Google Cloud Associate Data Practitioner is an entry-level data certification. It covers ingesting and cleaning data, analysing it with BigQuery SQL and notebooks, building dashboards in Looker, simple ML with BigQuery ML, basic pipelines, and data security and lifecycle. Google recommends only 6+ months of hands-on data work on Google Cloud, so it suits freshers and data analysts more than the Professional Data Engineer.
This guide follows Google's official exam guide: four sections with weights, the services to tell apart, a 4-week plan and 15 original practice questions.
Quick facts (Google Cloud, November 2026)
| Item | Detail |
|---|---|
| Length | 2 hours |
| Questions | 50–60, multiple choice and multiple select |
| Fee | USD 125 (plus tax where applicable) |
| Languages | English, Japanese |
| Delivery | Online-proctored or at a test centre |
| Prerequisites | None; 6+ months working with data on Google Cloud recommended |
Read the official Associate Data Practitioner page and exam guide before booking. Google also offers free sample questions and $300 of free credits for new customers.
Who should take it?
- Freshers aiming for data analyst, BI or junior data engineer roles.
- Excel and SQL users moving to cloud data.
- Anyone who passed Cloud Digital Leader and wants a hands-on data credential.
Start with the data analyst roadmap for freshers and practise SQL interview questions.
Sections and weights
| Section | Weight |
|---|---|
| 1. Data Preparation and Ingestion | ~30% |
| 2. Data Analysis and Presentation | ~27% |
| 3. Data Pipeline Orchestration | ~18% |
| 4. Data Management | ~25% |
Section 1: Preparation and ingestion (~30%)
- ETL vs ELT vs ETLT; Storage Transfer Service vs Transfer Appliance; data quality; cleaning with Cloud Data Fusion, BigQuery SQL and Dataflow.
- File formats: CSV, JSON, Parquet, Avro and database tables.
- Extraction tools: Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion.
- Storage choice: Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner; regional, dual-region and multi-region locations; structured vs semi-structured vs unstructured.
- Loading with the gcloud and bq CLIs, transfer services and client libraries.
| Data | Usual home |
|---|---|
| Images, PDFs, raw files, backups | Cloud Storage |
| Analytics over billions of rows with SQL | BigQuery |
| A small transactional app database (MySQL or PostgreSQL) | Cloud SQL |
| Global transactional database at large scale | Spanner |
| Mobile and web app documents | Firestore |
| Very high-throughput time series or IoT | Bigtable |
Section 2: Analysis and presentation (~27%)
- BigQuery SQL for reports and insights; Jupyter notebooks in Colab Enterprise.
- Looker vs Looker Studio; creating and sharing dashboards; simple LookML parameters.
- ML: use cases for BigQuery ML and AutoML; calling Google LLMs from BigQuery through a remote connection; training, evaluating and predicting with BigQuery ML SQL; Model Registry.
-- Monthly revenue by city, top 5 cities (BigQuery)
SELECT city, FORMAT_DATE('%Y-%m', order_date) AS month, ROUND(SUM(amount), 2) AS revenue
FROM shop.orders
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 6 MONTH)
GROUP BY city, month
QUALIFY RANK() OVER (PARTITION BY month ORDER BY revenue DESC) <= 5
ORDER BY month, revenue DESC;
-- Train and evaluate a simple model with BigQuery ML
CREATE OR REPLACE MODEL shop.repeat_buyer
OPTIONS (model_type = 'LOGISTIC_REG', input_label_cols = ['repeat']) AS
SELECT orders_90d, avg_amount, city, repeat FROM shop.customer_features;
SELECT * FROM ML.EVALUATE(MODEL shop.repeat_buyer);Section 3: Pipeline orchestration (~18%)
- Choosing a transformation tool: Dataproc (Spark/Hadoop), Dataflow (Apache Beam), Cloud Data Fusion (no-code), Cloud Composer (Airflow), Dataform (SQL in BigQuery).
- Scheduled queries, Cloud Scheduler and Composer; monitoring Dataflow jobs; Cloud Logging and Monitoring.
- Orchestration choice: Composer, scheduled queries, Dataproc Workflow Templates or Workflows; Pub/Sub to BigQuery streaming; Eventarc triggers.
