Note, that this collection of links and topics does not cover the ML/DL part of the exam, in order to prepare for that I’d recommend reading through the book “Geron Aurelien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” and doing some additional recap on basic CNN, RNN, Transformers topics, common types of NN layers, types of activation functions, types of loss functions, optimizers, embeddings and common hyperparameters
Big article with links and tools and product lists for full ML lifecycle on gcp
https://cloud.google.com/architecture/ml-on-gcp-best-practices
high-level stuff in one place
https://developers.google.com/machine-learning/guides/rules-of-ml
Enormous collection of labs for ML and Analytics on GCP
https://github.com/GoogleCloudPlatform/training-data-analyst
good but not exhaustive collection of materials
https://dzlab.github.io/certification/2022/01/08/gcp-ml-engineer-prep/