Automating and orchestrating ML pipelines
Practise only this domain. 57 questions with explanations, 20 at a time.
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- After writing a Kubeflow pipeline in Python, which artifact must you generate before Vertex AI can run it?Easy
- A retail company drops daily sales files into a storage bucket. Each new file should kick off the training workflow automatically, with no polling and no human action. What is the best design?Medium
- A team applies normalization and vocabulary lookups to features during training, then reimplements them in the serving service. Predictions drift because the two copies diverge. What key advantage does TensorFlow Transform offer over preprocessing written separately for training and serving?Hard
- A fraud-scoring pipeline ingests transaction batches whose columns change: vendors add new fields and retire old ones. The team wants new columns flagged for review rather than silently breaking training, while still tracking gradual value shifts. Which TFX components support this?Hard
- A team wants retraining to start automatically whenever a new data batch passes validation, with a new model promoted to an endpoint only if evaluation thresholds are met. Which pairing fits this continuous training and delivery pattern?Medium