- A
Implement a caching mechanism in the pipeline that stores the results of the BigQuery query and reuses them if the data hasn't changed.
Why wrong: Pipeline caching is based on component inputs, not on data content, so it may not prevent rerun if inputs differ.
- B
Move the feature extraction to a separate scheduled query in BigQuery and load the results into a table that the pipeline reads from.
This separates concerns and avoids redundant execution, while still allowing data drift detection via the pipeline.
- C
Reduce the pipeline frequency to once a day to minimize the number of runs.
Why wrong: This reduces cost but delays model updates and data drift detection.
- D
Use a conditional pipeline that checks if the data has changed before running the feature extraction step.
Why wrong: This adds complexity and still requires executing the pipeline to perform the check.
Quick Answer
The answer is to move the feature extraction to a separate scheduled query in BigQuery and load the results into a table that the pipeline reads from. This approach is correct because it decouples the expensive feature engineering step from the training pipeline, eliminating redundant query execution when the underlying data hasn’t changed. By using a scheduled BigQuery query to precompute features into a materialized table, you reduce pipeline cost by separating BigQuery feature extraction from the training run, while still detecting data drifts by adjusting the query’s schedule frequency. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of cost optimization through pipeline decoupling and the trade-off between freshness and efficiency. A common trap is to cache results within the pipeline itself, which still incurs query costs on each run, or to reduce the pipeline frequency, which sacrifices drift detection. Remember the memory tip: “Schedule the extraction, not the execution”—precompute features once per hour, then let your pipeline read the ready table.
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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.
Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI and uses BigQuery for feature engineering. Recently, the pipeline has been taking significantly longer to execute. Upon investigation, you find that the BigQuery query for feature extraction is being rerun every time the pipeline runs, even though the underlying data hasn't changed. The pipeline is scheduled to run every hour. You want to reduce cost and execution time without losing the ability to detect data drifts. Which approach should you take?
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
Move the feature extraction to a separate scheduled query in BigQuery and load the results into a table that the pipeline reads from.
Option B is correct because it decouples the feature extraction from the training pipeline by using a separate scheduled BigQuery query that writes results to a table. This eliminates redundant query execution on every pipeline run, reducing cost and execution time, while the scheduled query can be set to run at a frequency that still detects data drifts (e.g., hourly). The pipeline then reads from the precomputed table, avoiding repeated full scans of the source data.
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.
- ✗
Implement a caching mechanism in the pipeline that stores the results of the BigQuery query and reuses them if the data hasn't changed.
Why it's wrong here
Pipeline caching is based on component inputs, not on data content, so it may not prevent rerun if inputs differ.
- ✓
Move the feature extraction to a separate scheduled query in BigQuery and load the results into a table that the pipeline reads from.
Why this is correct
This separates concerns and avoids redundant execution, while still allowing data drift detection via the pipeline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the pipeline frequency to once a day to minimize the number of runs.
Why it's wrong here
This reduces cost but delays model updates and data drift detection.
- ✗
Use a conditional pipeline that checks if the data has changed before running the feature extraction step.
Why it's wrong here
This adds complexity and still requires executing the pipeline to perform the check.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that caching or conditional checks are sufficient to reduce cost, when in fact the most efficient solution is to offload the repetitive computation to a separate scheduled job that writes to a table, avoiding any pipeline-level overhead.
Detailed technical explanation
How to think about this question
BigQuery scheduled queries can be configured to run at a specific interval (e.g., every hour) and append or overwrite a destination table, allowing the pipeline to read from that table without re-executing the extraction logic. This approach also enables partitioning and clustering on the destination table for efficient reads, and the scheduled query can use BigQuery's time-based partitioning to detect new data arrivals, ensuring drift detection is maintained. In practice, this pattern is common in production ML pipelines where feature engineering is a bottleneck, and it aligns with Vertex AI's recommended architecture for separating data transformation from model training.
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
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and orchestrating ML pipelines — This question tests Automating and orchestrating ML pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Move the feature extraction to a separate scheduled query in BigQuery and load the results into a table that the pipeline reads from. — Option B is correct because it decouples the feature extraction from the training pipeline by using a separate scheduled BigQuery query that writes results to a table. This eliminates redundant query execution on every pipeline run, reducing cost and execution time, while the scheduled query can be set to run at a frequency that still detects data drifts (e.g., hourly). The pipeline then reads from the precomputed table, avoiding repeated full scans of the source data.
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: Jun 30, 2026
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