DEA-C01 Data Store Management Practice Question
A data engineer manages an Amazon DynamoDB table for order events. Reads and writes are evenly spread across a partition key with very high cardinality, but during flash sales the table throttles with ProvisionedThroughputExceededException even though consumed capacity is below the provisioned total. Which cause is MOST likely?
⚠ Common exam trap
The trap here is reading ProvisionedThroughputExceededException as proof that the table needs more capacity, when spare table capacity plus throttling actually indicates a hot partition.
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
✓
A single partition key value is receiving a disproportionate share of traffic, creating a hot partition that exceeds its per-partition throughput limit.
DynamoDB throughput is provisioned at the table level but enforced at the partition level, and each partition can serve only about 1,000 write units per second. When traffic concentrates on one partition key value, that partition throttles while the table still shows unused capacity. Even distribution across a high-cardinality key normally prevents this, so the flash-sale burst must be funneling requests to a single key value.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The table's on-demand capacity mode must be enabled to remove per-partition limits.
Why it's wrong here
Switching to on-demand mode removes the need to provision capacity and absorbs traffic growth, but it does not eliminate per-partition throughput ceilings. A sustained hot partition key can still throttle under on-demand mode. Since the evidence points to concentrated access on one key value, changing the capacity mode would not fix the underlying access-pattern problem.
- ✗
The table's items are too large, and each write consumes more than one write capacity unit.
Why it's wrong here
Item size affects how many write capacity units a single request consumes, but a uniformly larger item would raise consumed capacity above what is provisioned and be visible in the metrics. The stem states consumed capacity is below the provisioned total, which rules this out. Throttling with spare table capacity is the signature of a hot partition, not oversized items.
- ✗
The table has too many global secondary indexes, which each consume write capacity.
Why it's wrong here
Global secondary indexes do consume their own write capacity and can throttle if undersized, but the stem states that consumed capacity is below the provisioned total and the key has high cardinality. The described symptom points to a single hot partition rather than index overhead. GSI capacity is provisioned separately, so it would not explain throttling while the main table shows unused capacity.
- ✓
A single partition key value is receiving a disproportionate share of traffic, creating a hot partition that exceeds its per-partition throughput limit.
Why this is correct
DynamoDB divides a table into partitions, and each partition has a hard ceiling of roughly 3,000 read units and 1,000 write units per second regardless of the table's total provisioned capacity. If one key value, such as a default or shared customer ID, receives a burst of traffic, that partition saturates and throttles even though aggregate consumed capacity sits below the table's provisioned total.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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