MLS-C01 Data Engineering Practice Question
A company is running a data pipeline that uses Amazon EMR with Spark to process 100 TB of data daily. The pipeline must complete within 6 hours. Currently, it takes 8 hours. Which optimization will most likely reduce the runtime?
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
✓
Enable EMR Managed Scaling
EMR Managed Scaling automatically adjusts the number of core and task nodes based on workload, increasing parallelism and reducing runtime for large data pipelines. This is the most effective optimization when the current cluster is under-provisioned for the 100 TB workload. Option A is incorrect because consolidating input files reduces small file overhead but does not address a compute capacity bottleneck. Option C is incorrect because increasing memory per node (e.g., r5 instances) helps memory-bound tasks, but the primary bottleneck here is likely throughput and parallelism, not memory. Option D is incorrect because Spot Instances can be interrupted, which may cause delays and increase runtime, making them unsuitable for a time-sensitive pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Consolidate small input files into fewer larger files
Why it's wrong here
This can improve performance but may not be enough to reduce runtime by 2 hours.
- ✓
Enable EMR Managed Scaling
Why this is correct
Managed Scaling dynamically adds resources to meet deadlines.
- ✗
Increase the memory of each node by using r5 instances
Why it's wrong here
Memory may not be the bottleneck; compute parallelism is key.
- ✗
Use Spot Instances for all core nodes
Why it's wrong here
Spot instances can be reclaimed, causing delays.
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