Artificial Intelligence Projects and Problem Solving Questions
Detailed discussion of artificial intelligence and machine learning projects you have designed, implemented, or contributed to. Candidates should explain the problem definition and success criteria, data collection and preprocessing, feature engineering, model selection and justification, training and validation methodology, evaluation metrics and baselines, hyperparameter tuning and experiments, deployment and monitoring considerations, scalability and performance trade offs, and ethical and data privacy concerns. If practical projects are limited, rigorous coursework or replicable experiments may be discussed instead. Interviewers will assess your problem solving process, ability to measure success, and what you learned from experiments and failures.
EasyTechnical
0 practiced
Walk me through an AI/ML project you led or contributed to from problem definition through results. Describe: the concrete problem statement, measurable success criteria (both business and model-level), key stakeholders and constraints (data, latency, budget), what data you used, your role, major technical decisions, and the final outcome (including unexpected discoveries).
EasyTechnical
0 practiced
In Python, implement compute_class_weights(labels: List[int]) -> Dict[int, float]. Use formula weight = total_samples / (num_classes * count_class). Your function should: ignore None labels, handle empty input by returning an empty dict, and run in O(n) time. Explain complexity and potential pitfalls when plugging these weights into training frameworks.
MediumTechnical
0 practiced
In Python, write a function log_prediction(user_id: str, model_name: str, model_version: str, input_features: dict, prediction: float, filepath: str) that appends a JSONL line to filepath containing a timestamp, sorted keys, and model metadata. Include error handling and outline how you'd handle concurrent writes and file rotation in production.
MediumTechnical
0 practiced
Compare offline evaluation, online A/B testing, and interleaving for measuring model improvements. For each method describe strengths, weaknesses, required instrumentation, sample-size considerations, and situations where one approach is preferred over the others.
MediumTechnical
0 practiced
Describe the monitoring and observability stack you would implement for a deployed ML service. Specify the model-level metrics (e.g., accuracy, calibration), data/feature drift detection, system metrics (latency, CPU/GPU), logging and prediction lineage, alerting thresholds, and incident prioritization. Mention tools you would use and why.
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