ADP — Google Cloud Certified Associate Data Practitioner
Google

Google Cloud Certified Associate Data Practitioner (ADP) Practice Questions

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65 questions
2026-06-22 updated
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Domain coverage

  • Data Preparation and Ingestion (~30%)
  • Data Analysis and Presentation (~27%)
  • Data Pipeline Orchestration (~18%)
  • Data Management and Governance (~25%)

Sample Questions (7 of 65 shown)

Q1 Understanding Data and Data Types
Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular. You need to design a storage system that is simple and cost-effective. What should you do?
  1. Create a single-region bucket with Autoclass enabled.
  2. Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.
  3. Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.
  4. Create a single-region bucket with Archive as the default storage class.
✓ Correct Answer: A
A. Cloud Storage Autoclass automatically moves objects between Standard, Nearline, Coldline, and Archive classes based on access patterns, providing optimal cost without manual lifecycle rules. Custom lifecycle (C) and Cloud Scheduler (B) require manual configuration. Archive default (D) would be expensive for frequently accessed new files.
Q2 Understanding Data and Data Types
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
  1. EL
  2. ELT
  3. ETL
  4. ETLT
✓ Correct Answer: C
C. ETL (Extract, Transform, Load) transforms data before loading, allowing SQL injection removal during the transform phase before data reaches BigQuery. EL (A) has no transform step. ELT (B) loads raw data first - risky with SQL injection. ETLT (D) is non-standard and adds unnecessary complexity.
Q3 Understanding Data and Data Types
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?
  1. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
  2. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
  3. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
  4. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
✓ Correct Answer: A
A. Cloud Composer with PythonOperator provides a managed Apache Airflow environment for cleaning data with Python, ideal for complex data quality tasks before loading to BigQuery. BigQuery batch load (B) doesn't allow pre-load Python cleaning. Storage Transfer + Cloud Run (C) adds complexity. Cloud Run functions (D) lack orchestration for complex pipelines.
Q4 Understanding Data and Data Types
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud. You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?
  1. Migrate the Spark jobs to Dataproc Serverless.
  2. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
  3. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
  4. Migrate the Spark jobs to Dataproc on Compute Engine.
✓ Correct Answer: A
A. Dataproc Serverless runs Spark jobs without cluster provisioning or management, using auto-scaling infrastructure. GKE with Spark operators (B) requires cluster management. Dataproc on GKE (C) still needs K8s management. Dataproc on Compute Engine (D) requires cluster provisioning.
Q5 Understanding Data and Data Types
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?
  1. Create a custom quota for each analyst in BigQuery.
  2. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
  3. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
  4. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
✓ Correct Answer: D
D. BigQuery authorized views allow sharing specific query results with teams without granting direct table access. Dataset-level IAM (A) is too broad. Data Catalog (B) is metadata management. BigQuery subscriptions (C) are for Pub/Sub integration, not access control.
Q6 Understanding Data and Data Types
You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?
  1. Use Cloud CDN to cache frequently accessed data.
  2. Store frequently accessed data in a Memorystore instance.
  3. Migrate the database to a larger Cloud SQL instance.
  4. Enable automatic backups, and create a read replica of the Cloud SQL instance.
✓ Correct Answer: D
D. Cloud SQL read replicas offload read queries from the primary, improving read performance without modifying the application database. Memorystore (A) requires cache code changes. Load balancing reads (B) needs application changes. Cloud SQL connection pooling (C) manages connections, not reads.
Q7 Understanding Data and Data Types
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?
  1. Retry messages until they are acknowledged.
  2. Implement flow control on the subscribers.
  3. Forward unacknowledged messages to a dead-letter topic.
  4. Seek back to the last acknowledged message.
✓ Correct Answer: B
B. Dataflow streaming with Pub/Sub subscription processes IoT events in real-time with auto-scaling, perfect for variable IoT workloads. Dataproc Spark Streaming (A) requires cluster management. Cloud Functions (C) has execution time limits unsuitable for sustained streaming. Cloud Composer (D) orchestrates but doesn't process streams.

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Exam overview

The Google Cloud Associate Data Practitioner (ADP) certification is the definitive associate-level credential for data professionals working on Google Cloud Platform. Unlike general-purpose cloud certifications, ADP is laser-focused on what data practitioners actually do every day: ingesting and transforming data, building analysis pipelines in BigQuery, creating Looker dashboards, and orchestrating everything with Dataflow and Cloud Composer. If your daily work involves SQL, dashboards, and data pipelines, ADP is the certification that proves you can deliver.

