DEA-C01 Data Operations and Support Practice Question
A company is using Amazon DynamoDB as a data store for a real-time application. The application reads a single item by primary key and occasionally updates it. The data engineer notices high read latency during peak hours. Which TWO actions would most effectively reduce read latency?
⚠ Common exam trap
Many candidates confuse global tables or secondary indexes as solutions for read latency, when in fact they address different concerns (disaster recovery and query flexibility), while the correct approach is to either increase provisioned throughput or implement a caching layer like DAX.
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
✓
Increase the read capacity units for the table.
Option A is correct because the workload is read-heavy with single-item reads by primary key, and if the table is provisioned with insufficient read capacity units (RCUs), requests will throttle or queue during peak hours, so raising the provisioned RCUs directly increases available read throughput and reduces latency. Option E is correct because DynamoDB Accelerator (DAX) is an in-memory cache purpose-built for DynamoDB that serves eventually consistent reads of individual items in microseconds, which is ideal for this read-by-primary-key pattern and offloads repeated reads from the table. Option B is not appropriate because global tables provide multi-Region active-active replication for availability and locality, not lower latency for a single-Region read pattern, and they add replication overhead. Option C is not appropriate because a local secondary index only provides an alternative sort key on the same partition key and does not speed up reads that already use the primary key. Option D is wrong because disabling auto-scaling and fixing read capacity removes the ability to scale with peak demand and would likely worsen throttling and latency.
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 read capacity units for the table.
Why this is correct
Increasing read capacity units raises the table's provisioned throughput ceiling, so reads are not throttled when consumed capacity approaches the limit during peak hours. For a workload dominated by single-item GetItem calls, this directly addresses the constraint causing latency: insufficient provisioned read throughput for the peak request rate.
- ✗
Enable DynamoDB global tables.
Why it's wrong here
Global tables replicate writes across Regions for locally low-latency access and disaster recovery, not for accelerating single-item reads within one Region. Here reads already target a primary key, so replication adds cost and write overhead without addressing hot-partition or capacity-driven latency.
- ✗
Add a local secondary index on the table.
Why it's wrong here
A local secondary index provides an alternative sort key on the same partition, supporting queries by non-key attributes. The application reads by primary key, so the base table already serves those reads; the index adds storage and write cost while leaving read latency unchanged.
- ✗
Disable auto-scaling and set a fixed read capacity.
Why it's wrong here
Fixing read capacity removes DynamoDB's ability to scale throughput with demand, so peak-hour reads throttle or queue and latency worsens. Auto-scaling exists precisely to raise provisioned read capacity during traffic spikes; a static value only suits steady, predictable workloads.
- ✓
Enable DynamoDB Accelerator (DAX) for the table.
Why this is correct
DAX is a write-through in-memory cache sitting in front of DynamoDB. It serves repeated primary-key GetItem reads from memory in microseconds, bypassing table read throughput entirely and cutting peak-hour read latency for this read-heavy access pattern.
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