- A
Use Vertex AI batch prediction with BigQuery source and sink.
Correct. Vertex AI batch prediction natively supports BigQuery as both source and sink.
- B
Use Cloud Run jobs to read from BigQuery and write results back.
Why wrong: Cloud Run jobs are not designed for large-scale batch prediction and lack built-in model serving.
- C
Export BigQuery data to GCS, then run a Dataflow pipeline to call the model's online prediction endpoint for each row.
Why wrong: This adds unnecessary complexity and cost compared to using Vertex AI batch prediction directly.
- D
Use Cloud Dataproc to spin up a Spark cluster and run the model inference in parallel.
Why wrong: Overkill and more expensive than managed batch prediction.
Question 132 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 10 TB of text data stored in BigQuery using a custom container model hosted in Vertex AI. What is the most cost-effective and simple approach?
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 source and sink.
Vertex AI batch prediction natively supports BigQuery as both source and sink, allowing you to run predictions on 10 TB of text data without any data movement or intermediate storage. This is the most cost-effective and simple approach because it eliminates the need for exporting data, managing infrastructure, or calling online endpoints, and it leverages Vertex AI's optimized batch inference infrastructure that scales automatically.
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.
- ✓
Use Vertex AI batch prediction with BigQuery source and sink.
Why this is correct
Correct. Vertex AI batch prediction natively supports BigQuery as both source and sink.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Run jobs to read from BigQuery and write results back.
Why it's wrong here
Cloud Run jobs are not designed for large-scale batch prediction and lack built-in model serving.
- ✗
Export BigQuery data to GCS, then run a Dataflow pipeline to call the model's online prediction endpoint for each row.
Why it's wrong here
This adds unnecessary complexity and cost compared to using Vertex AI batch prediction directly.
- ✗
Use Cloud Dataproc to spin up a Spark cluster and run the model inference in parallel.
Why it's wrong here
Overkill and more expensive than managed batch prediction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that you must export data from BigQuery to GCS before running batch predictions, when in fact Vertex AI batch prediction can directly read from and write to BigQuery, making the export step unnecessary and cost-inefficient.
Detailed technical explanation
How to think about this question
Vertex AI batch prediction uses a distributed architecture that shards the input data (e.g., BigQuery tables) into manageable chunks and processes them in parallel across multiple workers, automatically handling retries and checkpointing. Under the hood, it reads the BigQuery source using the BigQuery Storage API for high-throughput streaming, and writes predictions back to a BigQuery table using the BigQuery Write API, ensuring exactly-once semantics. In real-world scenarios, this approach is ideal for large-scale offline inference (e.g., monthly customer churn scoring on terabytes of user data) where latency is not critical but cost and simplicity are paramount.
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.
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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 source and sink. — Vertex AI batch prediction natively supports BigQuery as both source and sink, allowing you to run predictions on 10 TB of text data without any data movement or intermediate storage. This is the most cost-effective and simple approach because it eliminates the need for exporting data, managing infrastructure, or calling online endpoints, and it leverages Vertex AI's optimized batch inference infrastructure that scales automatically.
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
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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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