- A
Dataflow for preprocessing and writing results to Cloud Storage
Dataflow can perform complex transforms at scale.
- B
Cloud Functions to preprocess data row by row
Why wrong: Cloud Functions has timeout and scalability limitations for large volumes.
- C
Cloud Run to serve the preprocessed data as an API
Why wrong: Cloud Run is for online serving, not batch preprocessing.
- D
Vertex AI Batch Prediction with Cloud Storage source
Batch Prediction reads preprocessed data from GCS efficiently.
- E
Vertex AI Batch Prediction with BigQuery source
Why wrong: BigQuery source is not suitable for complex preprocessing; Dataflow is better.
Question 727 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 team is building a batch prediction pipeline that processes raw data from Cloud Storage, performs complex preprocessing, and then runs predictions using a large model. The preprocessing step is compute-intensive and the prediction step is I/O-bound. Which TWO Google Cloud services should they combine to optimize cost and performance? (Choose 2)
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
✓
Dataflow for preprocessing and writing results to Cloud Storage
Dataflow is ideal for the compute-intensive preprocessing step because it can horizontally scale across many workers to handle complex transformations in parallel, and it writes results directly to Cloud Storage, which serves as the input source for Vertex AI Batch Prediction. Vertex AI Batch Prediction is optimized for I/O-bound inference workloads: it reads batches of data from Cloud Storage, runs predictions using the large model, and writes results back to Cloud Storage, all without requiring a persistent serving endpoint, which minimizes cost for offline predictions.
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.
- ✓
Dataflow for preprocessing and writing results to Cloud Storage
Why this is correct
Dataflow can perform complex transforms at scale.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Functions to preprocess data row by row
Why it's wrong here
Cloud Functions has timeout and scalability limitations for large volumes.
- ✗
Cloud Run to serve the preprocessed data as an API
Why it's wrong here
Cloud Run is for online serving, not batch preprocessing.
- ✓
Vertex AI Batch Prediction with Cloud Storage source
Why this is correct
Batch Prediction reads preprocessed data from GCS efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Vertex AI Batch Prediction with BigQuery source
Why it's wrong here
BigQuery source is not suitable for complex preprocessing; Dataflow is better.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between batch and online serving patterns, and the trap here is that candidates may choose Cloud Functions or Cloud Run for preprocessing because they are familiar serverless options, without realizing that Dataflow is purpose-built for large-scale, compute-intensive batch processing and that Vertex AI Batch Prediction is the correct service for offline inference at scale.
Detailed technical explanation
How to think about this question
Dataflow uses the Apache Beam SDK to define preprocessing pipelines that can be executed on Google-managed resources with autoscaling; it supports features like side inputs and windowing for complex transformations. Vertex AI Batch Prediction leverages the model's serving container to process input files in parallel across multiple machines, and it automatically handles retries and error logging for large-scale inference jobs. In practice, a common pattern is to use Dataflow to transform raw CSV or Avro files into TFRecord format stored in Cloud Storage, then point Vertex AI Batch Prediction to that bucket, which avoids the cost of a dedicated prediction endpoint.
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.
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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: Dataflow for preprocessing and writing results to Cloud Storage — Dataflow is ideal for the compute-intensive preprocessing step because it can horizontally scale across many workers to handle complex transformations in parallel, and it writes results directly to Cloud Storage, which serves as the input source for Vertex AI Batch Prediction. Vertex AI Batch Prediction is optimized for I/O-bound inference workloads: it reads batches of data from Cloud Storage, runs predictions using the large model, and writes results back to Cloud Storage, all without requiring a persistent serving endpoint, which minimizes cost for offline predictions.
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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