Google Cloud · GCP-ADP · Intermediate

Associate Data Practitioner — Practice Questions and Mock Exam

Name: Pass-IT
Author: Pass-IT

Prepare for GCP-ADP with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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55Mock exam questions

120minTime limit

Checked against Google Cloud · August 2026 · Current exam version

Overview

About the exam

The Associate Data Practitioner certification is a newer Google Cloud credential launched in 2025, validating the ability to work with data on Google Cloud across the full data lifecycle. It covers data ingestion, transformation, pipeline orchestration, analysis, visualization, and data governance.

This certification is designed for data analysts, data practitioners, and early-career data engineers who work with Google Cloud's data tools including BigQuery, Dataflow, Pub/Sub, Looker, and Cloud Storage.

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Try five GCP-ADP questions

Try five practice questions from the app’s current Associate Data Practitioner question bank, with answers and explanations.

Data Analysis and Presentation1 / 5

Which SQL statement is used in BigQuery ML to generate predictions from a trained model?

AlexFull explanation from Alex

In BigQuery ML, ML.PREDICT generates predictions from a trained model by applying it to new data (docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict). The standard workflow uses CREATE MODEL for training, ML.EVALUATE for performance metrics, and ML.PREDICT for inference. ML.TRAIN does not exist—model training uses CREATE MODEL syntax. ML.SCORE is not a valid BigQuery ML function. ML.INFER is also not a BigQuery ML function. The correct syntax is SELECT * FROM ML.PREDICT(MODEL project.dataset.model, (SELECT ...)).

Sourcedocs.cloud.google.com

Data Preparation and Ingestion2 / 5

You are building a data pipeline that needs to continuously replicate changes from an on-premises Oracle database to BigQuery in near real-time. Which Google Cloud service is designed for this change data capture (CDC) use case?

AlexFull explanation from Alex

Datastream is Google Cloud's serverless CDC (change data capture) and replication service. It continuously reads the transaction log of source databases (Oracle, MySQL, PostgreSQL, SQL Server, AlloyDB) and streams changes to destinations like BigQuery, Cloud Storage, or Cloud SQL. This makes it ideal for near real-time replication from on-premises databases. Unlike Database Migration Service (which is focused on one-time or continuous migration to Cloud SQL/AlloyDB), Datastream specifically targets CDC use cases where you need ongoing replication to analytics destinations like BigQuery. BigQuery Data Transfer Service only imports from SaaS sources like Google Ads, not from operational databases. Storage Transfer Service moves files between storage systems, not database records. Exam tip: CDC from operational databases = Datastream.

Sourcecloud.google.com

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Question pool

300 practice questions

The Pass-IT question pool gives you material to practice for GCP-ADP. A Pass-IT mock exam uses 55 questions and a 120-minute time limit; these are practice settings.

Pool details: GCP-ADP

Objectives in the guide11 objectives listed in the official guide

across 4 domains in the official exam guide

Pool size300 questions

= The pool size is equivalent to 5 sets of 55 questions; this does not mean that each mock exam uses a separate set.

Blueprint domains4 domains in the exam blueprint

Data Preparation and Ingestion 82 · Data Analysis and Presentation 73 · Data Pipeline Orchestration 55 · Data Management 90

Recorded as checked against sources300 of 300

questions recorded as having their answer, options, and explanation checked against official Google Cloud documentation

How questions are made →

Exam domains

What's on the exam

Data preparation and ingestion carries the most weight at 30%, testing whether you can pick the right transfer tool, clean a dataset, and route it into a storage layer that fits its structure and how downstream tools will query it. Data management follows at 25%, covering IAM-based access control, storage lifecycle rules, backup and disaster recovery, and encryption choices across the data estate. Analysis and presentation makes up 27%, where BigQuery SQL, Jupyter notebooks, and Looker dashboards turn governed data into something a stakeholder can act on, alongside training and serving simple models via BigQuery ML or AutoML. Pipeline orchestration closes the blueprint at 18%, testing Dataflow, Dataproc, and Cloud Composer for scheduling and monitoring rather than building a pipeline from scratch.

The split reflects the role's hybrid nature: half analyst, half engineer. Candidates who have only queried data, never moved or governed it, underestimate the ingestion and management sections; candidates from a pure engineering background tend to underestimate how much of the exam expects fluency in BigQuery and Looker rather than pipeline code.

