Anupam

Arsenal,Mumbai Indians.
Learning Devops 🕶️

[Anupam @Anupam_Devops]

Most people prepare for Kubernetes interviews by memorizing definitions.

Real interviews are different.

You’ll be asked things like:

• Why is a pod stuck in Pending?
• Why did the app suddenly start returning 403?
• How would you upgrade EKS without downtime?
• Why is the deployment in CrashLoopBackOff?
• How do you isolate traffic between namespaces?

That’s what actually tests DevOps engineers:
Debugging + production thinking.

This article covers 25 real Kubernetes interview scenarios every DevOps engineer should know.

[Anupam @Anupam_Devops]

https://t.co/nmQChvTtVU

[Anupam @Anupam_Devops]

@darvasboxtrader 😃😃

[Anupam @Anupam_Devops]

@elonmusk UP and Bihar will never let us down... continents within a country 🙌

[Anupam @Anupam_Devops]

@waisenor 😅😅

[Anupam @Anupam_Devops]

@devops_nk Jai Bhavani Jai Shivaji ♥️

[Anupam @Anupam_Devops]

@mallexibra Good points worth looking at. Thanks for sharing.

[Anupam @Anupam_Devops]

Karpenter internals — let's make it concrete.

Two core concepts:

NodePool:

Defines constraints for the nodes Karpenter can provision. Think of it as your "what am I allowed to create?" policy.

NodeClass (EC2NodeClass on AWS)

The cloud-specific config — AMI, subnets, security groups, instance profile.

The genius bit: disruption + consolidation.

Karpenter doesn't just scale up. It continuously asks: "can I pack these workloads onto fewer, cheaper nodes?" If yes, it drains and replaces automatically.

That consolidation line alone can cut your EC2 bill by 20–30%.

[Anupam @Anupam_Devops]

@devops_nk Yeah it really helped us in the prod.

[Anupam @Anupam_Devops]

@egobiiwai @SpursOfficial 😅😅

[Anupam @Anupam_Devops]

@fc8609fifa 😅😅

[Anupam @Anupam_Devops]

@_jaydeepkarale Wow 😃🙌

[Anupam @Anupam_Devops]

@aravind Aur garmi ni bahut hai bhai...biryani khake dharna karna tough hai abi

[Anupam @Anupam_Devops]

@AnupamHaldkar Well done bro 🙌

[Anupam @Anupam_Devops]

One noisy container can ruin performance for every other workload on the same node.

Containers are isolated…
but not as completely as many engineers assume.

The kernel still shares critical resources like:

• disk I/O
• network bandwidth
• CPU caches
• memory pressure

So one aggressive workload can create collateral damage across the node.

Typical example:

A heavy batch job starts massive disk writes…
and suddenly your latency-sensitive API becomes slow for completely unrelated requests.

CPU and memory isolation via cgroups is reasonably mature.

I/O and network isolation are far weaker by default.

That’s why “neighbor noise” still exists heavily in Kubernetes environments.

If workloads have very different performance profiles:

• isolate them with taints and tolerations
• use node affinity
• separate critical services onto dedicated nodes

The scheduler optimizes placement.

It does not guarantee peace between workloads.

[Anupam @Anupam_Devops]

@whoisjeph_ @SpursOfficial 😅🙌

[Anupam @Anupam_Devops]

@hogspur @SpursOfficial 😂😂

[Anupam @Anupam_Devops]

@JamessTheDrinke @SpursOfficial We do we call Shit 😂

[Anupam @Anupam_Devops]

@Khavod_ @SpursOfficial 😂😂

[Anupam @Anupam_Devops]

@SLocks8283 @SpursOfficial Oh yeah 😅

[Anupam @Anupam_Devops]

@richsmith072583 @SpursOfficial Makes sense 😃

[Anupam @Anupam_Devops]

@Anthony_100000 @SpursOfficial Dividends included 😂