- A
Export the model to Cloud Functions and trigger on file upload
Why wrong: Cloud Functions is for event-driven, not batch processing.
- B
Create a Vertex AI Batch Prediction job with GCS input and BigQuery output
Batch Prediction directly supports this configuration.
- C
Use Vertex AI Online Prediction with a batch job
Why wrong: Online prediction is for real-time, not batch workloads.
- D
Use Dataflow to read from GCS and write to BigQuery, calling the model for each record
Why wrong: While possible, it is more complex than using Vertex AI Batch Prediction directly.
Question 499 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.
A company needs to run batch predictions on 10 TB of data stored in Cloud Storage. The predictions should be written to BigQuery. Which approach should they use?
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
✓
Create a Vertex AI Batch Prediction job with GCS input and BigQuery output
Vertex AI Batch Prediction natively supports reading input from Cloud Storage and writing predictions directly to BigQuery, making it the most efficient and fully managed solution for large-scale batch inference on 10 TB of data. This approach avoids the complexity of custom infrastructure or per-record model calls, leveraging Vertex AI's optimized batch processing pipeline.
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 model to Cloud Functions and trigger on file upload
Why it's wrong here
Cloud Functions is for event-driven, not batch processing.
- ✓
Create a Vertex AI Batch Prediction job with GCS input and BigQuery output
Why this is correct
Batch Prediction directly supports this configuration.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI Online Prediction with a batch job
Why it's wrong here
Online prediction is for real-time, not batch workloads.
- ✗
Use Dataflow to read from GCS and write to BigQuery, calling the model for each record
Why it's wrong here
While possible, it is more complex than using Vertex AI Batch Prediction directly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
This question tests the distinction between batch and online prediction modes in Vertex AI. The trap is that candidates may confuse Vertex AI's batch prediction with using Dataflow or Cloud Functions, not realizing that Vertex AI natively supports BigQuery as a direct output destination for batch jobs.
Detailed technical explanation
How to think about this question
Vertex AI Batch Prediction automatically shards input data, distributes inference across multiple workers, and handles retries and error logging, making it suitable for petabyte-scale workloads. Under the hood, it uses a managed prediction service that can output results directly to BigQuery tables via the `bigquery` output configuration, eliminating the need for intermediate staging. In real-world scenarios, this is critical for cost and time savings when running monthly or quarterly predictions on large historical datasets.
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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Serving and Scaling Models — study guide chapter
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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: Create a Vertex AI Batch Prediction job with GCS input and BigQuery output — Vertex AI Batch Prediction natively supports reading input from Cloud Storage and writing predictions directly to BigQuery, making it the most efficient and fully managed solution for large-scale batch inference on 10 TB of data. This approach avoids the complexity of custom infrastructure or per-record model calls, leveraging Vertex AI's optimized batch processing pipeline.
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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