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10

Java & Spring Boot

1.Java Memory Model Foundations: Visibility, Ordering, and Safe Publication
2.ConcurrentHashMap Internals: Coordinating Updates Without One Global Lock
3.Spring Transaction Boundaries: Where to Commit and When to Split
4.JVM Memory Errors: Diagnose Heap Exhaustion, StackOverflow, and Leaks
5.Java Thread Dumps: Find Deadlocks, Starvation, and Lock Contention
6.Java equals and hashCode: Fix Broken HashMap and HashSet Lookups
7.Java Exception Debugging: Trace the Root Cause and Fix the Boundary
8.Advanced Java and Spring: Runtime, Concurrency, Proxies, and Reactive Failure Modes
9.Java Appointment Times: Resolve DST Gaps and Overlaps Explicitly
10.Java deadlines: spend one budget across every stage
11.Java class-loader retention: why an undeployed app can remain in memory

Python & Async APIs

1.Python Data Model Foundations: Objects, Types, and Reliable Boundaries
2.Python Collections: Lists, Tuples, Sets, and Map/Reduce Trade-offs
3.Python Error Debugging: Tracebacks, Root Causes, and Safe Fixes
4.FastAPI Async Patterns: When to Use Background Tasks vs Real Concurrency
5.FastAPI Backpressure: Protecting Your Service from Unbounded Fan-Out
6.Async Python Cancellation and Timeouts: Stop Work Without Leaking It
7.Debugging Flaky Python Tests: Time, State, Concurrency, and Isolation
8.Python Async Runtime Advanced: GIL, ASGI Lifecycles, and Safe Concurrency
9.Python Runtime Advanced: Native Memory, ASGI Lifecycles, and Worker Failure
10.Python Event Times: Normalize Offsets Without Losing Elapsed Time
11.Python queues: bound waiting work and own its completion
12.Python TaskGroups: make sibling failure and cleanup part of the design

Security & Authentication

1.Authentication Foundations: Sessions, Cookies, and Trust Boundaries
2.Security Boundaries: Object Authorization, Input Validation, and Secret Handling
3.Tenant authorization: carry trusted scope into every lookup
4.CSRF defenses: reason from browser credential attachment, not CORS

Distributed Systems

1.Distributed Systems Foundations: Consistency, Retries, and Delivery Semantics
2.Kafka Consumer Groups: Architecture & Common Pitfalls
3.Idempotency and the Transactional Outbox: Making Retries Safe
4.Distributed Systems Advanced: Rebalancing, Sagas, Consensus, and Trace Context
5.Consensus and Observability Advanced: Quorums, Split Brain, and Trace Context
6.Event-Time Windows: Watermarks, Late Data, and Correctable Results
7.Fencing tokens: reject workers whose lease has expired
8.Causal order: what a logical clock can and cannot tell you

DevOps & Delivery

1.DevOps Foundations: Reproducible Builds and Artifact Promotion
2.DevOps Foundations: Safe Changes, Health Signals, and Rollback Boundaries
3.Docker Multi-Stage Builds: Optimization Patterns for Production
4.Production Observability: Signals, Service Objectives, and Useful Alerts
5.Reliable CI Pipelines: Fast Feedback Without Hiding Failures
6.Delivery Engineering Advanced: Build Caches, Runner Isolation, and Safe Releases
7.Release Safety Advanced: State, Schema Compatibility, and Artifact Trust
8.Incident Timelines: Separate Event Time, Receipt Time, and Clock Evidence
9.Scheduled jobs: separate a logical run from its attempts
10.CI workload identity: replace copied cloud keys with scoped claims

Databases & SQL

1.Relational Databases and SQL Foundations: Model the Invariant First
2.PostgreSQL MVCC and Indexes: Reads, Vacuum, and Query Plans
3.MySQL InnoDB: Transactions, Indexes, and Reading EXPLAIN
4.Oracle SQL: Analytic Functions, Execution Plans, and Bind Variables
5.SQL Query Recipes: Joins, Grouping, Window Functions, and Safe Pagination
6.Top-N and Third-Highest Salary Queries in PostgreSQL, MySQL, and Oracle
7.Stored Functions, Procedures, and Triggers: Put Logic in the Right Layer
8.Database Locks and Isolation: Diagnose Blocking, Deadlocks, and Retries
9.Fix Slow SQL: N+1 Queries, Missing Indexes, and Bad Cardinality Estimates
10.Database Internals Advanced: MVCC Anomalies, Cost Models, Sharding, and Storage Engines
11.SQL Event Gaps: LAG, Stable Ordering, and Explicit Window Frames
12.SQL keyset pagination: continue an order without promising a snapshot
13.PostgreSQL concurrent indexes: reason about locks, retries, and invalid states

