Fixing messy records before they reach a report
Data cleaning on Google Cloud can be performed with code-free tools like Cloud Data Fusion (via Wrangler and prebuilt transformation directives) or with code-based approaches using BigQuery SQL for in-warehouse cleaning and Dataflow for large-scale, pipelined batch/streaming transformations. The Associate Data Practitioner exam expects you to know which tool fits a given cleaning scenario based on skill level, data volume, and whether processing is batch or streaming.
Must-know
- Cloud Data Fusion's Wrangler provides a visual, spreadsheet-like interface with prebuilt 'directives' (e.g., parse, deduplicate, mask, fill null) for non-engineers to clean data without writing code.
- BigQuery supports data cleaning directly in SQL using functions like TRIM, CAST, IFNULL/COALESCE,
REGEXP_REPLACE, and DISTINCT/QUALIFY for deduplication, ideal when data is already loaded into BigQuery. - Dataflow (based on Apache Beam) is the preferred choice for large-scale, complex, or streaming data cleaning/transformation pipelines that need custom logic beyond SQL, and it scales automatically.
- Cloud Data Fusion pipelines can include a Wrangler transform stage that generates a recipe of directives, which can then be reused and scheduled as part of a full ETL/ELT pipeline.
- BigQuery's built-in data quality/scheduled queries or MERGE statements are commonly used to deduplicate, standardize, or update data incrementally without needing a separate ETL tool.
- For streaming or near-real-time cleaning (e.g., filtering malformed records before landing in BigQuery), Dataflow is generally favored over Cloud Data Fusion or plain SQL, which are better suited to batch/ELT-style cleaning.
A data practitioner has a BigQuery table of order records where the same transaction_id can appear multiple times because an upstream system occasionally resends events. Each duplicate has a different processing_timestamp. The practitioner needs a query that returns only the most recent version of each transaction_id. Which approach correctly accomplishes this?
What you have tried across GCP ADP's objectives, not a readiness score.
Data Preparation and Ingestion
- When to load first and when to transform first, and what sits between the two
- Picking a way to move existing data into Google Cloud
- Judging whether a dataset is trustworthy enough to build on
- Fixing messy records before they reach a report
- Telling CSV, JSON, Parquet, Avro, and relational tables apart, and where each fits
- Picking how to pull data out of a source system
- Matching a workload to the right storage or database service
- Getting files and tables loaded with a CLI, a transfer service, or a client library
Data Analysis and Presentation
- Writing BigQuery SQL that answers a reporting question
- Exploring and charting data inside a hosted notebook
- Turning a question from the business into an analysis that settles it
- Building a dashboard and getting it in front of the right people
- Deciding whether a job calls for Looker or for Looker Studio
- Editing LookML to change what a model exposes
- Spotting a problem worth solving with BigQuery ML or AutoML
- Calling a hosted Google language model straight from BigQuery
- Sequencing a machine learning project from raw data to served predictions
- Building, fitting, and scoring a model with SQL alone
- Running predictions against a model you already trained
- Keeping trained models catalogued in one place
Data Pipeline Orchestration
- Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
- Weighing whether the transform belongs before or after the load
- Assembling the services a simple transformation pipeline needs
- Putting a query on a schedule and keeping it running
- Watching a Dataflow job and spotting where it stalls
- Reading logs and metrics to work out what a pipeline actually did
- Choosing what should drive a multi-step workflow
- Streaming messages into BigQuery as they arrive rather than in batches
- Wiring a trigger so one event starts the next step
Data Management
- Granting only the access a person or service actually needs
- Controlling who can read a bucket, and what uniform access changes
- Sharing a dataset with another team or company without copying it
- Matching a storage class to how often the data gets read
- Expiring old data automatically so it stops costing money
- Picking somewhere to park data that must be kept but is rarely read
- Comparing the managed backup and restore options across services
- Working out when a second copy is worth what it costs
- Regions, dual-regions, multi-regions, and zones as redundancy choices
- Deciding who should hold the encryption keys
- What a key management service does for creating, rotating, and revoking keys
- Protecting data on the wire versus data sitting on a disk
Coverage checked against the published exam guide on Aug 12, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.