DOP-C02 Resilient Cloud Solutions Practice Question
A company's application uses Amazon DynamoDB as its primary data store. The application experiences occasional throttling errors during traffic spikes. The DevOps team needs to implement a solution that ensures consistent performance without manual intervention. Which TWO actions should the team take? (Choose TWO.)
⚠ Common exam trap
Test-takers frequently think On-Demand capacity mode (Option E) is the only way to handle spikes without manual intervention, but it ignores the cost implications and the fact that DAX plus Auto Scaling provides a more balanced and cost-effective solution for read-heavy workloads.
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 requests.
To handle occasional throttling during traffic spikes without manual intervention, the team should use DynamoDB Accelerator (DAX) to cache read requests, reducing read load on the table, and enable DynamoDB Auto Scaling to automatically adjust read and write capacity based on traffic patterns. DAX absorbs spikey read traffic, while Auto Scaling ensures sufficient capacity for writes and uncached reads, together providing consistent performance without manual scaling. Option E (On-Demand) also handles spikes automatically but can be costlier for predictable workloads; the combination of DAX and Auto Scaling is often more cost-effective for read-heavy applications.
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 eventually consistent reads for all queries.
Why it's wrong here
Eventually consistent reads reduce read capacity unit consumption by half because DynamoDB doesn't need to wait for replication to all AZs, but throttling occurs when your consumed capacity exceeds the provisioned table's capacity. Throttling is a function of the capacity limit, not the consistency mode; changing consistency simply changes how many RCUs each request costs, not the overall throughput ceiling. If the workload is already exceeding provisioned read capacity, eventual consistency may help marginally but won't solve systematic throttling, and it's not a scalable or guaranteed fix.
- ✗
Move the data to Amazon RDS with read replicas.
Why it's wrong here
Migrating to Amazon RDS with read replicas abandons DynamoDB's low-latency, fully managed NoSQL access patterns and operational simplicity. Read replicas address read scaling in relational databases, but they require schema redesign, connection management, and eventual consistency caveats, plus you'd need to handle write scaling separately. This is a disproportionate architectural overhaul when DynamoDB has built-in native mechanisms—like DAX or Auto Scaling—that solve the throttling problem without changing the data store or application data model.
- ✓
Implement DynamoDB Accelerator (DAX) to cache read requests.
Why this is correct
DynamoDB Accelerator (DAX) is an in-memory cache that sits in front of a DynamoDB table, serving read-heavy workloads with microsecond latency while absorbing a large fraction of read requests. By caching frequently accessed items (including strongly consistent reads when DAX is enabled), DAX reduces the number of read requests that actually reach DynamoDB, thereby reducing the table's consumed read capacity and preventing read throttling. It is a native, fully managed solution specifically designed for this scenario, preserving the DynamoDB API and requiring no application rewrite beyond adding a DAX client endpoint.
- ✓
Enable DynamoDB Auto Scaling for read and write capacity.
Why this is correct
Auto Scaling uses Amazon CloudWatch alarms to adjust the table's provisioned read and write capacity based on actual utilization, targeting a configurable utilization percentage. When traffic spikes, the scaling policy increases capacity before throttling occurs, and when demand falls, it decreases capacity to save cost. This is an operational lever that continuously reconciles capacity with demand, directly preventing throttling from capacity shortfalls, though it requires careful configuration of minimum/maximum capacities and scaling policies to respond quickly enough to sharp, unpredicted bursts.
- ✗
Switch DynamoDB to On-Demand capacity mode.
Why it's wrong here
On-Demand capacity mode eliminates the need to provision capacity; DynamoDB instantly accommodates workload spikes and you pay per request, so it does avoid throttling. However, On-Demand pricing typically costs more than well-rightsized provisioned capacity—particularly for steady or predictable workloads—because you're paying a premium for the flexibility of unlimited capacity. While it's a valid mitigation, the question likely expects a more cost-effective solution that doesn't unnecessarily inflate your bill, especially when the read-heavy pattern can be addressed with DAX or Auto Scaling.
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Senior Network & Security Engineer · founder of Courseiva
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