DBS-C01 Workload-Specific Database Design Practice Question
Which THREE factors should be considered when selecting a database for a time-series workload (e.g., IoT sensor data) that requires high write throughput and efficient data retention?
⚠ Common exam trap
Watch out — candidates often confuse general database best practices (like normalization or connection pooling) with the specialized optimizations required for time-series workloads, overlooking that TTL and time-based partitioning are the key architectural patterns for write-heavy, retention-focused IoT data.
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
✓
Configure automatic data expiration using TTL (Time-to-Live).
TTL (Time-to-Live) is a critical feature for time-series workloads, allowing automatic deletion of data that has exceeded a specified retention period. This reduces storage costs and manual maintenance overhead, which is essential for high-volume IoT sensor data where old data loses value over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize the schema to reduce data duplication.
Why it's wrong here
Normalization can hurt write performance.
- ✗
Use Amazon RDS Proxy to manage database connections.
Why it's wrong here
RDS Proxy is for connection pooling, not time-series optimization.
- ✓
Configure automatic data expiration using TTL (Time-to-Live).
Why this is correct
TTL automates data retention.
- ✓
Partition the table by time intervals (e.g., hourly or daily).
Why this is correct
Partitioning improves query performance and data management.
- ✓
Use Amazon Timestream for its built-in time-series optimizations.
Why this is correct
Timestream is designed for time-series.
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Variation 1. Which TWO factors should be considered when designing a database for an IoT workload that ingests millions of sensor readings per second? (Choose 2.)
medium- A.Ensure strong consistency for all reads
- B.Enforce ACID transactions for all writes
- ✓ C.Implement data retention and aggregation to reduce storage costs
- ✓ D.Use a time-series database for efficient storage and querying
- E.Use a graph database to model relationships between sensors
Why C: IoT workloads generate massive volumes of data, and implementing data retention policies (e.g., automatically deleting raw data after a set period) combined with aggregation (e.g., downsampling sensor readings into hourly or daily averages) directly reduces storage costs. This is a core design pattern for time-series databases like Amazon Timestream, which supports automatic retention and aggregation via scheduled queries or rollups.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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