Invoice extraction
An accounts-payable team needs structured fields from scanned invoices. They have limited ML engineering capacity and a representative labeled sample. What should they evaluate first?
- A document-processing service against the labeled sample
- A custom training cluster before inspecting the invoices
- A forecasting model trained on invoice totals
Reveal the reasoning
Best starting choice: A. A document-processing service is a practical baseline for supported extraction tasks. Measure field-level quality, difficult document slices, costs, and review needs. Custom training may be justified later if the baseline misses the requirements; forecasting does not solve field extraction.
Review the related guide ↗The late transaction
A transaction occurred at 09:55, arrived in the feature system at 10:15, and a prediction was made at 10:00. Which historical feature value belongs in the training example?
- The value available to the serving system at 10:00
- The value after ingestion because the event happened before 10:00
- The latest corrected value regardless of when it was available
Reveal the reasoning
Best starting choice: A. Use the information available to the actual serving system at decision time. Event time alone does not establish availability; the later value would leak information into the reconstructed example unless another serving source actually supplied it earlier.
Review the related guide ↗An idle accelerator
A training job has low GPU utilization and spends most of each step waiting for input. What is the best first investigation?
- Double the GPU count immediately
- Profile the data loading and preprocessing path
- Use the final test set to tune the model
Reveal the reasoning
Best starting choice: B. Input starvation can leave additional accelerators idle. Profile throughput, CPU, storage, and preprocessing before changing compute. A final test set should stay separate from tuning.
Review the related guide ↗A model with a deadline
A retailer needs a score for every product before a nightly inventory job. The scores do not need to change during the day. Which serving pattern is a sensible starting point?
- Scheduled batch inference with a completion deadline
- A dedicated online endpoint per product
- Manual notebook execution for every product
Reveal the reasoning
Best starting choice: A. Batch inference matches the known deadline and bulk workload. Confirm data freshness, job completion, failures, cost, and downstream availability. Online serving adds little value unless a new real-time requirement exists.
Review the related guide ↗A failed release gate
A new model trains successfully and improves overall recall, but falls below a required quality threshold for an important slice. What should an automated pipeline do?
- Promote because training succeeded
- Remove the slice check for this run
- Block promotion and retain the evaluation evidence
Reveal the reasoning
Best starting choice: C. Training success and overall improvement do not satisfy a failed release gate. Preserve the metrics, investigate the slice and its sample size, and keep the existing model until the release policy is met.
Review the related guide ↗A shifted input
Input distributions change after expansion into a new region. Outcome labels will arrive in a month. What can you conclude now?
- There is an input shift to investigate; quality impact is not yet established
- The model definitely became inaccurate and must be retrained today
- Concept drift has been proven
Reveal the reasoning
Best starting choice: A. Check feature pipelines, population changes, data quality, and operational signals immediately. Input drift alone does not prove model degradation or concept drift; evaluate outcomes when representative labels mature.
Review the related guide ↗How to use your results
For each scenario, explain the constraint, why the other choices are weaker, and what evidence could change your decision. Revisit topics where your reasoning was uncertain. Follow up with the official sample questions and hands-on labs before judging your readiness.
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