- A
Switch to a machine type with more CPU cores and vCPUs
Why wrong: Better machine type may help but increasing parallelism is more cost-effective for batch.
- B
Increase the machine count (number of worker replicas) in the batch prediction job
More workers process data in parallel, reducing runtime linearly with cost.
- C
Downsample the dataset to 500k rows
Why wrong: Downsampling reduces accuracy; not an acceptable solution.
- D
Use Online prediction instead of batch
Why wrong: Online prediction is for real-time, not batch; cost would be higher and may not fit time constraint.
Question 170 of 1,000
Speeding Up Vertex AI Batch Prediction with Workers
This PDE practice question tests your understanding of operationalizing machine learning models. 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 run batch predictions using Vertex AI Batch Prediction on a tabular dataset. The job processes 1 million rows and takes 6 hours to complete. You need to reduce the processing time to under 2 hours without increasing cost significantly. What should you do?
Quick Answer
The answer is to increase the machine count, or number of worker replicas, in the batch prediction job. This approach directly speeds up Vertex AI batch prediction workers by distributing the 1 million rows across multiple machines, allowing parallel processing to dramatically cut wall-clock time from six hours to under two without significantly increasing cost, as you pay only for the total compute time used. On the Google Professional Data Engineer exam, this tests your understanding of batch prediction job configuration and the trade-off between vertical scaling (larger machine types) and horizontal scaling (more workers); the common trap is choosing a more powerful machine type, which offers diminishing returns and higher per-hour costs. Remember the key principle for batch jobs: more workers equals more parallelism, and for tabular data, increasing the machine count is the most cost-effective lever. A simple memory tip is “parallel workers, not bigger boxes” to avoid the costly mistake of upgrading machine types unnecessarily.
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 the machine count (number of worker replicas) in the batch prediction job
Vertex AI Batch Prediction supports distributed processing by increasing the number of worker replicas. By adding more workers, the job can process partitions of the 1 million rows in parallel, reducing wall-clock time from 6 hours to under 2 hours. This approach scales horizontally without requiring more expensive machine types, keeping costs roughly linear with the number of workers and avoiding the diminishing returns of vertical 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.
- ✗
Switch to a machine type with more CPU cores and vCPUs
Why it's wrong here
Better machine type may help but increasing parallelism is more cost-effective for batch.
- ✓
Increase the machine count (number of worker replicas) in the batch prediction job
Why this is correct
More workers process data in parallel, reducing runtime linearly with cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Downsample the dataset to 500k rows
Why it's wrong here
Downsampling reduces accuracy; not an acceptable solution.
- ✗
Use Online prediction instead of batch
Why it's wrong here
Online prediction is for real-time, not batch; cost would be higher and may not fit time constraint.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between vertical scaling (more powerful machines) and horizontal scaling (more machines), where candidates mistakenly choose a more expensive machine type (Option A) instead of the cost-effective parallelization approach (Option B).
Detailed technical explanation
How to think about this question
Vertex AI Batch Prediction automatically shards the input dataset into partitions based on the number of worker replicas, using a distributed file system (e.g., Cloud Storage) for intermediate results. The job's parallelism is limited by the `machine_count` parameter, which defines the number of worker nodes in the prediction cluster. In practice, increasing workers from 1 to 4 can reduce runtime by nearly 4x, assuming linear scaling, but network I/O and model loading overhead may cause sub-linear speedup; for tabular models, the bottleneck is often the model's inference latency per row, which benefits from data parallelism across workers.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the machine count (number of worker replicas) in the batch prediction job — Vertex AI Batch Prediction supports distributed processing by increasing the number of worker replicas. By adding more workers, the job can process partitions of the 1 million rows in parallel, reducing wall-clock time from 6 hours to under 2 hours. This approach scales horizontally without requiring more expensive machine types, keeping costs roughly linear with the number of workers and avoiding the diminishing returns of vertical scaling.
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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