MLS-C01 Data Engineering Practice Question
A data engineering team is building a pipeline to process terabytes of log data daily using Amazon EMR with Spark. The data arrives in hourly batches and must be processed within 4 hours. The team needs to minimize cost. Which cluster configuration is MOST cost-effective?
⚠ Common exam trap
The trap here is that candidates overestimate the cost savings of EMR Serverless or long-running clusters, failing to recognize that transient spot-based clusters are the most cost-effective for fixed-window batch processing due to zero idle time and spot pricing discounts.
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
✓
Use a transient cluster with a mix of on-demand and spot instances, terminated after the job completes.
A transient cluster with a mix of on-demand and spot instances minimizes cost for batch workloads that have a defined lifecycle. Spot instances offer significant discounts (up to 90%) for fault-tolerant Spark jobs, and terminating the cluster after processing eliminates idle compute charges. This approach aligns with the 4-hour processing window and hourly batch arrival, as EMR can provision and tear down clusters quickly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a single large instance with multiple cores to avoid data shuffling.
Why it's wrong here
Single instance limits parallelism and increases runtime.
- ✓
Use a transient cluster with a mix of on-demand and spot instances, terminated after the job completes.
Why this is correct
Transient clusters reduce idle cost, spot instances lower compute cost.
- ✗
Use a long-running cluster of on-demand instances to avoid startup time.
Why it's wrong here
Long-running clusters incur cost even when idle.
- ✗
Use Amazon EMR Serverless to automatically scale.
Why it's wrong here
EMR Serverless may be less cost-effective for large, predictable workloads.
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