Unix & Linux

1.Unix Foundations: File Permissions, Ownership, and Descriptors
2.grep, sed, and awk: Reliable Text Search and Small Stream Programs
3.Shell Script Debugging: Trace Failures, Preserve Exit Codes, and Fix Safely
4.TCP, UDP, ss, netstat, and tcpdump: A Layered Network Debugging Guide
5.pipefail, Signals, and Unix Process Management
6.Unix Signals and Child Processes: What Your Service Must Clean Up
7.DNS Troubleshooting: Trace Name Resolution Before Blaming the Network
8.Linux Disk Full Errors: Blocks, Inodes, Deleted Files, and Safe Cleanup
9.Linux Under Load: Epoll, File Descriptors, OOM, TCP Queues, and VFS
10.Unix Log Sorting: Normalize Offset Timestamps Before Ordering Events
11.Unix file publication: replace names without exposing partial bytes
12.Unix process inheritance: environment variables and file descriptors

HTTP & APIs

1.HTTP Foundations: Methods, Status Codes, and Representation Boundaries
2.Reliable API Clients: Pagination, Conditional Requests, and Retry Budgets
3.HTTP conditional writes: detect an edit based on stale state
4.HTTP range requests: resume transfers without stitching different files

Kubernetes & Platform

1.Kubernetes Foundations: Pods, Workloads, and Declarative State
2.Kubernetes Foundations: Services, Configuration, and Least-Privilege Access
3.Kubernetes Foundations: Objects, Controllers, and Reconciliation
4.Kubernetes Rollouts: Readiness, Health Checks, and Safe Deployments
5.Kubernetes Requests, Limits, and Autoscaling: A Practical Capacity Model
6.Kubernetes Advanced Operations: Packet Paths, Rollouts, DNS, and Operators
7.Kubernetes CronJobs: Timezones, Missed Starts, and Duplicate Work
8.Kubernetes disruption budgets: protect capacity, not every failure
9.Kubernetes finalizers: deletion is a protocol, not a cleanup callback

TypeScript & Node.js

1.TypeScript Foundations: Narrow Unknown Data Before Trusting It
2.Node.js Foundations: Event Loop Work, Backpressure, and Cancellation
3.TypeScript results: distinguish absence, invalid input, and dependency failure
4.TypeScript async iterators: connect pull-based demand to resource cleanup

Learn / Kubernetes & Platform quiz

Kubernetes & Platform quiz

A focused round on this track. Submit to see your score and explanations, then return to another subject whenever you like.

Choose another subject

Question 1

Kubernetes1 / 9

What does a successful kubectl apply prove?

Every Pod is readyThe application passed its business checksThe API accepted the desired object; reconciliation may still failThe image was verified and downloaded

Question 2

Kubernetes2 / 9

Does updating a ConfigMap change environment variables in an already running container?

Always immediatelyNo; environment values are established when the container startsOnly if the Pod is ReadyOnly in the default namespace

Question 3

Kubernetes3 / 9

When should a controller remove its finalizer?

Immediately when deletion is requestedAfter its idempotent external cleanup is confirmedWhenever the object is older than one hourWhen any controller reports ready

Question 4

Kubernetes4 / 9

A PDB requires two healthy pods and currently has exactly two. What should a supported voluntary eviction do?

Proceed because both are healthyIgnore the budget during drainGuarantee a replacement nodeRefuse an eviction that violates the minimum

Question 5

Kubernetes5 / 9

What should happen when a Kubernetes readiness probe fails?

The container must always restartThe pod is removed from service endpoints until it is ready againThe deployment is rolled back immediatelyThe node is marked unhealthy

Question 6

Kubernetes6 / 9

What does a Kubernetes controller do when actual state differs from desired state?

Runs the manifest once and exitsRepeatedly observes and reconciles toward desired stateRewrites every pod directlyGuarantees dependent databases have capacity

Question 7

Kubernetes7 / 9

What is a likely consequence of exceeding a container memory limit?

CPU is throttled but the process always continuesThe container may be OOM-killedThe pod is automatically given a larger requestReadiness becomes permanently true

Question 8

Kubernetes8 / 9

What is a common effect of reaching a container CPU limit?

The container is immediately OOM-killedThe process is throttled, which may increase latencyThe pod is automatically rescheduledIts memory request is increased

Question 9

Kubernetes9 / 9

After a client reaches a NodePort, what normally selects a backing pod endpoint?

The Service dataplane rules, then node/CNI routing into the pod network namespaceThe container image registryCoreDNS directly chooses a pod for every packetThe Deployment rewrites the client packet in user space

Answer all 9 questions to see your score.