You have a large dataset causing your Hadoop job to fail frequently. Which combined tuning approach best prevents slow and failed jobs?

hard📝 Application Q15 of 15

Hadoop - Performance Tuning

ADecrease memory allocation, reduce reducers to 1, and lower timeout

BIncrease memory allocation, set more reducers, and raise timeout limits

CKeep default settings and rerun the job multiple times

DDisable logging and speculative execution to save resources

Step-by-Step Solution

Solution:

Step 1: Analyze large dataset impact
Large data needs more memory, more reducers to split work, and longer time to complete.
Step 2: Identify tuning combination to handle load
Increasing memory, reducers, and timeout helps job run smoothly without failures.
Final Answer:
Increase memory allocation, set more reducers, and raise timeout limits -> Option B
Quick Check:
Large data needs more resources and time [OK]

Quick Trick: Boost memory, reducers, and timeout for big data jobs [OK]

Common Mistakes:

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