DEA-C01 Data Store Management Practice Question
A data engineer is migrating an on-premises Apache HBase workload to Amazon DynamoDB. The HBase table has a row key with composite structure: customer_id (10 chars) + timestamp (10 digits). The access pattern is to query by customer_id and retrieve the latest entries. How should the DynamoDB table be designed to optimize performance?
⚠ Common exam trap
Watch out — candidates often think concatenating the row key into a single partition key (Option C) preserves the query pattern, but DynamoDB requires the partition key to be known exactly for queries, making it impossible to query by customer_id alone without a full scan.
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
✓
Create a table with partition key = customer_id and sort key = timestamp.
DynamoDB's partition key (customer_id) evenly distributes data across partitions, while the sort key (timestamp) enables efficient range queries using Query with ScanIndexForward=false to retrieve the latest entries. This design directly maps the HBase composite row key pattern to DynamoDB's primary key structure, optimizing for the described access pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a table with partition key = customer_id and sort key = timestamp.
Why this is correct
Allows querying by customer_id and sorting by timestamp to get latest entries.
- ✗
Use Amazon S3 with customer_id as prefix and timestamp as object name.
Why it's wrong here
S3 is not designed for low-latency queries like DynamoDB.
- ✗
Create a table with partition key = concatenated customer_id and timestamp.
Why it's wrong here
Cannot query by customer_id alone; would require full scan.
- ✗
Create a table with partition key = timestamp and sort key = customer_id.
Why it's wrong here
This would scatter data across partitions and make queries inefficient.
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