PDE Designing Data Processing Systems Practice Question
A data engineer needs to run an existing Spark job on Google Cloud with minimal code changes. The job requires Hive metastore access. Which Dataproc feature should they use to provide a managed Hive metastore?
⚠ Common exam trap
PDE often tests the confusion between a backing database (Cloud SQL) and a managed metastore service (Dataproc Metastore); candidates pick Cloud SQL thinking MySQL is the Hive metastore, missing that the managed service is the correct answer.
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
✓
Dataproc Metastore
Dataproc Metastore is a fully managed, highly available Hive metastore service (based on Hive Metastore 2.3/3.1) that can be attached to Dataproc clusters, providing a persistent, serverless metastore without running a separate Hive metastore on a cluster. It allows existing Spark jobs that require Hive metastore access to run with minimal code changes, since the metastore endpoint is configured via cluster properties.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud SQL for MySQL
Why it's wrong here
Cloud SQL for MySQL is a relational database, not a Hive metastore service; Spark's Hive client cannot read its schema without custom configuration and code changes. It tempts because Hive's legacy metastore schema runs on MySQL, but Dataproc Metastore is the managed, Hive-compatible offering.
- ✓
Dataproc Metastore
Why this is correct
Dataproc Metastore is a fully managed, Hive-compatible metastore service that existing Spark jobs connect to via the standard Hive metastore interface, so no code changes are needed. It satisfies the managed Hive metastore requirement directly, unlike cluster-local or self-managed alternatives.
- ✗
BigQuery as a Hive metastore
Why it's wrong here
BigQuery is an analytical warehouse, not a Hive-compatible metastore, so Spark jobs expecting the Hive Thrift metastore interface cannot use it without rewriting catalog calls. It tempts as a central metadata store, but Dataproc Metastore is the managed service providing that Hive-compatible endpoint.
- ✗
Dataproc on GKE
Why it's wrong here
Dataproc on GKE runs Spark workloads on Kubernetes but does not itself supply a managed Hive metastore; you would still configure one separately. It tempts because it hosts Dataproc jobs, yet the requirement is a managed metastore endpoint, which Dataproc Metastore provides independently of the compute platform.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.