Personal account of hands on experience using public cloud providers and the concrete results delivered. Candidates should describe specific services and patterns they used for compute, storage, networking, managed databases, serverless and eventing, and explain their role in architecture decisions, deployments, automation and infrastructure as code practices, continuous integration and continuous delivery pipelines, container orchestration, scaling and performance tuning, monitoring and incident response, and cost management. Interviewees should quantify outcomes when possible with metrics such as latency reduction, cost savings, availability improvements or deployment frequency and note any formal training or certifications. This topic evaluates depth of practical experience, ownership, and the ability to operate and improve cloud systems in production.
EasyTechnical
0 practiced
How do you manage credentials and secrets used by data-science workloads in the cloud? Describe a pattern you used that included service accounts or IAM roles, secrets management (for example AWS Secrets Manager, Azure Key Vault, GCP Secret Manager), key rotation, and audit logging. How did you ensure least-privilege and avoid embedding secrets in code or notebooks?
HardSystem Design
0 practiced
Design an end-to-end reproducible ML pipeline in the cloud that includes Infrastructure-as-Code, CI/CD for training and serving, data and model lineage, artifact storage, model versioning, and explainability hooks. Describe components, how they connect, how to guarantee reproducibility of a model release (data, code, infra, hyperparameters), and how you'd roll back to a prior reproducible state.
MediumTechnical
0 practiced
You need ad-hoc analytics and large-scale joins over petabyte datasets. Compare managed data warehouses (for example Redshift or Snowflake) versus serverless query services (for example BigQuery). Discuss performance, concurrency, cost model (on-demand vs provisioned), maintenance, and the impact on feature generation and rapid experimentation for data scientists.
EasyTechnical
0 practiced
Which observability and monitoring tools have you used on cloud platforms (for example CloudWatch, Cloud Monitoring/Stackdriver, Prometheus, Grafana, Datadog)? For each tool, describe the specific metrics and dashboards you created to monitor model serving and data pipelines, and explain how alerts were configured and how on-call escalation worked.
EasyTechnical
0 practiced
As a data scientist, what day-to-day and project-level practices did you apply to control cloud cost? Include examples such as instance rightsizing, using spot/preemptible instances, storage lifecycle rules, query optimization techniques (for example partitioning or caching), and tagging for cost allocation. Quantify any cost savings you achieved when possible.
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