When to load first and when to transform first, and what sits between the two
ETL, ELT, and ETLT describe the order in which data extraction, transformation, and loading occur, and Google Cloud supports all three patterns depending on the target warehouse and transformation needs. Choosing between them depends on where you want transformation logic to run (in a dedicated processing engine vs. in the warehouse itself) and how much raw data you need to retain.
Must-know
- ETL (Extract, Transform, Load) transforms data before loading using tools like Dataflow or Dataproc, and is typical when data must be cleaned, masked, or restructured before it reaches the destination, such as loading into Cloud SQL or Bigtable.
- ELT (Extract, Load, Transform) loads raw data first, then transforms it using the destination engine's compute, and is the dominant pattern with BigQuery because it can run large-scale SQL transformations directly on loaded data.
- ETLT (Extract, Transform, Load, Transform) applies light transformations (e.g., filtering, format conversion, PII masking) before loading, then applies further business-logic transformations inside the warehouse, combining benefits of both approaches.
- Cloud Data Fusion and Dataflow are commonly used for ETL or the first-T in ETLT, while BigQuery SQL, scheduled queries, and dbt-style transformations are used for the ELT/second-T stage.
- ELT is generally preferred on Google Cloud when using BigQuery because it separates storage and compute, allowing raw data to be retained for reprocessing and reducing pipeline complexity.
- ETL is often chosen when transformations must occur before data reaches a destination that lacks strong transformation capabilities, or when compliance requires masking/redaction prior to storage.
A retail analytics team ingests raw sales transaction files into Cloud Storage, then loads them unchanged into BigQuery staging tables. All cleansing, aggregation, and reshaping into reporting tables is performed afterward using scheduled BigQuery SQL queries. Which data manipulation methodology does this pipeline follow?
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.