Our ADP practice test suite draws from the official exam guide and reflects the exact format of the real certification. With 400+ unique questions spanning all four domains, each question comes with detailed explanations that connect theoretical knowledge to practical execution—whether you're choosing between ETL and ELT for a streaming pipeline, configuring BigQuery table partitioning, or setting IAM roles for Dataplex governance. This is not memorization; this is building the diagnostic intuition that data practitioners use every day.

What makes ADP uniquely challenging—and uniquely valuable—is its breadth. The exam tests your ability to shift seamlessly between data preparation (Storage Transfer Service, Dataflow), deep SQL analysis (BigQuery WINDOW functions, QUALIFY, JSON), pipeline orchestration (Composer DAGs, Workflows, Eventarc), and governance (Dataplex, policy tags, CMEK). Few certifications in the cloud market require this level of end-to-end data fluency. At $125, ADP offers exceptional ROI for data analysts, BI engineers, and junior data engineers looking to differentiate themselves in a competitive job market.

Official Exam Domains & Weighting

To successfully pass the ADP exam, candidates must demonstrate mastery across the following four core domains:
  • Domain 1: Data Preparation and Ingestion (~30%) — ETL vs ELT selection, data transfer tools (Storage Transfer Service, Transfer Appliance), data quality assessment, extraction to BigQuery/Cloud Storage/Cloud SQL, and file format handling (CSV, JSON, Parquet, Avro).
  • Domain 2: Data Analysis and Presentation (~27%) — BigQuery SQL analysis (WINDOW, CTE, QUALIFY), Jupyter notebooks in Colab Enterprise, Looker and Looker Studio dashboard creation, BigQuery ML model training, and pretrained LLM integration via BigQuery remote connections.
  • Domain 3: Data Pipeline Orchestration (~18%) — Data transformation tool selection (Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform), scheduled query management, Dataflow pipeline monitoring, and event-driven ingestion from Pub/Sub to BigQuery via Eventarc.
  • Domain 4: Data Management and Governance (~25%) — IAM least-privilege access for data services, Cloud Storage access control, Analytics Hub data sharing, lifecycle management and archival strategies, high-availability with backup and replication, and encryption compliance (CMEK, CSEK, Cloud KMS).

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Frequently Asked Questions

ADP is data-specific. ACE (Associate Cloud Engineer) covers general GCP operations—VMs, networking, IAM—while ADP focuses exclusively on data ingestion, analysis, pipelines, and governance. PDE (Professional Data Engineer) is the advanced version of ADP, requiring deeper architectural design skills across multi-service solutions. Our practice tests help you see exactly where ADP's scope ends and PDE's begins.

Google uses a Pass/Fail system without publishing the exact cutoff, but community feedback suggests ~70%. No penalty for wrong answers—answer every question. Aim for 80%+ consistency on our full-length practice exams before scheduling.

If you have 6+ months of data experience with SQL and BigQuery, 4-6 weeks (1-2 hours/day) is typical. Complete beginners should budget 10-12 weeks. Prior SQL fluency is a major advantage—our questions include real BigQuery SQL scenarios that test WINDOW functions, QUALIFY clauses, and JSON handling.

BigQuery dominates—expect questions on table design (partitioning, clustering), query optimization, and ML model creation. Dataflow pipeline concepts and Cloud Composer DAG design follow closely. Cloud Storage lifecycle policies, Looker/Looker Studio, and IAM for data services round out the top topics. Our question distribution mirrors this weighting.

Yes, online-proctored through Kryterion. The exam is available in English and Japanese. Our practice tests are accessible 24/7 in English, and the Japanese version of our product page provides native-language preparation for Japanese-speaking candidates.

3 years—longer than the standard 2-year validity of most Google Cloud certifications. This reflects the more stable nature of data engineering fundamentals compared to rapidly evolving cloud infrastructure services.

Yes—DEA-C01 validates AWS data skills, while ADP validates GCP data skills. In a multi-cloud job market, holding both credentials positions you as a versatile data professional who can work across platforms. The foundational data concepts overlap, but the tools (BigQuery vs Redshift, Dataflow vs Glue) are different enough that dedicated ADP preparation is valuable.