- A
Use Pub/Sub with Dataflow, writing to BigQuery using the Storage Write API in committed mode.
This provides low-latency streaming, late data handling via Dataflow's triggers, and efficient writes.
- B
Use Cloud Logging to capture logs and export to BigQuery via a sink.
Why wrong: This is for logs, not real-time clickstream data, and introduces minutes of delay.
- C
Use Pub/Sub with Cloud Functions, writing each event directly via BigQuery legacy streaming inserts.
Why wrong: Cloud Functions have a timeout limit and are not ideal for high-throughput streaming; legacy streaming inserts have a 1-day quota and lack exactly-once semantics.
- D
Use Datastream to stream clickstream data from Cloud SQL to BigQuery.
Why wrong: Datastream is for CDC from databases, not for web clickstream data.
PDE Ingesting and Processing the Data Practice Question
This PDE practice question tests your understanding of ingesting and processing the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are building a streaming pipeline to ingest real-time clickstream data from a website into BigQuery for immediate analysis. The data must be available in BigQuery within seconds and you need to handle late-arriving data (e.g., browser offline events) that may arrive hours later. Which approach should you use?
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 Pub/Sub with Dataflow, writing to BigQuery using the Storage Write API in committed mode.
Option A is correct because Pub/Sub provides a scalable, durable ingestion layer for real-time clickstream data, and Dataflow can handle late-arriving data via its built-in watermark and trigger mechanisms. The Storage Write API in committed mode ensures exactly-once semantics and low-latency writes to BigQuery, meeting the sub-second availability requirement while preserving data consistency for delayed events.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Pub/Sub with Dataflow, writing to BigQuery using the Storage Write API in committed mode.
Why this is correct
This provides low-latency streaming, late data handling via Dataflow's triggers, and efficient writes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Logging to capture logs and export to BigQuery via a sink.
Why it's wrong here
This is for logs, not real-time clickstream data, and introduces minutes of delay.
- ✗
Use Pub/Sub with Cloud Functions, writing each event directly via BigQuery legacy streaming inserts.
Why it's wrong here
Cloud Functions have a timeout limit and are not ideal for high-throughput streaming; legacy streaming inserts have a 1-day quota and lack exactly-once semantics.
- ✗
Use Datastream to stream clickstream data from Cloud SQL to BigQuery.
Why it's wrong here
Datastream is for CDC from databases, not for web clickstream data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume legacy streaming inserts (Option C) are sufficient for real-time needs, but they overlook the 1-hour buffer delay and lack of late-data handling, which are explicitly tested in the PDE exam's focus on streaming pipelines with out-of-order events.
Detailed technical explanation
How to think about this question
Dataflow's handling of late-arriving data relies on event-time processing with configurable allowed lateness (e.g., hours) and accumulation triggers, which re-compute windows when out-of-order events arrive. The Storage Write API in committed mode uses a two-phase commit protocol to guarantee exactly-once delivery, avoiding duplicates that could arise from retries in streaming pipelines. In a real-world scenario, a browser offline for 2 hours would emit events with timestamps in the past; Dataflow's watermark mechanism would hold the window open until the allowed lateness expires, then append the late records to BigQuery without reprocessing the entire window.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PDE question test?
Ingesting and Processing the Data — This question tests Ingesting and Processing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Pub/Sub with Dataflow, writing to BigQuery using the Storage Write API in committed mode. — Option A is correct because Pub/Sub provides a scalable, durable ingestion layer for real-time clickstream data, and Dataflow can handle late-arriving data via its built-in watermark and trigger mechanisms. The Storage Write API in committed mode ensures exactly-once semantics and low-latency writes to BigQuery, meeting the sub-second availability requirement while preserving data consistency for delayed events.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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