Scaling prototypes into ML models
Practise only this domain. 85 questions with explanations, 20 at a time.
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- A lender must tell each rejected applicant the main factors behind that specific decision. The model is a gradient boosted classifier deployed on Vertex AI. Which technique provides this?Medium
- A data science lead notices that her classifier scores 0.98 accuracy on the examples used to fit it but only 0.71 on a holdout set drawn from the same population. Which diagnosis fits best?Easy
- An engineer fine-tuning a transformer language model on a single accelerator keeps hitting out-of-memory errors, and the batch size they need is out of reach. Which two changes most directly lower peak memory while keeping training quality?Hard
- While training a churn predictor, the loss on the training split keeps falling, but the loss on a held-out validation split has risen over the last several evaluation rounds. What is the most appropriate stopping rule?Easy
- In a housing price model, floor area, room count and built-up area are almost perfectly correlated, and the coefficient estimates swing wildly between retrains. Which remedy addresses this redundancy?Medium
- A research team trained a model in JAX and now wants to serve it on Vertex AI. None of the prebuilt prediction images include JAX. What is the best way to deploy it?Medium
- A vision model needs seven days per run on one accelerator, but your deadline requires each run to finish within one day. The architecture and dataset must stay the same. What change would most directly meet the deadline?Medium
- You trained a binary classifier and now want to inspect its confusion matrix and precision-recall metrics for version 3 in the Vertex AI console. Where should you look?Easy
- A fraud team's classifier flags very few transactions, and fraud is rare in the data. The business says missing a fraudulent transaction is far worse than a false alarm. Which metric should headline the report?Easy
- A convolutional classifier scores 97% on training batches but only 79% on held-out images. The team wants better generalization without changing the dataset. What is the best next step?Easy
- A multilingual support team is evaluating one language model for three jobs: answering product questions, condensing long tickets, and tagging customer mood. Which evaluation plan gives the most complete picture?Medium