Exam blueprint: GCP-ADP

Data Preparation and Ingestion~30%

Cleanse and transform raw data with ETL/ELT tooling, then route it into the right Google Cloud storage layer based on its shape and how it will be consumed.

≈ 18 h

Data Analysis and Presentation~27%

Surface patterns in the data through BigQuery queries and Jupyter notebooks, turn that analysis into Looker dashboards stakeholders can act on, and train, evaluate, and serve models with BigQuery ML or AutoML.

≈ 16 h

Data Pipeline Orchestration~18%

Build straightforward transformation pipelines with tools like Dataflow, Dataproc, and Cloud Data Fusion, then schedule, automate, and watch over them using Cloud Composer and event-driven triggers.

≈ 11 h

Data Management~25%

Govern who can reach the data through IAM and Analytics Hub, set lifecycle rules that age or delete it automatically, make sure it stays available and recoverable if disaster strikes, and apply encryption to stay within privacy requirements.

≈ 15 h

Format

Exam format and question types

The exam draws 50–60 multiple-choice and multiple-select questions inside a 120-minute window, weighted roughly 80% single-answer to 20% multiple-select. Questions are scenario-based, asking you to pick the right storage system for a dataset, assemble a transformation pipeline, write a BigQuery SQL query, or apply the correct data-governance control.

Question types: GCP-ADP

Multiple Choice80%

Select the single answer that best meets the question’s requirements.

Multiple Response20%

Select multiple answers. Follow the question’s instructions on how many to choose.

See Google Cloud for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for GCP-ADP

The exam runs through Pearson VUE, either online-proctored or at a testing center, and is offered in English and Japanese. The certification holds for 3 years, with renewal options available during the renewal eligibility period.

Preparation and logistics: GCP-ADP

Preparation

Illustrative study time35–90 h

illustrative planning range: 35 h with relevant experience to 90 h when starting out; your needs may fall outside this range

LevelIntermediate

Recommended backgroundNo formal prerequisites. Recommended 6+ months of experience working with data on Google Cloud.

Taking and maintaining the certification

DeliveryOnline-proctored (Pearson VUE) or onsite-proctored at testing centers

Retake policy14-day wait after the first failed attempt, 60 days after the second, 365 days after the third. Maximum 4 attempts in a 2-year period.

Certification validity3 years

Certification valid for 3 years. Renewal options available within the renewal eligibility period.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: GCP-ADP

01BigQuery vs Cloud SQL
Not understanding when to use a data warehouse (BigQuery) vs. a relational database (Cloud SQL)
02Pipeline Tools
Confusing Dataflow, Dataproc, and Cloud Composer for different pipeline scenarios
03Data Governance
Overlooking data lineage, cataloging with Dataplex, and access control policies
04Streaming vs Batch
Misunderstanding when to use Pub/Sub + Dataflow streaming vs. batch processing

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

Which pitfalls should I review when preparing for Associate Data Practitioner?

Topics to review include BigQuery vs Cloud SQL, Pipeline Tools, Data Governance, Streaming vs Batch. Work through examples to check that you understand the distinctions and can explain your answer.

How is the Associate Data Practitioner exam weighted?

Data preparation and ingestion is the largest section at 30%, followed by data analysis and presentation at 27% and data management at 25%. Pipeline orchestration takes the remaining 18%. Getting data in and making sense of it is therefore well over half the exam.

What do you need before the Associate Data Practitioner exam?

Google Cloud sets no formal prerequisite and recommends about six months of working with data on the platform. The catalog budget is around 60 hours. It is an associate exam, so it expects familiarity with BigQuery and the console rather than architectural judgement.

How long is the Associate Data Practitioner valid?

Three years, which is longer than the two years Google Cloud gives its professional certifications. Renewal happens inside an eligibility window before the expiry date rather than at any time you choose.

What happens if you fail the Associate Data Practitioner exam?

The waiting periods escalate: 14 days after the first failure, 60 days after the second and 365 days after the third. Google Cloud also caps you at four attempts in any two-year period. A third failure is therefore expensive in time, which is an argument for sitting it only once you are ready.

Associate Data Practitioner or Professional Data Engineer?

The associate exam is about using the data platform: ingesting, preparing, analysing and presenting. The professional exam is about designing the systems that do it, and its budget is 100 hours against 60. If you are asked what a pipeline should look like rather than how to run one, the professional exam is the right target.

One certification, 12 months

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