DVA-C02 Development with AWS Services Practice Question
A developer is building an application that uses Amazon DynamoDB as a data store. The application reads the same item frequently but writes rarely. The developer wants to reduce read costs. Which DynamoDB feature should the developer use?
⚠ Common exam trap
It's easy for candidates to confuse DAX with ElastiCache or assume that Auto Scaling reduces costs, but DAX is the only DynamoDB-native service that directly reduces read costs by caching frequently accessed items.
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
✓
DynamoDB Accelerator (DAX)
DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency from single-digit milliseconds to microseconds. Since the application reads the same item frequently but writes rarely, DAX can serve repeated read requests from its cache, significantly reducing the number of read capacity units consumed against the DynamoDB table and thus lowering read costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
DynamoDB Accelerator (DAX)
Why this is correct
DynamoDB Accelerator (DAX) is an in-memory cache designed to provide microsecond response times for read-heavy workloads, significantly reducing the number of read capacity units (RCUs) consumed from the underlying DynamoDB table. When an application reads data through DAX, if the item is in the cache, it's served directly, bypassing DynamoDB and incurring no RCU cost. This makes DAX highly effective for applications requiring low-latency access to frequently read data, directly lowering operational costs associated with read throughput.
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DynamoDB Global Tables
Why it's wrong here
DynamoDB Global Tables provide a fully managed, multi-region, active-active replication solution for DynamoDB tables, enabling applications to access data with low latency from users in different geographic regions. While they enhance disaster recovery and global data availability, Global Tables do not inherently reduce the read capacity unit consumption or cost for read operations within a single AWS region. Their primary purpose is data redundancy and localized access across multiple regions, not optimizing single-region read costs.
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DynamoDB Auto Scaling
Why it's wrong here
DynamoDB Auto Scaling automatically adjusts the provisioned read and write capacity of a DynamoDB table in response to actual traffic patterns, preventing throttling and optimizing costs by matching capacity to demand. However, Auto Scaling does not reduce the fundamental cost per read capacity unit or decrease the number of RCUs consumed by an individual read operation. It optimizes the *provisioning* of capacity to avoid over-provisioning or under-provisioning, rather than making each read operation cheaper.
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Time to Live (TTL)
Why it's wrong here
Time to Live (TTL) is a mechanism in DynamoDB that allows developers to define an expiration timestamp for items, after which DynamoDB automatically deletes them from the table without consuming any write throughput. This feature is primarily used for data lifecycle management and reducing storage costs by removing old or irrelevant data. TTL does not, however, have any direct impact on reducing the number of read capacity units consumed by an application or the cost associated with read operations.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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