Search
Find a bite, explore a topic or look for a role.
Results for “Kubernetes”
Bites 384
How do you manage configuration and secrets for a containerized FastAPI app?
Tests 12-factor config separation and Docker secret hygiene. A strong answer uses pydantic-settings with runtime env vars, lru_cache, and keeps .env out of the image. Red flag: baking credentials into Dockerfile layers or committing .env files.
Docker Compose for Local FastAPI Stacks
Docker Compose turns your laptop into a one-command datacenter. Define Postgres, Redis, and your FastAPI app in one YAML file and they boot as a networked stack.
What technical areas would you investigate in acquisition due diligence?
Tests strategic integration risk beyond code quality. Cover: architecture compatibility and tech debt; data model overlap and migration cost; security and compliance gaps; team retention; roadmap conflicts.
Find a novel value proposition from a recent technology breakthrough
Tests translating a technical breakthrough into product strategy: name an underserved market, quantify economic value, map a minimal build. Pick one tech, define pricing power, list 3-4 parts. Red flag: solution seeking a problem or feature lists sans value.
How would you pivot system architecture from enterprise to startups?
Mapping a business pivot to tech tradeoffs across systems, features, and ops. Cut bloat for speed; shift to self-serve multi-tenant SaaS; automate ops and swap high-touch support for self-serve signup. Calling it simple scale-down not value-chain redesign.
How would you out-engineer a competitor's new data-intensive feature?
This tests strategic design under competition. Strong answer maps competitor's bottleneck, applies distributed sharding or streaming, picks asymmetric edge caching, and locks in latency SLAs. Red flag: no bottleneck analysis or ignoring quantified moats.
How does a model registry differ from cloud storage like S3?
This tests model governance beyond raw storage. A strong answer contrasts storage with stage transitions, lineage, and ACLs, then lists metadata like metrics, dependencies, and schemas. A red flag is treating S3 folders with naming conventions as a registry.
How do you configure Docker for host GPU access and CUDA libraries?
This tests GPU passthrough via the NVIDIA Container Toolkit. Strong answers use nvidia/cuda base images matching the host driver, pass GPUs with --gpus all, and avoid installing drivers inside the container.
Why avoid global Python dependencies for ML, and how do containers help?
This probes environment isolation and reproducibility in ML. A strong answer cites global dependency conflicts, system library skew, and brittle environments; then notes containers freeze the full stack for deterministic deployment.
Design a centralized model registry for a large enterprise
Tests ML artifact governance at scale. Strong answers cover immutable versioned artifacts with dependency manifests, a framework-agnostic API, and pluggable deployment targets. Red flag: treating models as opaque files without environment reproducibility.
Compare and contrast Apache Airflow versus Kubeflow Pipelines for ML orchestration
This tests matching orchestrators to ML constraints. A strong answer contrasts Airflow's task scheduling and backfills with Kubeflow's K8s-native GPU scaling, choosing based on team skills.
Design a showback or chargeback system for ML infrastructure costs
Tag workloads to cost centers; define shared-resource formulas; automate reconciliation; use showback.
Differences between on-demand, reserved, and spot EC2 instances?
Tests cost-reliability-commitment tradeoffs for ML infrastructure. Good answers map on-demand to experiments, reserved for production training, and spot to fault-tolerant batch jobs. Red flag: spot for real-time serving or skipping reserved capacity analysis.
What is the wrong and right way to manage ML database secrets?
This tests secret management hygiene for ML pipelines. A strong answer rejects hardcoded secrets and env vars, then proposes AWS Secrets Manager with IAM retrieval, TLS, caching, and rotation. A red flag is suggesting .env files, ConfigMaps, or CLI arguments.
Describe securing an automated ML pipeline and CI/CD integration points
Tests ML supply-chain depth versus bolt-on appsec. Strong answers stage checks across
How do you give read-only access to a shared cloud storage bucket?
Bind an IAM role with read permissions to the team at the bucket level, avoid object-level ACLs, and mount read-only on training VMs.
Propose an architectural solution for contended GPU training resources
Tests multi-tenant GPU scheduling design at scale. Great answers tier jobs by checkpointability, apply quota-based preemption, mix spot and on-demand instances, and use MIG or time-slicing to bin-pack. Red flag: buying GPUs without scheduling logic.
Design a multi-tenant ML platform with isolation, security, and cost attribution
Tests mapping tenancy to compute, network, and identity primitives. Strong answers compare hard vs soft isolation, use namespaces or node pools with network policies and IAM, and enforce chargeback via resource quotas and labels.
How would you architect dynamic training resource provisioning?
Routes jobs to right-sized instances, provisions managed jobs using spot, and auto-tears down on completion.
Design a multi-model serving architecture for infrequently used models
Tests sparse-traffic cost efficiency via shared compute and dynamic loading. Strong answers: LRU cache on fast storage, scale-to-zero with async preload, pinned hot models, per-model quotas.