DEA-C01 Data Store Management Practice Question
A company uses Amazon DynamoDB to store session data for a web application. The application experiences throttling errors during peak traffic. The data engineer observes that the table's read capacity is consistently at 100% and the write capacity is at 20%. The engineer needs to resolve the throttling with minimal cost. Which solution should the engineer implement?
⚠ Common exam trap
A common mix-up: candidates assume scaling (auto scaling or increasing capacity) is the only solution for throttling, overlooking that caching with DAX can resolve read-heavy throttling at a lower cost by reducing the actual read load on the table.
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
✓
Implement DynamoDB Accelerator (DAX) to cache read-heavy workloads.
The issue is read-heavy throttling with read capacity at 100% while write capacity is low. Implementing DynamoDB Accelerator (DAX) provides an in-memory cache that offloads read traffic from the table, reducing read capacity consumption and eliminating throttling without increasing provisioned capacity. This is the most cost-effective solution as it avoids scaling costs and leverages caching for repeated reads.
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 provisioned read capacity units for the table.
Why it's wrong here
Increases cost and does not optimize efficiency.
- ✗
Enable DynamoDB auto scaling for read capacity.
Why it's wrong here
Auto scaling may not react quickly enough for sharp spikes, and still consumes capacity.
- ✓
Implement DynamoDB Accelerator (DAX) to cache read-heavy workloads.
Why this is correct
DAX reduces read load on the table by caching, lowering required read capacity.
- ✗
Decrease the provisioned write capacity units to free up budget for reads.
Why it's wrong here
Write capacity is independent; decreasing write does not help read throttling.
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Same concept, more angles
3 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Amazon DynamoDB as a session store for a web application. During peak hours, the application experiences high latency and throttling on the DynamoDB table. The table has a read capacity of 5000 RCU and write capacity of 2000 WCU. The application reads and writes session data using the session ID as the partition key. What is the most cost-effective solution to reduce throttling?
hard- A.Enable Auto Scaling on the table to automatically adjust capacity.
- B.Increase the read capacity units (RCU) and write capacity units (WCU) to 10000 each.
- C.Enable DynamoDB global tables to distribute read traffic.
- ✓ D.Implement DynamoDB Accelerator (DAX) to cache frequent reads.
Why D: DynamoDB Accelerator (DAX) provides an in-memory cache that absorbs read-heavy traffic, reducing the load on the underlying DynamoDB table. Since the application reads session data using the session ID as the partition key, DAX can serve frequent reads with microsecond latency, eliminating throttling without requiring a capacity increase. This is the most cost-effective solution because it avoids provisioning additional RCUs for reads that are repetitive and cacheable.
Variation 2. A company is using Amazon DynamoDB for a gaming application with high read and write throughput. The data engineer notices that the read latency is high during peak hours. The table has a partition key only (no sort key). The engineer wants to improve read performance by distributing reads across partitions more evenly. Which action should the engineer take?
hard- A.Increase the read capacity units of the table.
- B.Add a sort key to the table.
- ✓ C.Enable DynamoDB Accelerator (DAX).
- D.Enable DynamoDB global tables.
Why C: DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency from single-digit milliseconds to microseconds by caching frequently accessed items. Since the issue is high read latency during peak hours and the table has only a partition key, DAX offloads reads from the underlying table, distributing the read load and improving response times without changing the table's key structure.
Variation 3. A company is using Amazon DynamoDB for a high-traffic web application. They notice increased read latency during peak hours. Which design change would best reduce read latency without increasing cost?
medium- A.Increase read capacity units
- B.Use DynamoDB global tables
- C.Switch to strongly consistent reads
- ✓ D.Enable DynamoDB Accelerator (DAX)
Why D: DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency from single-digit milliseconds to microseconds for eventually consistent reads, without requiring any changes to provisioned capacity. Since the question specifies reducing latency without increasing cost, DAX is ideal because it offloads read traffic from the underlying table, allowing you to potentially lower read capacity units (RCUs) while maintaining performance.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.