hardMultiple Choice
PMLE Use ML to predict customer churn Practice Question
A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?
⚠ Common exam trap
Google Cloud often tests the misconception that AutoML Tables can handle multi-source data natively, when in fact it requires a single pre-joined dataset, and that Cloud Functions are suitable for heavy preprocessing workloads despite their strict resource limits.
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
✓
Use BigQuery to join logs and account data, train on Vertex AI, deploy to an endpoint
It leverages BigQuery's ability to join structured account data with semi-structured logs (via federated queries or external tables), then uses Vertex AI for end-to-end ML training and deployment. This architecture minimizes data movement, keeps the pipeline serverless, and directly addresses the requirement for an automated pipeline with both data sources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Load both data sources into AutoML Tables and train directly
Why it's wrong here
AutoML Tables accepts a single tabular dataset, so it cannot ingest Cloud Storage logs and BigQuery account data together without prior joining; the stem requires an automated pipeline combining both sources. AutoML Tables suits one clean flat table, not multi-source ingestion.
- ✗
Export logs from Cloud Storage to Cloud Dataproc for preprocessing, then train
Why it's wrong here
Cloud Dataproc is a managed Hadoop/Spark service for batch processing, not an automated ML pipeline orchestrator; exporting logs there adds manual staging and no BigQuery integration. Dataproc fits lift-and-shift Spark jobs, not churn pipelines needing scheduled training and deployment.
- ✗
Use Cloud Functions to preprocess data, then train on AI Platform
Why it's wrong here
Cloud Functions have execution time and memory limits unsuited to large-scale log preprocessing, and AI Platform training alone provides no orchestration joining Cloud Storage logs with BigQuery data. Cloud Functions fit lightweight event-driven tasks, not end-to-end ML pipelines.
- ✓
Use BigQuery to join logs and account data, train on Vertex AI, deploy to an endpoint
Why this is correct
BigQuery joins the Cloud Storage activity logs with account data using external tables or load jobs, Vertex AI trains the churn model on that consolidated dataset, and endpoint deployment serves predictions. This satisfies the automated pipeline requirement while keeping data processing within managed Google Cloud services.
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 |
Go deeper
Related to this question
About these practice questions
Courseiva writes every PMLE question from scratch — 775 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.