"text":237},"`top` only works on remote servers, while `htop` only works on the local desktop. It is a plausible-sounding guess rather than something confirmed by the kernel or tool documentation.",{"key":184,"text":239},"`htop` reads a completely different, more accurate kernel data source than `top`, so their numbers frequently disagree. In practice, this framing does not match how the mechanism is documented to behave.",{"key":187,"text":241},"`top` cannot show memory usage at all, only `htop` can. This description sounds intuitive but conflicts with how the underlying system actually operates.","Both tools read the same kernel-exposed process\u002FCPU\u002Fmemory data (mainly via `\u002Fproc`); `htop` just presents it more conveniently — a scrollable, colorized, mouse-friendly view with per-core CPU bars and easier interactive process management. `top` is more minimal but ships virtually everywhere by default.",{"id":244,"topic":9,"difficulty":205,"body":245,"options":246,"correct_key":181,"explanation":255},"019f913c-22ba-7e20-a41f-167dbd48caaa","Given `free -h` output:\n```\n              total        used        free      shared  buff\u002Fcache   available\nMem:           15Gi       2.1Gi       500Mi       200Mi        12Gi        12Gi\n```\nWhy is `available` (12Gi) so much larger than `free` (500Mi)?",[247,249,251,253],{"key":178,"text":248},"It's a bug; `free` and `available` should always be equal on a healthy system. In practice, this framing does not match how the mechanism is documented to behave.",{"key":181,"text":250},"Most of the memory is used for buffer\u002Fcache, which the kernel can reclaim on demand for applications, so `available` accounts for that reclaimable memory while `free` only counts completely unused memory",{"key":184,"text":252},"`available` includes swap space, while `free` does not count swap at all. This description sounds intuitive but conflicts with how the underlying system actually operates.",{"key":187,"text":254},"`available` is measured in a different unit than `free`, which is why the numbers differ. Many engineers assume this at first, but it does not hold up once the tool's real behavior is checked.","Linux aggressively uses spare RAM for the page\u002Fbuffer cache to speed up disk access, since that memory would otherwise sit idle. That cached memory is reclaimable — the kernel can evict it instantly if an application needs RAM — so `available` (free + reclaimable cache) is the more useful figure for judging real memory pressure than the narrow `free` column.",{"fields":257,"seniorities":429,"interview_shapes":430,"locales":435,"oauth":437,"question_count":440,"coach_enabled":441,"jd_match_enabled":441},[258,283,303,320,330,343,362,381,403,410,416,423],{"key":259,"name_tr":260,"name_en":260,"sort":174,"specializations":261},"backend","Backend",[262,265,268,271,274,277,280],{"key":263,"name":264,"field":259},"general","Genel",{"key":266,"name":267,"field":259},"go","Go",{"key":269,"name":270,"field":259},"python","Python",{"key":272,"name":273,"field":259},"java","Java",{"key":275,"name":276,"field":259},"csharp","C#\u002F.NET",{"key":278,"name":279,"field":259},"nodejs","Node.js",{"key":281,"name":282,"field":259},"php","PHP",{"key":284,"name_tr":285,"name_en":285,"sort":205,"specializations":286},"frontend","Frontend",[287,288,291,294,297,300],{"key":263,"name":264,"field":284},{"key":289,"name":290,"field":284},"javascript","JavaScript",{"key":292,"name":293,"field":284},"typescript","TypeScript",{"key":295,"name":296,"field":284},"react","React",{"key":298,"name":299,"field":284},"vue","Vue",{"key":301,"name":302,"field":284},"angular","Angular",{"key":304,"name_tr":305,"name_en":305,"sort":306,"specializations":307},"fullstack","Fullstack",3,[308,309,310,311,312,313,314,315,316,317,318,319],{"key":263,"name":264,"field":304},{"key":266,"name":267,"field":259},{"key":269,"name":270,"field":259},{"key":272,"name":273,"field":259},{"key":275,"name":276,"field":259},{"key":278,"name":279,"field":259},{"key":281,"name":282,"field":259},{"key":289,"name":290,"field":284},{"key":292,"name":293,"field":284},{"key":295,"name":296,"field":284},{"key":298,"name":299,"field":284},{"key":301,"name":302,"field":284},{"key":5,"name_tr":6,"name_en":6,"sort":321,"specializations":322},4,[323,324,325,326,327,328,329],{"key":263,"name":264,"field":5},{"key":153,"name":154,"field":5},{"key":160,"name":161,"field":5},{"key":157,"name":158,"field":5},{"key":163,"name":164,"field":5},{"key":169,"name":170,"field":5},{"key":166,"name":167,"field":5},{"key":331,"name_tr":332,"name_en":332,"sort":333,"specializations":334},"ai-engineer","AI Engineer",5,[335,336,337,340],{"key":263,"name":264,"field":331},{"key":269,"name":270,"field":331},{"key":338,"name":339,"field":331},"llm-rag","LLM\u002FRAG",{"key":341,"name":342,"field":331},"mlops","MLOps",{"key":344,"name_tr":345,"name_en":346,"sort":347,"specializations":348},"database","Veritabanı","Database",6,[349,350,353,356,359],{"key":263,"name":264,"field":344},{"key":351,"name":352,"field":344},"postgresql","PostgreSQL",{"key":354,"name":355,"field":344},"mysql","MySQL",{"key":357,"name":358,"field":344},"mongodb","MongoDB",{"key":360,"name":361,"field":344},"redis","Redis",{"key":363,"name_tr":364,"name_en":365,"sort":366,"specializations":367},"mobile","Mobil","Mobile",7,[368,369,372,375,378],{"key":263,"name":264,"field":363},{"key":370,"name":371,"field":363},"ios-swift","iOS (Swift)",{"key":373,"name":374,"field":363},"android-kotlin","Android (Kotlin)",{"key":376,"name":377,"field":363},"flutter","Flutter",{"key":379,"name":380,"field":363},"react-native","React Native",{"key":382,"name_tr":383,"name_en":384,"sort":385,"specializations":386},"security","Güvenlik","Security",8,[387,388,391,394,397,400],{"key":263,"name":264,"field":382},{"key":389,"name":390,"field":382},"appsec","AppSec",{"key":392,"name":393,"field":382},"offensive-pentest","Offensive \u002F Pentest",{"key":395,"name":396,"field":382},"cloud-security","Cloud Security",{"key":398,"name":399,"field":382},"devsecops","DevSecOps",{"key":401,"name":402,"field":382},"blue-team-incident","Blue Team \u002F Incident",{"key":404,"name_tr":405,"name_en":406,"sort":407,"specializations":408},"qa-test-automation","QA \u002F Test Otomasyonu","QA \u002F Test Automation",9,[409],{"key":263,"name":264,"field":404},{"key":411,"name_tr":412,"name_en":412,"sort":413,"specializations":414},"data-engineer","Data Engineer",10,[415],{"key":263,"name":264,"field":411},{"key":417,"name_tr":418,"name_en":419,"sort":420,"specializations":421},"game-dev","Oyun Geliştirme","Game Development",11,[422],{"key":263,"name":264,"field":417},{"key":424,"name_tr":425,"name_en":425,"sort":426,"specializations":427},"ml-engineer","ML Engineer",12,[428],{"key":263,"name":264,"field":424},[14,15,16],{"junior":431,"mid":433,"senior":434},{"questions":432,"median_sec":3},20,{"questions":432,"median_sec":3},{"questions":432,"median_sec":3},[436,10],"tr",[438,439],"google","github",21750,true]