← PDE·Google Cloud

Question 3 of 499

Designing data processing systems →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to increase the Dataflow worker count and adjust the autoscaling configuration. This is correct because a growing Pub/Sub backlog during traffic spikes signals that the Dataflow pipeline is the bottleneck—it cannot consume messages as fast as Pub/Sub publishes them. Dataflow autoscaling for Pub/Sub backlog relies on horizontal scaling; by raising the maximum worker count and fine-tuning the autoscaling parameters, the pipeline dynamically adds more workers to match the incoming throughput, thereby draining the backlog. On the Google Professional Data Engineer exam, this scenario tests your understanding of streaming pipeline throughput and the consumer-producer relationship in Pub/Sub. A common trap is to assume Pub/Sub itself is the problem, but Pub/Sub is built for high ingestion rates—the real issue is always the consumer’s processing capacity. Memory tip: “Backlog means the consumer is slow, not the source.”

PDE Designing data processing systems Practice Question

This PDE practice question tests your understanding of designing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.

A company processes IoT sensor data in near real-time. They ingest data via Cloud Pub/Sub, then a Dataflow streaming pipeline writes to Bigtable for low-latency queries. Recently, they observed increased Pub/Sub message backlog during traffic spikes. What is the most effective scaling strategy?

Question 1hardmultiple choice

Full question →

A
Increase Pub/Sub subscription throughput by increasing the number of partitions
Why wrong: Pub/Sub subscriptions do not have partitions; the backlog is at the subscription level and scaling workers is more effective.
B
Increase Dataflow worker count and adjust autoscaling configuration
Dataflow autoscaling can handle backlogs if enough workers are provisioned; increasing the max number of workers allows the pipeline to catch up during spikes.
C
Use a Cloud Scheduler to throttle Pub/Sub publishing
Why wrong: Throttling publishing defeats the real-time purpose and may cause data loss.
D
Add a Cloud Function to pre-process messages before they are consumed by Dataflow
Why wrong: Adding a Cloud Function adds an extra hop and does not directly improve Dataflow's processing capacity.

Full breakdown with real-world context →

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

✓

Increase Dataflow worker count and adjust autoscaling configuration

The correct answer is B because the increased Pub/Sub backlog during traffic spikes indicates that the Dataflow pipeline is unable to consume messages as fast as they are being published. Increasing the Dataflow worker count and adjusting autoscaling configuration allows the pipeline to scale horizontally, processing more messages per second and reducing the backlog. Pub/Sub itself is designed to handle high throughput, so the bottleneck is the consumer (Dataflow), not the ingestion layer.

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.

✗
Increase Pub/Sub subscription throughput by increasing the number of partitions
Why it's wrong here
Pub/Sub subscriptions do not have partitions; the backlog is at the subscription level and scaling workers is more effective.
✓
Increase Dataflow worker count and adjust autoscaling configuration
Why this is correct
Dataflow autoscaling can handle backlogs if enough workers are provisioned; increasing the max number of workers allows the pipeline to catch up during spikes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a Cloud Scheduler to throttle Pub/Sub publishing
Why it's wrong here
Throttling publishing defeats the real-time purpose and may cause data loss.
✗
Add a Cloud Function to pre-process messages before they are consumed by Dataflow
Why it's wrong here
Adding a Cloud Function adds an extra hop and does not directly improve Dataflow's processing capacity.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates mistakenly think Pub/Sub's throughput is limited by partitions (like Kafka) or that throttling the publisher is a valid scaling strategy, when in fact the bottleneck is the streaming pipeline's processing capacity, which must be scaled horizontally.

Detailed technical explanation

How to think about this question

Dataflow's autoscaling is based on CPU utilization and backlog metrics; during traffic spikes, the pipeline may not scale quickly enough if the autoscaling configuration is too conservative (e.g., max workers too low or scaling algorithm set to 'none'). Pub/Sub uses a pull-based model where each worker can lease multiple messages, but if the worker pool is too small, messages accumulate in the subscription backlog. Adjusting the autoscaling parameters, such as setting a higher maximum number of workers or enabling throughput-based scaling, ensures the pipeline can dynamically add resources to match the incoming load.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

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.

Related PDE practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Designing data processing systems practice questions

Practise PDE questions linked to Designing data processing systems.

Building and operationalizing data processing systems practice questions

Practise PDE questions linked to Building and operationalizing data processing systems.

Operationalizing machine learning models practice questions

Practise PDE questions linked to Operationalizing machine learning models.

Ensuring solution quality practice questions

Practise PDE questions linked to Ensuring solution quality.

PDE fundamentals practice questions

Practise PDE questions linked to PDE fundamentals.

PDE scenario practice questions

Practise PDE questions linked to PDE scenario.

PDE troubleshooting practice questions

Practise PDE questions linked to PDE troubleshooting.

Practice this exam

Start a free PDE practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

PDE practice-test guide →Study guide →Browse all practice tests

FAQ

Questions learners often ask

What does this PDE question test?

Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Increase Dataflow worker count and adjust autoscaling configuration — The correct answer is B because the increased Pub/Sub backlog during traffic spikes indicates that the Dataflow pipeline is unable to consume messages as fast as they are being published. Increasing the Dataflow worker count and adjusting autoscaling configuration allows the pipeline to scale horizontally, processing more messages per second and reducing the backlog. Pub/Sub itself is designed to handle high throughput, so the bottleneck is the consumer (Dataflow), not the ingestion layer.

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.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

Same concept, more angles

1 more ways this is tested on PDE

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A gaming company uses Pub/Sub to ingest player events and Dataflow for real-time analytics. They notice that the Pub/Sub subscription backlog is growing despite the Dataflow pipeline running continuously. The pipeline has a 1-hour window for aggregations. What is the most effective way to reduce the backlog?

hard

✓ A.Increase the Dataflow pipeline's worker count via autoscaling.
B.Use a push subscription instead of pull.
C.Decrease the window duration to 10 minutes.
D.Enable Pub/Sub topic retention.

Why A: Increasing the Dataflow pipeline's worker count via autoscaling directly addresses the backlog by adding more parallel processing capacity to consume messages from the Pub/Sub subscription faster. Since the pipeline is continuously running but the backlog grows, the bottleneck is processing throughput, not pipeline availability. Autoscaling allows Dataflow to dynamically allocate more workers based on the backlog size, matching consumption rate to the incoming message rate.

Keep practising

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

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.