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Google Cloud Associate Data Practitioner (ADP) Exam Prep
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Google Cloud Associate Data Practitioner (ADP) Exam Prep
Master BigQuery, Looker, Dataplex, and pipeline orchestration to prepare for the Google Cloud ADP exam domains
Created byAseem Mankotia
Last updated 10/2026
English
What you'll learn
- Select the correct ingestion service (Pub/Sub, Dataflow, Datastream, Data Transfer Service) for a given source, volume, and latency
- Clean and transform structured, semi-structured, and unstructured data using BigQuery SQL, Dataprep, and Dataform
- Write BigQuery SQL with joins, aggregations, and window functions while controlling query cost via partitioning and clustering
- Build and share dashboards and reports in Looker Studio and Looker, including BigQuery ML simple predictions
- Design and automate orchestration workflows using Cloud Composer, Workflows, Cloud Scheduler, and scheduled queries
- Build ETL/ELT pipelines with proper dependency handling, retries, and downstream triggering
- Choose appropriate Cloud Storage classes and lifecycle policies based on access frequency
- Apply IAM least-privilege roles and govern data using Dataplex cataloging and lineage
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Course content
11 sections • 10 lectures • 2h 33m total length
Requirements
- Basic SQL knowledge (SELECT, JOIN, GROUP BY)
- Familiarity with Google Cloud Console navigation
- Conceptual understanding of data pipelines (ingestion, transformation, storage)
- No prior Google Cloud certification required
Description
This course contains the use of artificial intelligence.
This course is a focused, scenario-driven preparation path for the Google Cloud Associate Data Practitioner certification, built around the four official exam-guide domains: Prepare and ingest data, Analyze and present data, Orchestrate data pipelines, and Manage data. You will work through BigQuery SQL transformations, Pub/Sub and Dataflow streaming patterns, Datastream change-data-capture, Looker and Looker Studio reporting, Cloud Composer and Workflows orchestration, and Dataplex governance - the exact decision pairs the exam tests, such as Dataflow versus Pub/Sub, Cloud Storage class selection, partitioning versus clustering, and Looker versus Looker Studio. Every concept is paired with a console walkthrough or hands-on lab so you are reading and doing, not just watching slides.
Instructor Aseem Mankotia designed this course around realistic, exam-style scenario questions with plausible distractors at associate difficulty, rather than deep Professional-level architecture depth. Each of the ten chapters maps directly to an exam domain, includes heavily-tested concepts, common traps candidates fall into, and two scenario-based practice questions with full explanations of why each wrong answer is wrong. The final chapter is a complete timed exam simulation with a 2-hour, 50-to-60-question time-management strategy so you walk into test day with a rehearsed pacing plan.
This is Google's accessible entry-level data credential and a natural on-ramp if you later plan to study for the Professional Data Engineer or Professional Machine Learning Engineer exams in this catalog. Whether you are a data analyst, BI developer, or an engineer moving into Google Cloud, this course gives you a direct, no-fluff route through every exam domain, with BigQuery as the consistent through-line across ingestion, analysis, orchestration, and governance.
AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official Associate Data Practitioner exam guide.
Who this course is for:
- Entry-level and early-career data practitioners, data analysts, and aspiring data engineers who ingest, transform, analyze, and manage data on Google Cloud. Candidates should have roughly 6+ months of hands-on Google Cloud data experience and basic SQL. A natural first data certification and an on-ramp toward the Professional Data Engineer and Professional Machine Learning Engineer courses.