The 3 Array Patterns Every AWS Cloud Practitioner Needs to Know
Most people study for the AWS Cloud Practitioner exam by memorizing service names. That works until the exam throws a scenario question at you — and suddenly you need to actually think.
Here are 3 array patterns that show up constantly in cloud engineering and certification prep:
1. Two Pointer Technique
When to use it: Any problem involving sorted data or finding pairs.
Cloud example: You have a sorted list of EC2 instance costs and a budget. Find two instances whose combined cost exactly hits your budget.
def find_budget_pair(costs, budget):
left, right = 0, len(costs) - 1
while left < right:
total = costs[left] + costs[right]
if total == budget:
return (costs[left], costs[right])
elif total < budget:
left += 1
else:
right -= 1
return NoneTime complexity: O(n) instead of O(n²). That's the difference between passing and failing at scale.
2. Sliding Window
When to use it: Finding a contiguous subarray that meets some condition (max throughput, minimum latency window, etc.)
Cloud example: Given API request timestamps, find the 60-second window with the highest throughput.
The key insight: instead of recalculating the entire window each time, slide it — remove the leftmost element, add the new rightmost element. One pass. O(n).
3. Prefix Sum
When to use it: When you need to quickly compute the sum of any subarray.
Cloud example: Given hourly AWS costs for a month, instantly answer "What did I spend between day 5 and day 18?" without re-summing each time.
def build_prefix_sum(costs):
prefix = [0] * (len(costs) + 1)
for i in range(len(costs)):
prefix[i + 1] = prefix[i] + costs[i]
return prefix
# Query any range in O(1):
# spend(day_5, day_18) = prefix[18] - prefix[4]---
These aren't just leetcode tricks. They're how cloud engineers think about real problems — optimizing resource allocation, monitoring throughput, and analyzing cost data.
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