Judging whether a dataset is trustworthy enough to build on
Assessing data quality in Google Cloud typically involves profiling datasets to understand their structure and anomalies, then defining and running rules to check dimensions like completeness, uniqueness, validity, and accuracy. Dataplex is the primary managed service for automated data profiling and data quality scanning across BigQuery and Cloud Storage data, while BigQuery SQL and Cloud Data Fusion's Wrangler provide additional ways to inspect and validate data.
Must-know
- Dataplex Data Profiling scans automatically compute statistics (null %, distinct count, min/max, value distributions) on BigQuery tables to help you understand data quality before writing rules.
- Dataplex Data Quality scans let you define and schedule rules (e.g., null checks, range checks, regex/uniqueness checks) and can auto-generate suggested rules from a profiling scan's results.
- Dataplex Data Quality results integrate with BigQuery so failed-row details and quality scores can be queried and monitored over time, including alerting via Cloud Monitoring/Logging.
- Cloud Data Fusion's Wrangler lets you interactively explore sample data and apply directives (dedupe, parse, mask) while previewing the impact, useful for spotting quality issues during pipeline design.
- Common data quality dimensions tested on the exam are completeness, uniqueness, validity, consistency, accuracy, and timeliness. Know these terms conceptually.
- Simple ad hoc quality checks (duplicate counts via COUNT(DISTINCT), null counts, referential integrity) can be done directly with BigQuery SQL when a managed profiling/quality service isn't required.
A data analyst has just been given read access to a large BigQuery table and needs a quick statistical overview, including null percentage, approximate distinct count, and min/max values for each column, before deciding which cleansing steps are needed. The analyst wants this without writing any SQL. Which Dataplex capability should the analyst use?
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.