- A
Use Cloud Functions to transform each file individually.
Why wrong: Cloud Functions is not designed for large-scale batch processing (50TB). It's better suited for event-driven, short-lived tasks.
- B
Use Cloud SQL to store intermediate results.
Why wrong: Cloud SQL is a relational database not optimized for storing intermediate results of large-scale data pipelines. Cloud Storage or Dataflow's internal storage is more appropriate.
- C
Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.
Vertex AI batch prediction jobs natively support reading data from TFRecords stored in GCS, making this step efficient and correct.
- D
Use Dataflow to read CSV, perform feature engineering, and write to GCS in TFRecord format.
Dataflow provides distributed processing capable of handling 50TB of data, performing complex transformations, and writing TFRecords to GCS for batch prediction.
- E
Use Dataflow to read CSV, perform feature engineering, and write to BigQuery.
Why wrong: While Dataflow can write to BigQuery, batch prediction on Vertex AI requires input in TFRecord or JSON Lines format from GCS, so this step does not produce the correct input format.
PMLE Vertex AI Batch Prediction 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. A key principle to apply: vertex AI Batch Prediction. 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 are designing a batch prediction pipeline using Vertex AI. The input data is 50 TB in CSV format on GCS. The model requires feature engineering that involves complex transformations (e.g., datetime parsing, one-hot encoding). Which THREE services or steps should you include in your pipeline?
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
Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.
The pipeline requires two main steps: first, use Dataflow to read CSV files, perform feature engineering, and write the processed data as TFRecords to Cloud Storage; second, run a Vertex AI batch prediction job with the GCS source pointing to these TFRecords. Cloud Functions and Cloud SQL are not suitable for handling 50TB of data. Writing to BigQuery is not appropriate for batch prediction with Vertex AI, which expects TFRecord or JSON Lines input. The question asks for three steps, but only two services are necessary; the third step is implicitly the storage of input and output in Cloud Storage.
Key principle: Vertex AI Batch Prediction
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 Cloud Functions to transform each file individually.
Why it's wrong here
Cloud Functions is not designed for large-scale batch processing (50TB). It's better suited for event-driven, short-lived tasks.
- ✗
Use Cloud SQL to store intermediate results.
Why it's wrong here
Cloud SQL is a relational database not optimized for storing intermediate results of large-scale data pipelines. Cloud Storage or Dataflow's internal storage is more appropriate.
- ✓
Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.
Why this is correct
Vertex AI batch prediction jobs natively support reading data from TFRecords stored in GCS, making this step efficient and correct.
Related concept
Vertex AI Batch Prediction
- ✓
Use Dataflow to read CSV, perform feature engineering, and write to GCS in TFRecord format.
Why this is correct
Dataflow provides distributed processing capable of handling 50TB of data, performing complex transformations, and writing TFRecords to GCS for batch prediction.
Related concept
Vertex AI Batch Prediction
- ✗
Use Dataflow to read CSV, perform feature engineering, and write to BigQuery.
Why it's wrong here
While Dataflow can write to BigQuery, batch prediction on Vertex AI requires input in TFRecord or JSON Lines format from GCS, so this step does not produce the correct input format.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume that any scalable service can handle batch processing, but Cloud Functions and Cloud SQL are unsuitable for 50TB. The trap is that the question asks for three steps, but only two distinct services (Dataflow and Vertex AI) are required; the third step is implicit (Cloud Storage).
Detailed technical explanation
How to think about this question
Dataflow (option D) is the correct choice for distributed data processing because it uses Apache Beam under the hood, allowing you to read CSV files from GCS, apply complex feature engineering (e.g., datetime parsing, one-hot encoding) in a scalable, parallel manner, and write the output as TFRecord files. TFRecord is a binary format that TensorFlow models consume natively, reducing I/O overhead during batch prediction. In real-world scenarios, using Dataflow with autoscaling can handle terabytes of data by dynamically adjusting worker count based on throughput, while Cloud Functions would fail due to resource constraints.
KKey Concepts to Remember
- Vertex AI Batch Prediction
- Dataflow
- TFRecord
- Cloud Storage
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
Vertex AI Batch Prediction
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.
Review vertex AI Batch Prediction, then practise related PMLE questions on the same topic to reinforce the concept.
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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 — Vertex AI Batch Prediction.
What is the correct answer to this question?
The correct answer is: Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files. — The pipeline requires two main steps: first, use Dataflow to read CSV files, perform feature engineering, and write the processed data as TFRecords to Cloud Storage; second, run a Vertex AI batch prediction job with the GCS source pointing to these TFRecords. Cloud Functions and Cloud SQL are not suitable for handling 50TB of data. Writing to BigQuery is not appropriate for batch prediction with Vertex AI, which expects TFRecord or JSON Lines input. The question asks for three steps, but only two services are necessary; the third step is implicitly the storage of input and output in Cloud Storage.
What should I do if I get this PMLE question wrong?
Review vertex AI Batch Prediction, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Vertex AI Batch Prediction
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Last reviewed: Jul 4, 2026
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