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PDE Practice Question: A company uses Cloud Composer (Airflow) to…
A company uses Cloud Composer (Airflow) to orchestrate a daily batch job that runs a custom Python script on a Compute Engine instance. The process is slow because the instance takes 2 minutes to boot. How can you reduce the total runtime?
⚠ Common exam trap
It's easy for candidates to assume 'faster provisioning' means a larger machine type (Option B) or a serverless service (Option A), but the question specifically targets the boot time caused by environment setup, which is solved by pre-installing dependencies in a custom image.
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
✓
Create a custom image with the script and dependencies pre-installed
Creating a custom image with the script and dependencies pre-installed eliminates the need to install packages or configure the environment at boot time. In Cloud Composer, when a Compute Engine instance is provisioned via a BashOperator or SSHOperator, the boot process includes OS initialization and package installation. A custom image bypasses these steps, reducing boot time from minutes to seconds, directly addressing the 2-minute boot 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.
- ✗
Switch to Dataproc Serverless to avoid VM boot time
Why it's wrong here
Dataproc Serverless still has startup time for Spark runtime.
- ✗
Use a larger machine type for faster provisioning
Why it's wrong here
Larger instances may take longer to provision.
- ✓
Create a custom image with the script and dependencies pre-installed
Why this is correct
Custom image reduces boot time by avoiding package installs.
- ✗
Use a GPU-accelerated instance to speed up the script
Why it's wrong here
GPU may accelerate computation but boot time remains.
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