Weighing whether the transform belongs before or after the load
ELT and ETL are two approaches to moving and transforming data for analytics, differing mainly in when transformation happens relative to loading. On Google Cloud, the choice affects which services you use, how quickly raw data is available, and where transformation logic lives.
Must-know
- ETL (Extract, Transform, Load) transforms data before loading, typically using Dataflow or Dataproc, and is well-suited when data must be cleaned, masked, or restructured before it reaches the warehouse due to compliance, quality, or schema requirements.
- ELT (Extract, Load, Transform) loads raw data first, then transforms it in-place using SQL, commonly with BigQuery leveraging its scalable compute to run transformations after load.
- ELT is favored for BigQuery-centric analytics because it preserves raw data for reprocessing, simplifies pipelines, and lets analysts iterate on transformations using SQL without re-extracting source data.
- ETL is preferred when transformations are complex, require non-SQL processing (e.g., custom code, ML preprocessing), or when data must be filtered/redacted before storage for governance or cost reasons.
- Dataflow supports both patterns but is especially suited to ETL for streaming or complex batch transformations; Dataprep/Cloud Data Fusion offer low-code options for either pattern.
- A common gotcha: ELT can increase storage and compute costs in the warehouse if raw, unfiltered data volumes are large, so ETL may be more cost-effective when only a small subset of data is ultimately needed.
A retail company lands raw clickstream JSON files in Cloud Storage every night. Analysts want to explore the data with SQL right away and iterate quickly on cleaning and aggregation logic without standing up separate transformation compute. Which pipeline design best fits an ELT approach for this use case?
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.