- A
Implement custom retry logic with exponential backoff in the application.
Why wrong: Retry logic only handles transient failures; it does not provide capacity to absorb traffic spikes.
- B
Use Cloud SQL as a temporary buffer and process from there.
Why wrong: Cloud SQL is not designed for high-throughput streaming buffering and can become a bottleneck.
- C
Pre-provision 3x the expected peak capacity to handle spikes.
Why wrong: Pre-provisioning is wasteful and does not dynamically adapt; it can lead to unused resources.
- D
Use a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog.
Pub/Sub provides a highly scalable buffer; autoscaling consumers based on backlog ensures capacity matches demand.
Question 875 of 1,000
Handle Traffic Spikes in Streaming Applications on GKE
This PDE practice question tests your understanding of ensuring solution quality. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 is deploying a large-scale streaming application on Google Kubernetes Engine. They need to ensure the application can handle sudden traffic spikes without dropping data. Which architectural pattern is most appropriate?
Quick Answer
The answer is to use a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog. This pattern is correct because Pub/Sub acts as a durable, scalable buffer that decouples data producers from consumers, allowing the streaming application on GKE to absorb sudden traffic spikes without dropping data. The key technical concept is that by monitoring the subscription backlog (the number of unacknowledged messages), you can trigger Horizontal Pod Autoscaling to dynamically spin up more consumer pods exactly when demand surges, ensuring throughput scales with load. On the Google Professional Data Engineer exam, this question tests your understanding of decoupling patterns for streaming workloads, a common scenario where candidates mistakenly choose static overprovisioning or database-based buffering. A frequent trap is assuming Cloud SQL can handle high-throughput buffering, but it lacks Pub/Sub’s native autoscaling and durability guarantees. Memory tip: think “Backlog = Burst” — when the backlog grows, pods must burst to match it.
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 a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog.
Option D is correct because Pub/Sub provides a durable, scalable, and asynchronous message buffer that decouples the producer from the consumer. By autoscaling consumer pods based on the Pub/Sub subscription backlog (e.g., using the 'pubsub.googleapis.com/subscription/num_undelivered_messages' custom metric with Horizontal Pod Autoscaler), the application can elastically handle traffic spikes without data loss, as messages are persisted until acknowledged.
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.
- ✗
Implement custom retry logic with exponential backoff in the application.
Why it's wrong here
Retry logic only handles transient failures; it does not provide capacity to absorb traffic spikes.
- ✗
Use Cloud SQL as a temporary buffer and process from there.
Why it's wrong here
Cloud SQL is not designed for high-throughput streaming buffering and can become a bottleneck.
- ✗
Pre-provision 3x the expected peak capacity to handle spikes.
Why it's wrong here
Pre-provisioning is wasteful and does not dynamically adapt; it can lead to unused resources.
- ✓
Use a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog.
Why this is correct
Pub/Sub provides a highly scalable buffer; autoscaling consumers based on backlog ensures capacity matches demand.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse buffering with retry logic or database storage, failing to recognize that Pub/Sub is the Google Cloud-native service specifically designed for decoupling and buffering in event-driven architectures.
Detailed technical explanation
How to think about this question
Pub/Sub uses a pull-based model where consumers acknowledge messages after processing, and unacknowledged messages remain in the subscription backlog for up to 7 days (configurable). The Horizontal Pod Autoscaler can use the 'pubsub.googleapis.com|subscription|num_undelivered_messages' metric from Stackdriver to scale consumer pods, ensuring that backlog size drives capacity. In a real-world scenario, if a flash crowd generates 10x normal traffic, the backlog grows, HPA scales pods, and messages are not lost—unlike a direct HTTP endpoint that would return 503 errors.
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.
Visual reference
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.
- →
Ensuring solution quality — study guide chapter
Learn the concepts, then practise the questions
- →
Ensuring solution quality practice questions
Targeted practice on this topic area only
- →
All PDE questions
1,000 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
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.
Ingesting and Processing the Data practice questions
Practise PDE questions linked to Ingesting and Processing the Data.
Storing the Data practice questions
Practise PDE questions linked to Storing the Data.
Preparing and Using Data for Analysis practice questions
Practise PDE questions linked to Preparing and Using Data for Analysis.
Maintaining and Automating Data Workloads practice questions
Practise PDE questions linked to Maintaining and Automating Data Workloads.
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.
FAQ
Questions learners often ask
What does this PDE question test?
Ensuring solution quality — This question tests Ensuring solution quality — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog. — Option D is correct because Pub/Sub provides a durable, scalable, and asynchronous message buffer that decouples the producer from the consumer. By autoscaling consumer pods based on the Pub/Sub subscription backlog (e.g., using the 'pubsub.googleapis.com/subscription/num_undelivered_messages' custom metric with Horizontal Pod Autoscaler), the application can elastically handle traffic spikes without data loss, as messages are persisted until acknowledged.
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 →
Keep practising
More PDE practice questions
- A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated…
- A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a s…
- A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data qua…
- Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from B…
- A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into Big…
- A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a Tens…
Last reviewed: Jul 4, 2026
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.
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.
Sign in to join the discussion.