- A
Write a Cloud Composer workflow that runs the preprocessing and then triggers the batch prediction job.
Why wrong: Cloud Composer orchestrates but does not perform the actual data processing; Dataflow is better.
- B
Use Dataflow to read from both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source.
Dataflow handles the complex join and scales; batch prediction can read from GCS.
- C
Use Vertex AI batch prediction with a custom container that includes logic to read and join tables on the fly.
Why wrong: This mixes serving and preprocessing, and may not scale efficiently for large data.
- D
Use BigQuery to create a materialized view that joins the tables and directly use that as the batch prediction source.
Why wrong: Batch prediction does not support materialized views directly; it expects a table.
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 are using Vertex AI batch prediction and your model requires preprocessing that involves joining two BigQuery tables. The preprocessing logic is complex and must be done before inference. How should you design the 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
Use Dataflow to read from both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source.
Option B is correct because Dataflow (Apache Beam) is designed for complex, stateful data processing like joining two BigQuery tables and performing custom preprocessing. It can read from BigQuery, execute the join logic, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction then reads the preprocessed data from GCS, which is the recommended pattern for non-trivial transformations before inference, as it decouples preprocessing from prediction and avoids resource contention.
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.
- ✗
Write a Cloud Composer workflow that runs the preprocessing and then triggers the batch prediction job.
Why it's wrong here
Cloud Composer orchestrates but does not perform the actual data processing; Dataflow is better.
- ✓
Use Dataflow to read from both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source.
Why this is correct
Dataflow handles the complex join and scales; batch prediction can read from GCS.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI batch prediction with a custom container that includes logic to read and join tables on the fly.
Why it's wrong here
This mixes serving and preprocessing, and may not scale efficiently for large data.
- ✗
Use BigQuery to create a materialized view that joins the tables and directly use that as the batch prediction source.
Why it's wrong here
Batch prediction does not support materialized views directly; it expects a table.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that batch prediction can handle live data transformations within the prediction container, but the correct design is to preprocess data in a separate, scalable data processing service like Dataflow before feeding it to batch prediction.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow uses the Beam SDK to create a pipeline that reads from BigQuery via the BigQuery I/O connector, which uses the Storage API for high-throughput reads. The join operation is performed as a CoGroupByKey transform, and the preprocessing logic can include custom ParDo functions. Writing the output to GCS in JSON Lines format ensures compatibility with Vertex AI batch prediction, which reads input from GCS and writes predictions to a specified output location. In a real-world scenario, this pattern is critical when the preprocessing involves windowed aggregations or stateful operations that cannot be expressed as simple SQL queries.
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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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 both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source. — Option B is correct because Dataflow (Apache Beam) is designed for complex, stateful data processing like joining two BigQuery tables and performing custom preprocessing. It can read from BigQuery, execute the join logic, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction then reads the preprocessed data from GCS, which is the recommended pattern for non-trivial transformations before inference, as it decouples preprocessing from prediction and avoids resource contention.
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.
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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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