hardMultiple Choice
PMLE Practice Question: A team uses Cloud Composer to orchestrate a…
A team uses Cloud Composer to orchestrate a complex ML pipeline with many tasks. They notice that the DAG parsing time is very high, causing delays in task scheduling. Which action would most effectively reduce DAG parsing time?
⚠ Common exam trap
Google Cloud often tests the misconception that reducing the number of DAG files or increasing scheduler resources will fix parsing delays, when the real bottleneck is the top-level code execution inside each DAG file.
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
✓
Optimize DAG files to avoid heavy top-level imports and database queries
Heavy top-level imports and database queries in DAG files are executed every time the scheduler parses the DAG, which happens frequently (default every 30 seconds). By moving imports inside Python callables or using lazy loading, the parsing time is drastically reduced, allowing the scheduler to process DAGs faster and trigger tasks without delay.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all DAG files that are not currently needed from the bucket
Why it's wrong here
Removing unneeded DAG files reduces the number of files parsed, but the reported delay stems from parsing the complex pipeline's own DAG, which remains. It is tempting because deleting files obviously cuts parsing load, yet the fix must target the heavy DAG's top-level imports and code.
- ✗
Increase the parallelism of the Airflow scheduler
Why it's wrong here
Scheduler parallelism controls how many task instances run concurrently, not how quickly DAG files are parsed, so parsing delays persist. It is tempting because raising parallelism speeds overall pipeline throughput, which is its real purpose, but parsing time depends on file count, imports and top-level code.
- ✓
Optimize DAG files to avoid heavy top-level imports and database queries
Why this is correct
The scheduler parses every DAG file repeatedly, so heavy top-level imports and database queries executed at parse time inflate parsing duration and delay scheduling. Moving such work inside tasks or using lazy loading cuts parsing time directly.
- ✗
Combine all DAGs into a single file
Why it's wrong here
Merging DAGs into one file concentrates all parsing work into a single file, increasing per-file parse duration rather than reducing total parsing time. It is tempting because fewer files appear tidier, but Airflow parses each file independently, so splitting DAGs and trimming top-level imports is what shortens parsing.
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