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You want to optimize costs for a Cloud Function that processes large files and sends results over the network. Which combination of strategies will most effectively reduce your bill?

hard📝 Application Q15 of 15
GCP - Cloud Functions
You want to optimize costs for a Cloud Function that processes large files and sends results over the network. Which combination of strategies will most effectively reduce your bill?
AReduce function memory allocation and minimize outbound data size
BIncrease function timeout and add more retries
CDeploy multiple identical functions in different regions
DUse a higher memory allocation to speed up execution regardless of data size
Step-by-Step Solution
Solution:
  1. Step 1: Identify cost factors for large file processing

    Costs come from compute time (affected by memory and duration) and outbound data size.
  2. Step 2: Evaluate optimization strategies

    Reducing memory allocation lowers compute cost if performance is sufficient. Minimizing outbound data reduces network charges. Increasing timeout or retries raises cost. Deploying multiple functions increases cost. Higher memory may speed execution but can increase cost if not balanced.
  3. Final Answer:

    Reduce function memory allocation and minimize outbound data size -> Option A
  4. Quick Check:

    Lower memory + less data = cost savings [OK]
Quick Trick: Cut memory and data size to save costs effectively [OK]
Common Mistakes:
  • Thinking more memory always reduces cost
  • Adding retries without cost consideration
  • Deploying duplicates increases cost unnecessarily

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