- A
Export the BigQuery data to CSV in GCS, then run a custom Dataflow pipeline to make predictions.
Why wrong: This adds unnecessary complexity and cost with Dataflow.
- B
Use Vertex AI batch prediction with BigQuery as the source and sink.
Batch prediction natively supports BigQuery, is cost-effective, and scales automatically.
- C
Use Vertex AI online prediction and send all rows as separate requests.
Why wrong: Online prediction is for real-time, not batch; it would be slow and expensive for 10M rows.
- D
Use a custom container running on Google Kubernetes Engine to perform inference.
Why wrong: This adds operational overhead; Vertex AI batch prediction is simpler.
Question 913 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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 need to run batch predictions on a large dataset stored in BigQuery using a Vertex AI model. The dataset contains 10 million rows, and each prediction takes about 100ms. You want to minimize cost and execution time. What should you do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 Vertex AI batch prediction with BigQuery as the source and sink.
Vertex AI batch prediction natively supports BigQuery as both input and output, eliminating the need for data export or custom pipelines. For 10 million rows at 100ms each, batch prediction processes them in parallel across multiple machines, minimizing execution time while avoiding the per-node costs of online prediction or the overhead of managing Dataflow or GKE clusters.
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.
- ✗
Export the BigQuery data to CSV in GCS, then run a custom Dataflow pipeline to make predictions.
Why it's wrong here
This adds unnecessary complexity and cost with Dataflow.
- ✓
Use Vertex AI batch prediction with BigQuery as the source and sink.
Why this is correct
Batch prediction natively supports BigQuery, is cost-effective, and scales automatically.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI online prediction and send all rows as separate requests.
Why it's wrong here
Online prediction is for real-time, not batch; it would be slow and expensive for 10M rows.
- ✗
Use a custom container running on Google Kubernetes Engine to perform inference.
Why it's wrong here
This adds operational overhead; Vertex AI batch prediction is simpler.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between batch and online prediction, trapping candidates who overlook that batch prediction is purpose-built for large-scale, offline inference with native BigQuery integration, while online prediction is for real-time, low-latency use cases.
Detailed technical explanation
How to think about this question
Vertex AI batch prediction uses a distributed architecture that shards the input data across multiple workers, each processing a subset of rows concurrently. It supports BigQuery as a source via the `bigquery` input type, and results can be written back to BigQuery or Cloud Storage, leveraging the same model endpoint used for online predictions. In practice, this approach is ideal for offline inference on large datasets where latency is not critical, as it automatically handles retries and scales based on the number of rows.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Serving and Scaling Models — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Vertex AI batch prediction with BigQuery as the source and sink. — Vertex AI batch prediction natively supports BigQuery as both input and output, eliminating the need for data export or custom pipelines. For 10 million rows at 100ms each, batch prediction processes them in parallel across multiple machines, minimizing execution time while avoiding the per-node costs of online prediction or the overhead of managing Dataflow or GKE clusters.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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