PMLE study plan

Your step-by-step plan

1. Map the blueprint and book a date

   Day 1

   Read the official Google Cloud exam guide and the six domains with their weights. Book a provisional date now: a fixed date turns open-ended study into a plan and is the strongest predictor of actually sitting. Note that Scaling Prototypes (21 percent) and Serving (20 percent) are the two heaviest domains, with Automating Pipelines (18 percent) close behind.

2. Build the lifecycle decision map

   Week 1

   Before drilling any domain, build the decision rules the whole exam rests on: the low-code call (BigQuery ML versus AutoML versus a custom Vertex AI job versus a tuned foundation model), the parallelism call (model versus data), and the serving call (online endpoint versus batch prediction). Use the recall prompts in this guide: cover the answer, choose from the constraint, then reveal. If you cannot pick from the scenario alone, you do not own it yet.

3. Go deep on scaling and serving (Domains 3 and 4)

   Weeks 1 to 3

   These two are the largest by weight, so they get the most time. Drill model-type-by-constraint, the model-versus-data parallelism rule, the TPU pod scaling reason, the online-versus-batch call, and endpoint autoscaling levers. Practise on scenario questions and read the worked explanation on every one, including the ones you got right, watching for the named constraint that picks the answer.

4. Lock pipelines and collaboration (Domains 5 and 2)

   Weeks 3 to 4

   Pipelines rewards the retraining-trigger rule and the train-serve parity pattern of persisting and reapplying the fitted transform. Collaboration rewards the preprocessing scale-to-tool map, the Experiments-versus-ML-Metadata split, and the imbalanced-data metric. Do the harder calls by hand until the constraint alone decides them.

5. Cover low-code and monitoring (Domains 1 and 6)

   Week 4

   Low-code rewards the BigQuery ML and Gemini-from-BigQuery decisions plus the generative cost levers such as context caching and the Gen AI evaluation service. Monitoring rewards Model Armor for managed prompt screening, the integrated-gradients-versus-sampled-Shapley call, and the fact that a baseline dataset and notification channels are what make Model Monitoring useful. Both are dependable marks and tie straight back to named constraints.

6. Drill weak domains, then space the review

   Week 5

   Use your per-domain accuracy to attack the two domains dragging you down, not to re-read what you already know. Then space it: revisit each domain's recall prompts after a few days and again a week later. Spacing roughly doubles what sticks compared with cramming.

7. Sit a timed mock and calibrate

   Weeks 5 to 6

   Take at least one full timed mock under exam conditions to rehearse pacing and the flag-and-return habit across the question set in 120 minutes. Treat the score as a per-domain readiness signal, not a single number, and review every missed question, naming the constraint you misread, before you book or sit.

Frequently asked questions