Exploring and charting data inside a hosted notebook
Colab Enterprise provides a managed Jupyter notebook environment integrated into Google Cloud that lets you analyze and visualize data directly against BigQuery, Cloud Storage, and other GCP data sources without managing infrastructure. It combines the familiar Colab notebook interface with enterprise features like IAM-based access control, Vertex AI integration, and shared runtime templates for consistent compute environments.
Must-know
- Colab Enterprise notebooks run on Google-managed or custom runtime templates, allowing teams to standardize compute resources (CPU/GPU/memory) and control costs centrally.
- Colab Enterprise is accessed through the Vertex AI section of Google Cloud console and uses IAM permissions, so notebook access and BigQuery/storage access follow standard Google Cloud identity controls rather than notebook-level passwords.
- You can query BigQuery directly from a notebook cell using the BigQuery client library (google.cloud.bigquery) or the %%bigquery magic, then load results into a pandas DataFrame for visualization with libraries like matplotlib or seaborn.
- Notebooks can be shared and co-edited similarly to Google Docs, with version history, and results/output cells are saved with the notebook for collaboration and reproducibility.
- Colab Enterprise integrates with Vertex AI, so notebooks can also be used for ML model prototyping, training, and calling generative AI APIs, not just data exploration.
- A common gotcha: BigQuery query costs are incurred each time a notebook cell queries a table, so exam scenarios may test awareness of using LIMIT, sampling, or cached results to control cost during exploratory analysis.
A data practitioner opens a Colab Enterprise notebook to analyze a BigQuery table containing several billion rows of sales transactions. The notebook's runtime has a modest amount of memory, and the practitioner wants to run pandas-style filtering and aggregation without pulling the full table into the notebook's local memory. What should they do?
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.