DEA-C01 Data Operations and Support Practice Question
A company is experiencing high costs from Amazon Redshift. The data engineer wants to optimize costs. Which THREE actions should the engineer take? (Choose THREE.)
⚠ Common exam trap
A common mix-up: candidates confuse cost optimization with performance improvement, leading them to select 'Increase the number of nodes' (Option C) thinking it will reduce costs by improving efficiency, when in fact it increases costs.
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
✓
Right-size the cluster based on workload analysis.
Option B is correct because right-sizing the cluster based on actual workload analysis ensures you are not paying for compute and storage capacity that the workload does not consume, directly reducing Redshift costs. Option D is correct because purchasing Reserved Instances for steady-state (predictable, always-on) workloads provides a significant discount over on-demand pricing for the same node usage. Option E is correct because Concurrency Scaling adds transient capacity only when queries queue, and pairing it with a usage limit caps how much extra concurrency-scaling compute can be billed, preventing runaway costs. Option A is not correct because increasing automated snapshot frequency does not reduce cost and can actually increase storage charges for retained snapshots. Option C is not correct because adding more nodes increases both compute and storage cost, which is the opposite of cost optimization unless justified by a genuine performance need.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the frequency of automated snapshots.
Why it's wrong here
Snapshots add storage charges, so increasing their frequency raises cost rather than reducing it. Snapshot retention is a durability and recovery control; you would raise frequency when tighter recovery point objectives demand it, not when the goal is cost optimisation.
- ✓
Right-size the cluster based on workload analysis.
Why this is correct
Right-sizing matches provisioned cluster capacity to actual workload demand, eliminating spend on idle compute nodes. Redshift charges for running nodes regardless of utilisation, so analysing query concurrency, CPU and disk usage identifies over-provisioned clusters that can be resized to fewer or smaller nodes, directly reducing the cost driver described in the stem.
- ✗
Increase the number of nodes to improve performance.
Why it's wrong here
Adding nodes increases provisioned compute and storage charges, directly raising spend. Node count is scaled up to meet throughput or concurrency targets; it is the right action when performance, not cost, is the binding constraint on the cluster.
- ✓
Purchase Reserved Instances for steady-state workloads.
Why this is correct
Purchasing Reserved Instances for steady-state workloads satisfies the cost-optimisation constraint by committing to one- or three-year terms in exchange for substantially lower hourly rates than on-demand pricing. Steady-state usage is predictable, so the commitment risk is low, and this directly reduces the cluster's largest recurring expense.
- ✓
Enable Concurrency Scaling and set up a usage limit.
Why this is correct
Concurrency Scaling adds transient clusters only when queries queue, so burst capacity is billed per-second rather than provisioning permanent nodes. Pairing it with a usage limit caps that spend, directly addressing the stem's cost-optimisation constraint while preserving performance during peak concurrency.
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