PDE Preparing and Using Data for Analysis Practice Question
A data scientist wants to use Vertex AI Workbench for exploratory data analysis. Which TWO statements are true about Vertex AI Workbench?
⚠ Common exam trap
PDE often tests the distinction between serverless and managed services, so the trap is assuming Workbench scales to zero like Cloud Functions, when it actually runs on persistent VMs.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
It supports custom container images for the notebook environment.
Option B is correct because Vertex AI Workbench instances let you specify a custom container image for the notebook environment, so you can bring your own dependencies and tooling rather than being limited to the default image. Option D is correct because Vertex AI Workbench provides a managed JupyterLab environment with pre-installed ML libraries (such as TensorFlow, PyTorch, and scikit-learn), which is exactly what a data scientist needs for exploratory data analysis. Option A is incorrect because Workbench instances are user-managed VMs that do not scale to zero; they keep running (and incurring cost) until stopped. Option C is incorrect because Workbench supports multiple frameworks and languages, not only TensorFlow. Option E is incorrect because a built-in SQL query editor for BigQuery is a feature of BigQuery and other tools, not a defining capability of Vertex AI Workbench.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It is a serverless service that scales to zero when not in use.
Why it's wrong here
Workbench instances are user-managed notebooks or managed notebooks running on persistent Compute Engine VMs; they do not scale to zero. The claim tempts because serverless, scale-to-zero behaviour describes other Vertex AI components such as training jobs or prediction endpoints, not the notebook environment itself.
- ✓
It supports custom container images for the notebook environment.
Why this is correct
Custom container images let the notebook instance run with bespoke dependencies, frameworks or system packages beyond the default image. This satisfies reproducibility and version-pinning needs during exploratory data analysis when specific library builds are required.
- ✗
It can only be used with TensorFlow.
Why it's wrong here
Workbench supports any framework or library installable in its environment, including PyTorch, scikit-learn and R, so a TensorFlow-only restriction is false. The claim tempts because TensorFlow is heavily associated with Vertex AI, but the platform is framework-agnostic, letting data scientists run whatever exploratory analysis they need.
- ✓
It provides a managed JupyterLab environment with pre-installed ML libraries.
Why this is correct
Vertex AI Workbench supplies a fully managed JupyterLab instance, removing infrastructure provisioning from the data scientist, and ships with common ML and data libraries pre-installed. This lets exploratory data analysis begin immediately rather than after environment setup.
- ✗
It includes a built-in SQL query editor for BigQuery.
Why it's wrong here
Workbench provides managed JupyterLab notebooks, not a BigQuery SQL editor; querying BigQuery happens in the console or BigQuery Studio. The claim tempts because Workbench integrates with Google Cloud data services, but a built-in SQL editor is a BigQuery feature, so it misstates Workbench's actual capability.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.