Cloud
189 bites tagged Cloud — interview questions with model answers, and 60-second explainers.
How cloud DNS resolves a URL to an IP
Recursive resolver queries root, then TLD, then your authoritative cloud DNS zone; the matching record returns an IP, cached per TTL. end-to-end DNS resolution flow. skipping the recursive resolver or caching.
Layer 4 vs Layer 7 load balancers
L4 routes on IP and TCP/UDP ports fast and protocol-blind; L7 inspects HTTP for host, path, and headers. OSI model and load balancer routing knowledge. claiming L7 is always better.
What trade-offs decide managed ML platforms versus open-source Kubernetes?
Weigh total cost plus hidden engineering headcount, lock-in vs flexibility, and audit feature gaps. Ops overhead vs speed for ML infra. Recommending open-source purely to cut cost while ignoring the 2-4 person tax.
How does shared responsibility shift between IaaS and SaaS?
Tests your understanding of security ownership across cloud stacks. Strong answer: in IaaS you own OS, apps, and network controls; in SaaS you only own data, identities, endpoints, and accounts while the provider manages the rest.
Explain the difference between IaaS, PaaS, and SaaS with examples
This tests your grasp of cloud abstraction layers and shared responsibility. A strong answer maps IaaS to raw infrastructure like EC2, PaaS to managed runtimes like Heroku, and SaaS to end-user apps like Gmail.
AWS SDK: Code That Operates Your Cloud
The AWS SDK turns AWS API calls into native code so your app can command S3 or DynamoDB directly. It handles auth, retries, and formatting automatically. The footgun: forgetting region or credentials causes silent failures that look like network errors.
Customer Reliability Engineering: SRE for Your Customers
CRE extends SRE principles to your customers, making their reliability a shared goal. A provider partners with a key customer to co-engineer resilient services on their platform.
Horizontal Scaling: Add More Machines, Not Bigger Ones
Horizontal scaling (scaling out) means handling more load by adding more machines to your resource pool, not upgrading a single one. It’s used for web servers behind a load balancer. The footgun is that your app must be stateless to avoid losing user data.
Cloud Cost Anomaly Detection: Finding Waste
Think of it as a smoke detector for your cloud bill, distinguishing 'good' growth-related costs from 'bad' waste. It automatically flags unexpected spending spikes from misconfigurations or bugs.
Reserved Instances vs. Savings Plans: Pre-pay for Cloud Discounts
Think of Reserved Instances and Savings Plans as buying cloud compute in bulk for a discount. You commit to a certain usage level for 1-3 years to save money on steady-state workloads like production databases.
Data Storage Tiering: Pay Only for the Access You Need
Treat data like items in a house: hot, frequently used data on the counter; cool, less-used data in the pantry. Cloud providers use this to price storage, letting you move old logs to cheaper tiers.
Stop Paying for Idle Cloud Resources
Stop paying for idle cloud servers. Automated shutdown is like turning off the lights in an empty office, running compute only when needed. It's ideal for dev environments or scheduled batch jobs. The footgun is applying this to stateful production services.
Showback vs. Chargeback: Who Pays for Compute?
Showback tells teams what their resource usage costs; Chargeback makes them pay for it. It's the difference between a receipt and a bill. These models help manage cloud costs, but implementing chargeback without granular tracking leads to disputes.
Cloud Pricing: On-Demand, Reserved, and Spot Instances
Cloud pricing is like booking a flight. Pay full price for flexibility (On-Demand), get a discount for committing (Reserved), or bid on empty seats for a huge discount but risk getting bumped (Spot). This choice dictates your infrastructure cost.
Cloud Cost Allocation: Making Teams Own Their Spend
Cloud cost allocation answers "who pays for what?" by assigning every dollar of your cloud bill to a team or project. It uses metadata like tags and account structures to create showback reports.
FinOps: Making Cloud Costs Everyone's Job
FinOps makes cloud cost a shared responsibility between engineering, finance, and business teams. It applies the variable, on-demand nature of the cloud to financial accountability.
Azure Machine Learning: A Service on Microsoft's Cloud
Microsoft Azure is a general-purpose cloud platform for building applications. It provides the global infrastructure and tooling support upon which specialized services, like Azure Machine Learning, are built.
MLaaS: Your Machine Learning Lab in the Cloud
Machine Learning as a Service (MLaaS) provides the key ingredients for ML—data, compute, and expertise—as a cloud service. This lets teams build models for forecasting or spam detection without buying expensive hardware.
PaaS: The Managed Platform for Building Applications
PaaS gives you a ready-to-use development environment, handling the OS and middleware so you can just code. It's used to accelerate app development for web, IoT, or ML. The main footgun is vendor lock-in, making future platform migrations difficult.
Autoscaling ML Inference Endpoints
Autoscaling matches your ML model's compute to real-time demand, like an elastic container for your inference service. It handles spiky traffic for online endpoints, scaling up for peaks and down to save costs.
Serverless Inference: Run ML Models Without Managing Servers
Serverless inference treats ML prediction like a function call, abstracting away servers. You pay for compute time per prediction, not for idle infrastructure.
Slash ML Training Costs with Spot Instances
Spot Instances are like flying standby for compute: you get a massive discount but can be 'bumped' if someone pays full price. Use them for ML training jobs that can be paused and resumed. The footgun is using them for tasks that can't tolerate interruption.
MaaS: Renting AI Brains via API
Model-as-a-Service (MaaS) is like renting a pre-trained AI expert via an API. Instead of building and training your own models, you pay to use powerful, ready-made ones for tasks like text generation or image analysis.
Amazon EC2: Rentable Virtual Servers on AWS
Amazon EC2 is like renting virtual computers, letting you run applications without buying physical hardware. It's used for scalable deployments where you can launch and terminate servers as needed, paying only for what you use.
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