G O O G L E C E R T I F I C A T I O N
Associate Data Practitioner Practice Exam
Exam Number: 1013 | Last updated April 21, 2026 | 999+ questions across 4 vendor-aligned objectives
Associate Data Practitioner certification is aimed at working professionals who possess the practical knowledge Google expects on its platform. It is built for early-career data analysts, analytics engineers, and BI developers who work hands-on with Google Cloud data services, and scoring rewards candidates who translate features into measurable results rather than simply recognize service names.
Heavy-weighted areas define where study time pays back fastest: 30% targets Data Preparation and Ingestion (Cloud Storage, Dataflow templates, Datastream, Pub/Sub basics); 27% targets Data Analysis and Presentation (BigQuery SQL, Looker Studio dashboards, exploratory analysis).
Supporting domains fill out the blueprint: 25% covers Data Management (BigQuery datasets, IAM for data, lifecycle policies, Data Catalog basics); 18% covers Data Pipeline Orchestration (Cloud Composer, scheduled queries, Dataform workflows). Each still appears on the exam, so none can be safely skipped. Google updates exam guides regularly, so verify domain weights on the official certification page before you finalize a study plan. Candidates who time-box practice against each listed subtopic tend to outperform those who rely on passive review.
Every answer links to the source. Each explanation below includes a hyperlink to the exact Google documentation page the question was derived from. PowerKram is the only practice platform with source-verified explanations. Learn about our methodology →
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Question #1 - Data Analysis and Presentation
A junior analyst at a subscription company needs to return the top 10 products by revenue for the last 30 days from a large sales table in BigQuery.
Which SQL pattern is correct?
A) SELECT TOP 10 product FROM sales
B) RUN REPORT FOR product IN sales TODAY
C) SELECT product, SUM(revenue) FROM sales WHERE date_ts >= CURRENT_DATE() – INTERVAL 30 DAY GROUP BY product ORDER BY 2 DESC LIMIT 10
D) USE sales; EXPORT top_10;
Show solution
Correct answers: C – Explanation:
Standard SQL with GROUP BY, ORDER BY and LIMIT is the correct BigQuery pattern for ranked aggregates. SELECT TOP 10 is T-SQL syntax not supported by BigQuery. The other two are not valid SQL. Source: Check Source
Question #2 - Data Preparation and Ingestion
A data practitioner has 200 GB of CSV exports landing from partners every morning and needs a durable, low-cost staging location before loading into BigQuery.
Which Google Cloud service is the right staging layer?
A) Cloud Storage with a dedicated landing bucket
B) Memorystore for Redis
C) Bigtable time-series tables
D) Cloud DNS records
Show solution
Correct answers: A – Explanation:
Cloud Storage is the default durable, inexpensive staging layer for files feeding BigQuery. Memorystore is ephemeral in-memory cache. Bigtable is not meant for raw CSV staging. Cloud DNS is resolution. Source: Check Source
Question #3 - Data Pipeline Orchestration
An analyst needs to run a single daily SQL transform in BigQuery that updates a reporting table from a source table.
Which orchestration choice best matches the complexity?
A) Cloud Composer with a fully custom Airflow DAG
B) BigQuery scheduled queries
C) A Kubernetes cluster running a custom scheduler
D) A Cloud Scheduler job invoking a Compute Engine script
Show solution
Correct answers: B – Explanation:
For a single daily SQL transform, BigQuery scheduled queries are the simplest right-sized choice. Cloud Composer, a custom Kubernetes scheduler, and a Compute Engine script are all over-engineered for this task. Source: Check Source
Question #4 - Data Management
A data steward wants marketing analysts to query a BigQuery dataset but not modify or delete its tables.
Which BigQuery role fits the requirement?
A) roles/owner on the project
B) roles/bigquery.admin on the dataset
C) No role at all, relying on VPC firewall
D) roles/bigquery.dataViewer on the dataset
Show solution
Correct answers: D – Explanation:
BigQuery Data Viewer allows reading but not modifying dataset objects. Project Owner is far too broad. BigQuery Admin grants destructive permissions. VPC firewall does not govern BigQuery IAM. Source: Check Source
Question #5 - Data Analysis and Presentation
A practitioner wants to publish a free interactive dashboard reading from BigQuery that business users can filter by region and time.
Which Google tool is the simplest fit?
A) Looker Studio with a BigQuery data source
B) A static PDF emailed daily
C) A hand-built React app deployed to Cloud Run
D) An Excel file on a shared drive
Show solution
Correct answers: A – Explanation:
Looker Studio connects natively to BigQuery and publishes free interactive dashboards with filters. A PDF is not interactive. A custom React app is more work than needed. An Excel file does not query BigQuery live. Source: Check Source
Question #6 - Data Preparation and Ingestion
A team must move daily Avro files from Cloud Storage into BigQuery with some simple transformations, without building a custom pipeline framework.
