Role Understanding and Immediate Contribution Questions
Clear understanding of the specific role's responsibilities, success metrics, and the team's current priorities. Before the call, research what this team actually does and their known challenges if possible. During the call, discuss how your experience maps to their needs. Identify 2-3 specific areas where you could immediately contribute (e.g., 'I see you're migrating to cloud; I have 3 years' experience with hybrid networks'). Show you understand the role deeply, not just the job title.
EasyBehavioral
0 practiced
Write a concise three-sentence elevator pitch that explains how your background maps to a team building generative AI assistants. Highlight 2–3 relevant skills, one representative accomplishment, and one immediate contribution you could make in the first month.
MediumSystem Design
0 practiced
The team runs ad-hoc experiments across notebooks and S3. Walk through a plan to introduce experiment tracking and hyperparameter management: recommend a minimal first-step tooling choice, a migration approach for past experiments, and how you'd convince the team to adopt it during the next sprint.
HardTechnical
0 practiced
Write a Python script (<=40 lines) that compares two CSV files: 'production.csv' and 'training.csv'. The script should compute five statistical checks to surface data drift: (1) Kolmogorov-Smirnov p-value for numeric features, (2) Jensen-Shannon divergence for categorical feature distributions, (3) change in mean for numeric features, (4) top-category proportion change, and (5) a sample-size warning if n < 100. The script should output a JSON summary per feature suitable for alerting systems. Assume pandas, numpy, and scipy are available.
HardTechnical
0 practiced
Sensitive personally identifiable information (PII) appears in free-text training data. Design an immediate triage plan to detect, mask, or remove PII before retraining. Cover detection methods (regex, NER, heuristic scoring), validation of masking, safe audit trails (e.g., redaction logs or hashed-detectors), and a plan for long-term prevention.
HardTechnical
0 practiced
Propose a strategy to evaluate and reduce both monetary and carbon costs of a heavy training pipeline without harming model quality. Include immediate wins (for example: mixed-precision, instance scheduling), experiments to run (distillation, smaller architectures), and how to measure cost-per-quality unit (e.g., $ per validation F1 point).
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