A few people here have asked what changed in the Professional ML Engineer guide after the June 1 update. I had to go through it line by line for a project anyway, so here's my summary. This is all from the official guide PDF. I haven't sat the new exam.
Structure
- Still 6 sections, same order. Approximate weights: low-code AI ~13%, collaborating on data and models ~16%, scaling prototypes into models ~21%, serving and scaling ~20%, automating pipelines ~18%, monitoring ~13%. (Yes that's 101%, blame Google's rounding.)
- Vs the previous guide: pipelines went from ~22% to ~18%, scaling prototypes from ~18% to ~21%, collaborating from ~14% to ~16%. Google's old PDF link now serves the new guide, but there's an archived copy of the old one if you want to compare.
- 16 subsections went down to 14. The standalone AutoML subsection is gone (AutoML training now sits in 1.1), and so is the standalone metadata one (artifact, version and lineage tracking is in 2.3 now).
Naming
- Vertex AI is now Gemini Enterprise Agent Platform everywhere in the guide. For exam purposes it's mostly a rename: Vertex AI Pipelines is now Agent Platform Pipelines, Vertex AI Prediction is now Agent Platform Inference. I wouldn't find-and-replace your notes blindly though, a few things moved around.
New or more explicit
- 1.1: AutoML training on Agent Platform, and fine-tuning Gemini from BigQuery
- 1.2: evaluating and picking models from Model Garden, tuning Gemini / Imagen / Veo, cost vs latency vs availability for Gemini apps
- 2.1: picking a preprocessing tool by data size and complexity (SQL vs Spark vs in-memory Python)
- 2.3: predictive and gen AI evaluation are named together now, and LLM-as-a-judge is called out explicitly
- 3.1: choosing a model type (ARIMA vs DNN vs LLM) under cost, latency and scale constraints
- 4.1: prebuilt vs custom containers, versioning in Model Registry, canary rollouts next to A/B, pre- and postprocessing at inference time
- 5.1: Ray on Agent Platform gets named next to Agent Platform Pipelines (Managed Service for Apache Airflow is just Cloud Composer's new name, and Composer was already there)
- 5.2: CI/CD/CT spelled out (CT = continuous training)
- 6.1: LLM security. Data exfiltration, malicious prompts, sensitive data in prompts, Model Armor and safety filters
- 6.2: training-serving skew, data drift, concept drift and feature attribution drift grouped together, plus monitoring gen AI solutions
- The intro now mentions prompt/context engineering
No longer named explicitly (doesn't mean they can't show up)
- The Agent Builder RAG objective, TFX / Kubeflow DSL pipeline design, hosting MLflow, Jenkins, Reduction Server / Horovod, TensorBoard, and edge as a training hardware example
- Framework examples are PyTorch / scikit-learn / JAX now, a lot less TensorFlow-heavy
My take: it's still an ML engineering / MLOps exam, just with gen AI added on top. I wouldn't call it a "build agents" exam. If your prep material is from before June and leans on TFX, Kubeflow DSL or TensorFlow details, I'd shift some of that time to picking and tuning models in Model Garden (Gemini / Imagen / Veo), LLM-as-a-judge style evaluation, and LLM security (Model Armor).
Official guide: exam guide PDF
Disclosure: I build a PMLE practice site, which is why I was reading the guide this closely. There's a free diagnostic that scores you by section. No account needed. The 5-question quick check takes about 5 min and samples the five biggest sections; the full 20 takes about 20 min and covers all 6. Not affiliated with Google. If I got anything wrong above, tell me and I'll edit.
Bug bounty while it's new: if you find a question with a wrong answer (seeing all the answers needs a free account), comment here or DM me. First person to report each one gets a free 90-day pass once I've confirmed and fixed it.