DEA-C01 Data Operations and Support Practice Question
A company uses Amazon EMR to run Spark jobs on a transient cluster. The jobs process data from S3 and write results back to S3. The team wants to reduce costs by optimizing the cluster. Which action should the team take?
⚠ Common exam trap
Candidates often confuse cost optimization with performance tuning, leading them to choose larger instances (Option B) or consistency features (Option C), when the real cost lever for transient workloads is leveraging Spot pricing for ephemeral compute.
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 Spot Instances for the task nodes.
Using Spot Instances for task nodes in a transient EMR cluster significantly reduces compute costs because Spot Instances are spare AWS EC2 capacity offered at up to 90% discount compared to On-Demand. Since transient clusters are terminated after job completion, the risk of Spot Instance interruptions is mitigated—the job can simply be retried on a new cluster if needed. This directly addresses the cost optimization goal without sacrificing job functionality.
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 Spot Instances for the task nodes.
Why this is correct
Task nodes perform no HDFS storage, so their loss does not corrupt data, making them safe for Spot capacity. Spot Instances cost substantially less than On-Demand, cutting transient cluster expense while core nodes retain On-Demand reliability.
- ✗
Increase the number of core nodes and use larger instance types.
Why it's wrong here
Adding core nodes and larger instances raises hourly spend and leaves capacity idle between jobs; core nodes also hold HDFS data, prolonging termination. The correct option uses task nodes and automatic scaling so capacity matches demand. Larger core nodes suit sustained, storage-heavy processing, not a transient cost-reduction goal.
- ✗
Enable EMRFS consistent view.
Why it's wrong here
EMRFS consistent view adds a DynamoDB-backed metadata layer that tracks S3 object listings, incurring extra cost and latency; it addresses consistency for read-after-write scenarios, not cluster spend. The correct option right-sizes and auto-terminates capacity. Consistent view suits workloads needing immediate listing consistency.
- ✗
Terminate the cluster after each job and manually restart it for the next job.
Why it's wrong here
Manually terminating and restarting adds no saving beyond the transient cluster's existing auto-termination, and idle provisioning between jobs still bills. The correct option configures automatic scaling and termination behaviour so capacity tracks workload. Manual restarts suit debugging a single job, not routine cost control.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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