- A
Use a Cloud Function to preprocess each row and write to a new BigQuery table, then run batch prediction.
Why wrong: Cloud Functions have a timeout and are not designed for large-scale data processing.
- B
Use Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source.
Dataflow handles large-scale preprocessing and the pipeline integrates well with Vertex AI.
- C
Use Vertex AI batch prediction with BigQuery source and include preprocessing logic in the model container.
Why wrong: Model container should only contain the model; preprocessing should be separate for maintainability.
- D
Export BigQuery data to CSV, run a local Python script for preprocessing, then upload to GCS and start a batch prediction job.
Why wrong: Not scalable for 10 TB and introduces unnecessary data movement.
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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.
You need to perform batch predictions on 10 TB of data stored in BigQuery using Vertex AI. The model requires some preprocessing that cannot be expressed in SQL. What is the most scalable 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 Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source.
Option B is correct because Dataflow (Apache Beam) provides a fully managed, auto-scaling, serverless execution environment that can read from BigQuery, apply arbitrary Python/Java preprocessing logic (e.g., feature engineering, normalization) that cannot be expressed in SQL, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction can then read from GCS as input, making this the most scalable approach for 10 TB of data without requiring custom model container changes or manual data movement.
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 a Cloud Function to preprocess each row and write to a new BigQuery table, then run batch prediction.
Why it's wrong here
Cloud Functions have a timeout and are not designed for large-scale data processing.
- ✓
Use Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source.
Why this is correct
Dataflow handles large-scale preprocessing and the pipeline integrates well with Vertex AI.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI batch prediction with BigQuery source and include preprocessing logic in the model container.
Why it's wrong here
Model container should only contain the model; preprocessing should be separate for maintainability.
- ✗
Export BigQuery data to CSV, run a local Python script for preprocessing, then upload to GCS and start a batch prediction job.
Why it's wrong here
Not scalable for 10 TB and introduces unnecessary data movement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that Cloud Functions can handle large-scale batch processing, but the trap here is that Cloud Functions are designed for event-driven, short-lived tasks, not for processing terabytes of data in a batch pipeline. Dataflow is the appropriate Google Cloud service for this scenario.
Detailed technical explanation
How to think about this question
Dataflow uses the Apache Beam SDK to distribute preprocessing across hundreds of workers, automatically scaling based on the size of the data (e.g., 10 TB). It reads from BigQuery via the BigQuery I/O connector, which uses dynamic work rebalancing to avoid stragglers, and writes to GCS in Avro or TFRecord format, which Vertex AI batch prediction can consume natively. A real-world scenario is a fraud detection model requiring custom feature hashing and time-window aggregations that cannot be expressed in SQL; Dataflow handles this at petabyte scale without manual sharding.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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 Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source. — Option B is correct because Dataflow (Apache Beam) provides a fully managed, auto-scaling, serverless execution environment that can read from BigQuery, apply arbitrary Python/Java preprocessing logic (e.g., feature engineering, normalization) that cannot be expressed in SQL, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction can then read from GCS as input, making this the most scalable approach for 10 TB of data without requiring custom model container changes or manual data movement.
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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