DEA-C01 Data Store Management Practice Question
Which THREE factors should be considered when choosing a partition key for an Amazon DynamoDB table?
⚠ Common exam trap
Candidates often think maximizing item size (Option A) or minimizing RCU consumption (Option D) are primary factors, when in fact even distribution and access pattern alignment are the critical design principles for DynamoDB partition keys.
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
✓
If the table has a write-heavy workload, the partition key should distribute writes evenly.
Option B is correct because a write-heavy workload requires the partition key to spread write traffic across many partitions; otherwise a hot partition throttles throughput, since each partition supports a limited write capacity (up to 1,000 WCU per partition). Option C is correct because DynamoDB retrieves items by partition key, so aligning the key with the most common query access pattern enables efficient Query operations instead of expensive Scan operations. Option E is correct because high cardinality produces many distinct partition key values, which distributes items and traffic evenly across partitions and avoids hot partitions. Option A is not a valid factor because item size does not determine partition key choice; DynamoDB partitions data by key value, and large items only consume more capacity, not improve partitioning. Option D is not a valid factor because RCU consumption is driven by item size and consistency model, not by selecting a partition key to minimize 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.
- ✗
The partition key should be chosen to maximize the size of items in each partition.
Why it's wrong here
Maximising item size per partition is not a selection factor; DynamoDB partitions split on throughput and storage limits, and larger items consume more capacity without improving distribution. It is tempting because even data spread across partitions is desirable, but item size is governed by the 400 KB limit, not by partition key design.
- ✓
If the table has a write-heavy workload, the partition key should distribute writes evenly.
Why this is correct
Even write distribution across partitions prevents hot partitions, which throttle throughput when write volume is high. DynamoDB hashes the partition key to place items, so a high-cardinality key spreads writes evenly. This directly satisfies the stem's write-heavy workload constraint, sustaining provisioned capacity without request throttling.
- ✓
The partition key should align with the most common query access pattern.
Why this is correct
DynamoDB retrieves items by partition key, so aligning it with the dominant query pattern lets the application use efficient Query operations rather than full-table Scan operations, directly satisfying the stem's requirement to optimise for the most common access pattern.
- ✗
The partition key should be chosen to minimize read capacity unit consumption.
Why it's wrong here
Minimising read capacity unit consumption is not a partition key selection factor; RCU consumption depends on item size and access patterns, not key choice. It is tempting because cost reduction is a general design goal, and a well-distributed key does avoid hot partitions, but that is a throughput concern rather than an RCU-minimisation criterion.
- ✓
The partition key should have high cardinality to distribute data evenly.
Why this is correct
High cardinality spreads items across many distinct partition-key values, preventing hot partitions and throttling when throughput is unevenly distributed. DynamoDB hashes the key to place items, so a low-cardinality key concentrates traffic on few partitions. This directly satisfies the even data-distribution requirement for choosing a partition key.
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