Problem Solving and Learning from Failure Questions
Combines technical or domain problem solving with reflective learning after unsuccessful attempts. Candidates should describe the troubleshooting or investigative approach they used, hypothesis generation and testing, obstacles encountered, mitigation versus long term fixes, and how the failure informed future processes or system designs. This topic often appears in incident or security contexts where the expectation is to explain technical steps, coordination across teams, lessons captured, and concrete improvements implemented to prevent recurrence.
MediumTechnical
0 practiced
You deploy a new NLP model and production accuracy (measured on delayed human labels) is 12% lower than the canary tests. Provide a structured, step-by-step investigation plan you would execute in the first 2 hours to determine whether this is caused by data drift, a model regression, a serving bug, or label-sampling bias. Include which metrics and sample checks you perform.
EasyTechnical
0 practiced
List three practical automated mitigation mechanisms you could implement to reduce user impact when a production AI model begins producing incorrect or unsafe outputs. For each mechanism, explain the trade-offs (speed, coverage, false-positives) and when you'd prefer it over a full rollback.
HardSystem Design
0 practiced
Architect a disaster recovery plan for training infrastructure that must withstand region-wide outages while minimizing lost training progress. Include checkpointing strategy, cross-region storage, replication frequency, cost trade-offs, and process to resume long-running jobs on different hardware (e.g., different GPU types) with minimal hyperparameter drift.
HardSystem Design
0 practiced
Design an automated incident-correlation system that ingests logs, metrics, traces, and model-quality signals and groups alerts related to the same root cause. Describe the data model, correlation heuristics (time-window, topology, semantic similarity), evaluation metrics (precision/recall), and how to present human-readable root-cause hypotheses to operators.
MediumBehavioral
0 practiced
Tell me about a time you led a post-incident review for an AI production failure. Use the STAR method: Situation, Task, Action, Result. Focus on the technical debugging you performed, how you coordinated with other teams, and what process changes were implemented afterward. Explain measurable outcomes if possible.
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