Section 4: Data management (~25%)
- IAM least privilege: basic vs predefined roles; Cloud Storage public, private and uniform access; sharing data with Analytics Hub.
- Lifecycle: Standard, Nearline, Coldline and Archive storage classes; deletion rules in Cloud Storage and BigQuery; archiving.
- Availability: backups, replication, and choosing between regions, dual-regions and multi-regions.
- Security: CMEK vs CSEK vs Google-managed keys; Cloud KMS; encryption in transit vs at rest.
4-week study plan (about 1–2 hours a day)
| Week | Focus | Practice |
|---|---|---|
| 1 | Storage choices, file formats, loading data with bq and transfer services | Load a public CSV and a Parquet file into BigQuery |
| 2 | BigQuery SQL, Colab Enterprise notebooks, Looker Studio vs Looker | Build a sales dashboard in Looker Studio |
| 3 | BigQuery ML and AutoML; Dataflow, Data Fusion, Dataform, Composer; scheduled queries | Train a model in SQL; schedule a daily summary query |
| 4 | IAM, storage classes, lifecycle rules, encryption keys, backups; sample questions | Add a lifecycle rule and a least-privilege role; book the exam |
15 practice questions with answers
These are original practice questions written for this guide, not exam questions.
1. You load raw data into BigQuery first and transform it there with SQL. Is that ETL or ELT?
ELT.
2. 500 TB must move from an on-premises data centre with slow internet. Which tool?
Transfer Appliance.
3. Daily files from an Amazon S3 bucket must be copied to Cloud Storage. Which service?
Storage Transfer Service.
4. Google Ads data must land in BigQuery on a schedule. Which service?
BigQuery Data Transfer Service.
5. Which columnar file format is efficient for analytics?
Apache Parquet.
6. Business users need quick, free self-service reports. Looker or Looker Studio?
Looker Studio. Looker suits governed, modelled enterprise BI with LookML.
7. Analysts who know only SQL want to predict customer churn. What do they use?
BigQuery ML.
8. You want to summarise customer reviews with a Google LLM directly from BigQuery. How?
A remote model connection to a Google LLM, called from BigQuery SQL.
9. A team wants version-controlled SQL transformations inside BigQuery. Which tool?
Dataform.
10. A multi-step workflow across several services must run daily with dependencies and retries. Which tool?
Cloud Composer (managed Apache Airflow).
11. Click events must stream into BigQuery in near real time. What is the usual path?
Pub/Sub to BigQuery (directly or through Dataflow).
12. Log files are read for 30 days and then almost never. How do you cut cost?
A lifecycle rule moving them to Coldline or Archive, then deleting them after the retention period.
13. Your company must control and rotate its own encryption keys inside Google Cloud. Which option?
Customer-managed encryption keys (CMEK) in Cloud KMS.
14. A partner company needs read access to a curated dataset without copying it. What do you use?
Analytics Hub.
15. An analyst only needs to run queries and read one dataset. Which role type?
Predefined BigQuery roles (such as Job User plus Data Viewer on that dataset), not a basic Editor role.
After Associate Data Practitioner
- Go further with the Professional Data Engineer or PL-300 Power BI.
- Practise data engineering interview questions.
- See live jobs for freshers.
Cloud Soft Solutions in Ameerpet, Hyderabad teaches data analytics, data engineering and AI on Google Cloud, AWS and Azure with real-time projects. See the APEX program and all courses.
📞 Call +91 96660 19191 · WhatsApp · Flat No. 513, 5th Floor, Nilagiri Block, Aditya Enclave, Ameerpet, Hyderabad
Frequently Asked Questions
How long is the Associate Data Practitioner exam?
2 hours, with 50 to 60 multiple-choice and multiple-select questions.
How much does the Associate Data Practitioner exam cost?
USD 125, plus tax where applicable.
Is Associate Data Practitioner good for freshers?
Yes. There are no prerequisites and Google recommends only 6+ months of experience working with data on Google Cloud.
Which section has the most weight?
Data Preparation and Ingestion, at about 30%.
Does the exam include machine learning?
Yes, at a basic level: BigQuery ML and AutoML use cases, training and evaluating models with SQL, and using Google LLMs from BigQuery.
Which languages is the exam offered in?
English and Japanese.
