- A
Deploy the model on Vertex AI Prediction for batch prediction
Why wrong: Vertex AI Prediction is for online predictions, not batch; it would not help with batch scalability.
- B
Change Pub/Sub to a push subscription that sends messages to a load-balanced group of VMs
Push subscriptions with load balancing allow horizontal scaling across multiple VMs.
- C
Use Dataflow to read from Pub/Sub, run predictions using the model, and write to BigQuery
Dataflow provides distributed processing with auto-scaling, handling large volumes efficiently.
- D
Switch to a larger VM with more memory
Why wrong: A larger VM is a vertical scaling approach that is limited and does not solve the bottleneck.
- E
Store results in Cloud SQL instead of BigQuery
Why wrong: Cloud SQL is not optimized for large-scale analytical workloads and does not improve prediction speed.
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 media company uses a custom Python script on a Compute Engine VM to run batch predictions with a large ML model. The script loads the model from Cloud Storage, processes records from a Pub/Sub pull subscription, and writes results to BigQuery. Predictions are taking too long and the VM often runs out of memory. Which two changes should the company implement to improve performance and scalability? (Choose TWO)
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
Change Pub/Sub to a push subscription that sends messages to a load-balanced group of VMs
Option B is correct because switching to a push subscription with a load-balanced group of VMs distributes the message processing load across multiple instances, preventing any single VM from being overwhelmed. This directly addresses the memory exhaustion issue by parallelizing the work and allowing horizontal scaling.
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.
- ✗
Deploy the model on Vertex AI Prediction for batch prediction
Why it's wrong here
Vertex AI Prediction is for online predictions, not batch; it would not help with batch scalability.
- ✓
Change Pub/Sub to a push subscription that sends messages to a load-balanced group of VMs
Why this is correct
Push subscriptions with load balancing allow horizontal scaling across multiple VMs.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Dataflow to read from Pub/Sub, run predictions using the model, and write to BigQuery
Why this is correct
Dataflow provides distributed processing with auto-scaling, handling large volumes efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a larger VM with more memory
Why it's wrong here
A larger VM is a vertical scaling approach that is limited and does not solve the bottleneck.
- ✗
Store results in Cloud SQL instead of BigQuery
Why it's wrong here
Cloud SQL is not optimized for large-scale analytical workloads and does not improve prediction speed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between vertical scaling (larger VM) and horizontal scaling (load-balanced VMs or Dataflow), where candidates mistakenly choose a larger VM thinking it solves memory issues without recognizing the scalability bottleneck.
Detailed technical explanation
How to think about this question
Pub/Sub push subscriptions use HTTP POST requests to deliver messages to a configured endpoint, allowing a load balancer to distribute traffic across a backend service (e.g., a managed instance group). This enables auto-scaling based on message throughput, while pull subscriptions require the client to poll, which can lead to backpressure and resource contention on a single VM. Dataflow (Option C) is also correct because it provides a fully managed, autoscaling stream processing service that can read from Pub/Sub, run custom Python code (e.g., via the Apache Beam SDK) for predictions, and write to BigQuery, eliminating the need to manage VMs and memory.
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 PMLE question test?
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change Pub/Sub to a push subscription that sends messages to a load-balanced group of VMs — Option B is correct because switching to a push subscription with a load-balanced group of VMs distributes the message processing load across multiple instances, preventing any single VM from being overwhelmed. This directly addresses the memory exhaustion issue by parallelizing the work and allowing horizontal scaling.
What should I do if I get this PMLE 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 →
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Last reviewed: Jun 30, 2026
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.
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