GCP-ADP Data Preparation And Ingestion Practice Question
You are building a streaming pipeline using Dataflow to process sensor data arriving via Pub/Sub. You need to handle out-of-order data by allowing events to arrive late. Which Dataflow concept must you configure?
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
✓
Watermarks
Watermarks and triggers are used in Dataflow to handle windowing and late data arrival in streaming pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Side Inputs
Why it's wrong here
Side inputs provide extra data to transforms, not handling lateness.
- ✗
Partitioning
Why it's wrong here
Partitioning is for output distribution, not event timing.
- ✗
GroupByKey
Why it's wrong here
This is a transform, not a mechanism for handling event time lateness.
- ✓
Watermarks
Why this is correct
Watermarks track when the system expects all data for a specific time window has arrived.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Google Cloud exam blueprint
This GCP-ADP 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 GCP-ADP exam.