Search the practice set
275 questions, 991 terms and 600 topics in 30 areas.
32 results for “Feature Freshness”
Feature Staleness
The age of the feature values a model scores against, and the divergence between how they are computed at training time and at inference time.
Feature Criticality Tiering
Classifying product functionality by whether it must work, should work, or can be dropped, so degradation decisions are made in advance by the business.
Feature Flag
A runtime switch that decouples deploying code from releasing behaviour, so unfinished or risky work can ship dark.
Feature Parity Trap
The expectation that a replacement system must match every behaviour of the old one before it can be adopted, which is what makes rewrites never finish.
Feature Store
A system that computes, stores and serves model input features consistently for both training and inference, eliminating training-serving skew.
Freshness Requirement
How stale data may be before the decision it supports degrades — the only question that justifies streaming over batch.
Booking.com's Experimentation Platform
Booking.com runs over a thousand concurrent experiments and treats the ability to test any change safely as a platform capability rather than a product feature.
Build vs Buy
The choice between developing a capability in-house and acquiring it, decided on differentiation and total cost rather than on feature lists.
Data Catalog
A searchable inventory of datasets with their schema, owner, meaning, freshness, quality and classification.
Error Budget
The amount of unreliability an SLO permits, treated as a resource that feature velocity spends.
Flag Debt
The accumulating complexity of feature flags that are never removed, producing untested code paths and combinatorial behaviour nobody understands.
Flag Evaluation Latency
The delay between page load and the client knowing which variant to show, which produces a visible flicker unless the flag is resolved before render.
Non-Functional Acceptance
Treating quality-attribute targets as acceptance criteria with automated verification, so a release can fail on latency the way it fails on a broken feature.
Pipeline Anomaly Detection
Monitoring row counts, distributions, freshness and schema for unexplained change, because data pipelines fail silently far more often than they error.
Token Budget Enforcement
Limiting token consumption per user, tenant, feature or time window at a central point, so cost cannot run away unobserved.
A 15-year-old monolith must be modernised without a rewrite and without a feature freeze. Plan the migration.
Establish why, before deciding what "Modernise" is not an objective. The reasons that justify the cost are specific: delivery is too slow; the technology is uns
A codebase has 340 feature flags, most of them permanently on. What is the problem and how do you fix it?
The problem is combinatorial and it is not theoretical Every flag doubles the number of possible code paths. At 340, the number of configurations is unbounded a
An AI feature launched two months ago now costs more per month than the rest of the platform. What do you investigate?
Get cost per request, decomposed Token cost splits into input and output, and they price differently. Break the bill down by feature, by user, and by input vers
An LLM feature that worked last week now gives worse answers. Nothing was deployed. How do you find out what changed, and what should have been in place?
What the interviewer is testing Whether you treat an AI feature as a system with configuration and dependencies, or as a black box that mysteriously drifts. Wha
Product wants to add a recommendation feature using browsing history. Legal asks for a data protection impact assessment. What does architecture need to supply?
What the assessment actually needs from architecture Legal cannot assess a feature description. They need the data facts, which only the design supplies: A data
Your company needs a feature flag and experimentation platform. A vendor charges a substantial annual fee; a team estimates six weeks to build one. Decide.
Apply the differentiation test first Is this a source of competitive advantage? Feature flagging is context, not core: customers do not choose you for it, and b
Knight Capital lost roughly $460M in 45 minutes in 2012 after a deployment reached seven of eight servers. Which architectural failures made that possible, and which one would you fix first?
The case, as publicly reported Per the SEC's 2013 order, Knight Capital deployed new order routing code to eight production servers ahead of a NYSE programme la
Feature Freshness
How stale a feature can be before the model degrades, and the pipeline that follows.
Client Feature Flags
Flag evaluation on a device you do not control, and the flicker and staleness it brings.
Feature Flags
Decoupling deploy from release, with an expiry date.
Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
Data Observability
Freshness, volume, schema and distribution monitoring for pipelines that fail silently.
Error Budgets
Unreliability as a resource that feature velocity spends.
ML Platform
Feature stores, training pipelines, registries and deployment.
Platform Funding
Central cost, showback, chargeback, and justifying a team that ships no customer feature.
Streaming vs Batch
The freshness requirement that actually justifies streaming, and the cost of assuming one.