Show you can work with data on Google Cloud, from ingesting and analyzing it with BigQuery and Looker to managing access, and track your readiness for the Associate Data Practitioner exam.

Fundamental

Prepare and test your skills

Fundamental

Prepare and test your skills

Google Cloud service

BigQuery

Topic in 33 exam domains

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BigQuery · Service
%%bigquery · Feature · notebook magic command
BIGQUERY · Concept · transcode input format

Also called: Big Query, BigQuery destination, BigQuery source, bigquery.googleapis.com, Cloud BigQuery, GBQ, GCP BigQuery, Google BigQuery, Google Cloud BigQuery, Managed BigQuery

BigQuery is Google Cloud's fully managed, serverless enterprise data warehouse designed for storing, preparing, and analyzing large-scale structured datasets. On Associate Data Practitioner it comes up as a target storage solution for structured data, an in-warehouse SQL transformation engine, and a platform for assessing and cleaning data quality.

What you can expect on the exam

Differentiating ETL, ELT, and ETLT architectures. Know the difference between pipeline architectures to determine when to transform data before ingestion versus leveraging BigQuery for scalable, warehouse-native SQL transformations.
Executing data cleaning in the warehouse. Be ready to apply data cleaning techniques within BigQuery to standardize formats, handle missing values, and perform deduplication prior to downstream consumption.
Selecting storage for structured workloads. Expect to classify workload data requirements and identify BigQuery as the appropriate storage and processing solution when managing structured datasets with fixed schemas.
Validating table quality and uniqueness. You may need to evaluate data quality across BigQuery tables by applying validation rules, profiling attributes, and configuring data scans to detect anomalies and duplicate records.

Where this appears on the exam

23 subdomains in 4 of 4 domains

Practice questions on this topic

5 questions

Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery

How learners score on this topic

Last updated November 2, 2026

Associate Data Practitioner

Show you can work with data on Google Cloud, from ingesting and analyzing it with BigQuery and Looker to managing access, and track your readiness for the Associate Data Practitioner exam.

Fundamental

Prepare and test your skills

Fundamental

Prepare and test your skills

Google Cloud service

BigQuery

Topic in 33 exam domains

X LinkedIn Facebook Reddit

BigQuery · Service
%%bigquery · Feature · notebook magic command
BIGQUERY · Concept · transcode input format

Also called: Big Query, BigQuery destination, BigQuery source, bigquery.googleapis.com, Cloud BigQuery, GBQ, GCP BigQuery, Google BigQuery, Google Cloud BigQuery, Managed BigQuery

What you can expect on the exam

Differentiating ETL, ELT, and ETLT architectures. Know the difference between pipeline architectures to determine when to transform data before ingestion versus leveraging BigQuery for scalable, warehouse-native SQL transformations.
Executing data cleaning in the warehouse. Be ready to apply data cleaning techniques within BigQuery to standardize formats, handle missing values, and perform deduplication prior to downstream consumption.
Selecting storage for structured workloads. Expect to classify workload data requirements and identify BigQuery as the appropriate storage and processing solution when managing structured datasets with fixed schemas.
Validating table quality and uniqueness. You may need to evaluate data quality across BigQuery tables by applying validation rules, profiling attributes, and configuring data scans to detect anomalies and duplicate records.

Where this appears on the exam

23 subdomains in 4 of 4 domains

Practice questions on this topic

5 questions

Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery

Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise)

BigQuery DataFrames Pandas API

Analyze data to answer business questions

BigQuery APPROX_QUANTILES for Percentile Analysis

3.2 Schedule, automate, and monitor basic data processing tasks

Log-based Metrics

Practice this domain

How learners score on this topic

Last updated November 2, 2026

What you can expect on the exam

Where this appears on the exam

Practice questions on this topic

Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery

Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise)

Analyze data to answer business questions

3.2 Schedule, automate, and monitor basic data processing tasks

Related topics

How learners score on this topic

What you can expect on the exam

Where this appears on the exam

Practice questions on this topic

Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery

Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise)

Analyze data to answer business questions

3.2 Schedule, automate, and monitor basic data processing tasks

Related topics

How learners score on this topic