~/gcp-pmle · Collaborate within and across teams to manage data and models ▊

← Professional Machine Learning Engineer

GCP · objective · 17% of the exam

Collaborate within and across teams to manage data and models

Exam objectives are the official skill domains Professional Machine Learning Engineer tests. Below are the official GCP docs our questions on this objective cite — review them, then practise.

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Official references for this objective

  • Google Cloud — Data Science Solutions | Google Cloud

    Teams can share notebooks, data connections, and compute resources across projects, making Google Cloud a truly collaborative data science platform.

  • Google Cloud — BigQuery | AI data platform | EDW

    Stream detailed agent interactions to BigQuery for performance and cost optimization with a single line of code using BigQuery agent ops plugins for frameworks like

  • Google Cloud — Google Cloud borderless Lakehouse | Google Cloud

    Knowledge Catalog eliminates data fragmentation by automatically unifying metadata from Google Cloud, partner platforms, and third-party catalogs (such as Atlan, Collibra, and Datahub) into a

  • Google Cloud — Knowledge Catalog (formerly Dataplex) | Google Cloud

    Data engineers define structure in technical schemas while analysts define meaning in BI tools, creating a gap for AI agents that can lead to untrusted

  • Google Cloud — Cloud Data Fusion | Google Cloud

    the ability to create an internal library of custom connections and transformations that can be validated, shared, and reused across teams