Which Google Cloud option is purpose-built?
A) Compute Engine with hand-rolled code
B) Cloud Build steps
C) Dataflow templates (e.g., Cloud Storage Avro to BigQuery)
D) Memorystore
Show solution
Correct answers: C – Explanation:
Google-provided Dataflow templates cover the common Cloud Storage to BigQuery ETL pattern with minimal configuration. Compute Engine hand-rolled code and Cloud Build steps are not purpose-built for streaming/batch ETL. Memorystore is unrelated. Source: Check Source
Question #7 - Data Management
A practitioner wants Cloud Storage objects older than 90 days to move automatically to a cheaper storage class.
Which Google Cloud feature fits?
A) Manual ‘move’ job every week
B) Cloud Storage Object Lifecycle Management rules
C) Cloud CDN eviction policy
D) BigQuery partition expiration
Show solution
Correct answers: B – Explanation:
Object Lifecycle Management rules can transition objects between storage classes on age. Manual jobs do not scale. CDN eviction is cache-specific. BigQuery partition expiration is for BigQuery tables. Source: Check Source
Question #8 - Data Pipeline Orchestration
A team wants to version-control SQL transformations in BigQuery and run them as a DAG with dependencies and tests.
Which Google Cloud service is designed for that?
A) Cloud Storage bucket with notes
B) Memorystore
C) Cloud DNS
D) Dataform inside BigQuery
Show solution
Correct answers: D – Explanation:
Dataform manages SQL workflows with version control, dependencies, and tests on top of BigQuery. Cloud Storage, Memorystore, and Cloud DNS do not orchestrate SQL transforms. Source: Check Source
Question #9 - Data Analysis and Presentation
An analyst has received a new BigQuery table and wants to quickly inspect column types, null counts, and distributions before modeling.
Which Google Cloud feature supports that exploratory analysis?
A) BigQuery’s table details and Explore with Looker Studio or SQL profiling queries
B) Deleting the table and recreating it
C) Asking the table to describe itself via email
D) Disabling BigQuery permissions
Show solution
Correct answers: A – Explanation:
BigQuery’s schema pane, sample query, and integration with Looker Studio or SQL profiling queries are the standard exploratory path. Deleting and recreating loses data. The other options are not real workflows. Source: Check Source
Question #10 - Data Management
A data steward wants to search for datasets across the organization using business-friendly tags such as ‘PII’ or ‘Finance’.
Which Google Cloud service is the right fit?
A) A Google Sheet listing datasets
B) Dataplex (includes Data Catalog) with tag templates
C) Cloud Storage folder naming conventions
D) A manual email thread
Show solution
Correct answers: B – Explanation:
Dataplex Catalog (Data Catalog) supports tag templates and discovery across Google Cloud data assets. A Sheet, folder names, or email threads do not scale as a governed catalog. Source: Check Source
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Exam mode & learn mode · Score by objective · Updated April 21, 2026
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What the Data Practitioner exam measures
- Data Preparation and Ingestion (30%): Apply Google Cloud practices to Cloud Storage, Dataflow templates, Datastream, Pub/Sub basics.
- Data Analysis and Presentation (27%): Apply Google Cloud practices to BigQuery SQL, Looker Studio dashboards, exploratory analysis.
- Data Pipeline Orchestration (18%): Apply Google Cloud practices to Cloud Composer, scheduled queries, Dataform workflows.
- Data Management (25%): Apply Google Cloud practices to BigQuery datasets, IAM for data, lifecycle policies, Data Catalog basics.
How to prepare for this exam
- Review the Associate Data Practitioner official exam guide end to end before you commit a study plan, so every later hour is spent against the published blueprint.
- Complete the relevant Google Cloud Skills Boost learning path and treat its labs as non-optional rather than extra credit.
- Get hands-on practice in Qwiklabs sandbox, repeating the same tasks from memory until configuration feels routine.
- Apply what you learn in real-world project experience — your day job, a volunteer project, or an open-source contribution — so the concepts stick.
- Master one objective at a time, starting with the highest-weighted domain on the blueprint and moving down from there.
- Use PowerKram learn mode with feedback and sourced links to close gaps while the answer rationale is still fresh.
- Finish with PowerKram exam mode across all objectives under realistic time pressure before you book the real exam.
Career paths and salary outlook
Holding the Associate Data Practitioner certification typically supports roles such as:
- Data Analyst (GCP): roughly $ 80,000 to $115,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Glassdoor.
- Analytics Engineer: roughly $ 95,000 to $140,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Levels.fyi.
- BI Developer: roughly $ 90,000 to $130,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Payscale.
Official resources
Work directly from Google’s own preparation resources and treat third-party content as a supplement: