Matching a workload to the right storage or database service
For the Associate Data Practitioner exam, you must match a data scenario to the right Google Cloud storage service based on data structure, access pattern, and scale rather than just familiarity. The core decision hinges on whether data is structured or unstructured, needs SQL analytics, requires transactional consistency, or must serve high-throughput operational workloads.
Must-know
- Cloud Storage is for unstructured/semi-structured data (files, images, backups, staging area for ingestion pipelines) using an object storage model, not a database.
- BigQuery is the choice for large-scale structured/semi-structured analytical (OLAP) workloads where you need SQL queries over massive datasets, not for low-latency transactional updates.
- Cloud SQL is a managed relational database (MySQL, PostgreSQL, SQL Server) suited for traditional OLTP workloads with moderate scale and standard SQL compatibility needs.
- Spanner is chosen when you need a globally distributed, horizontally scalable relational database with strong consistency and high availability, typically for large enterprise transactional systems exceeding Cloud SQL's scale.
- Firestore is a NoSQL document database ideal for hierarchical/semi-structured JSON-like data with real-time sync, commonly used for mobile/web app backends.
- Bigtable is a NoSQL wide-column store built for very high-throughput, low-latency workloads with massive scale (e.g., time-series, IoT, or analytical data requiring single-digit millisecond access), not for complex ad hoc SQL queries.
An IoT platform ingests sensor readings from millions of devices, writing tens of thousands of rows per second. Applications need single-digit millisecond lookups of the latest readings for a given device using a key composed of device ID and timestamp, but no SQL joins across entities are required. Which storage solution best fits this workload?
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 13, 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.