CertLabProGoogle Cloud Associate Data Practitioner
225 questions de pratique
Dernière révision : April 2026
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The Google Cloud Associate Data Practitioner (ADP) is a newer associate-tier credential that validates day-to-day data work on Google Cloud — ingesting, transforming, analyzing, and presenting data with BigQuery, Dataform, Dataflow, Dataplex, and Looker. It targets data analysts, BI engineers, and analytics engineers rather than full data engineers, so the exam emphasizes SQL, scheduled queries, basic pipeline orchestration, and Looker / Looker Studio dashboards over deep streaming and platform-engineering content. ADP fits between Cloud Digital Leader and the Professional Data Engineer (PDE) certification: more technical than CDL, less architectural than PDE. It is the most accessible technical-data cert in the GCP track.
Largest domain at 30%. BigQuery loads, federated queries, Storage Transfer Service, Datastream for CDC, Pub/Sub for streaming ingestion, basic Dataflow templates. SQL transforms and Dataform.
BigQuery SQL (window functions, CTEs, ARRAYs/STRUCTs), Looker semantic model basics, Looker Studio dashboards, scheduled queries and BI Engine. 27% — heavy on practical SQL.
Cloud Composer (managed Airflow) DAGs, Dataform workflows, Cloud Scheduler + Cloud Workflows, Pub/Sub triggers. 18% — conceptual, no DAG code, but candidates must know which orchestrator fits which pattern.
Dataplex zones and lakes, Data Catalog tagging and search, IAM for BigQuery (dataset / table / column / row), encryption with CMEK, retention and table-level security. 25%.
$90k–$130k–$180k USD annual
Range reflects US-based analytics-engineer and BI roles where BigQuery is the primary warehouse. FAANG-equivalent senior analytics engineers clear $200k. Pure reporting analyst roles trend lower; analytics engineers at GCP-heavy unicorns and digital-native companies trend higher.
Source: levels.fyi 2025–2026 (Google L3–L4 data analyst, analytics engineer at GCP-shop unicorns), U.S. BLS OEWS May 2024 (15-2051 data scientists, 13-2031 budget analysts, 15-1211 computer systems analysts). Figures are approximate; actual compensation depends on role, region, and experience.
ADP is new (introduced 2024) and demand is still building, but it fills a clear gap below the Professional Data Engineer cert that Google has long needed. Companies running BigQuery-centric stacks — particularly digital-native, ad-tech, retail-analytics, and gaming companies — list it on analyst-engineer postings as a differentiator. Demand is concentrated in markets with strong GCP presence (SF Bay Area, NYC, London) and in industries where Looker is the standard BI tool. As the credential matures, expect it to become the default GCP cert on data-analyst job postings the way Microsoft DP-900 / DP-203 dominate the Azure analytics track.
There are no formal prerequisites. Google recommends six months or more of hands-on data work on Google Cloud, comfort with SQL, and a basic working understanding of data pipeline concepts. The official Associate Data Practitioner Learning Path on Google Cloud Skills Boost (around 30–40 hours of labs) covers everything tested.
If you have no SQL experience at all, plan on 20–30 extra hours getting comfortable with intermediate SQL (joins, window functions, CTEs) — the BigQuery SQL questions are not flashcards, they are short scenarios. If you already hold AWS Data Engineer Associate, Azure DP-900, or DP-203, the conceptual content maps directly; you mostly relearn product names (BigQuery vs. Redshift / Synapse, Dataflow vs. Glue / ADF, Dataform vs. dbt-cloud, Looker vs. QuickSight / Power BI).
ADP is associate-level and aimed at the "I do data work" practitioner rather than "I architect data platforms" engineer. Plan on 50–80 hours over 5–8 weeks if you are new to GCP data tooling, or 20–35 hours over 2–4 weeks if you already work daily in BigQuery. The exam is 50–60 multiple-choice / multiple-select questions in 120 minutes, delivered through Pearson VUE (Google migrated from Kryterion / Webassessor in early 2026).
The most common stumbling block is the breadth of Dataplex, Data Catalog, and Dataform terminology — these products evolved rapidly and questions can hinge on naming distinctions (zones vs. lakes vs. assets, tag templates vs. tags). Hands-on practice with the BigQuery sandbox and a small Looker Studio dashboard project is the highest-leverage preparation. Google does not publish numeric scores — only pass/fail. The credential is valid for three years and recertification requires re-passing the current exam.
Initial general availability. New Associate-tier credential filling the gap between Cloud Digital Leader and the Professional Data Engineer cert. Current version as of April 2026.
ADP (Google Cloud Associate Data Practitioner) is a a moderately difficult exam expecting practical hands-on experience plus solid understanding of best practices Associate-level exam. Most candidates need 80–150 hours of study spread over 6–12 weeks for associate-level exams. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.