DP-203 Design and implement data storage Practice Question
A company is designing a data lake solution on Azure Data Lake Storage Gen2. Data will be ingested from IoT devices at high frequency (every 5 seconds). Each device sends a JSON payload of 2 KB. The data must be stored in a hierarchical namespace and partitioned by date and device ID to optimize query performance. Which partition strategy should be used?
⚠ Common exam trap
Many exam-takers confuse storage services (ADLS Gen2) with database or NoSQL solutions (SQL Database, Table Storage, Cosmos DB), failing to recognize that the question explicitly requires a data lake with a hierarchical namespace, which only ADLS Gen2 provides.
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
✓
Organize folders as /YYYY/MM/DD/DeviceID/ in ADLS Gen2 and use file naming that includes timestamp.
ADLS Gen2 with a hierarchical namespace allows folder-based partitioning by date and device ID (e.g., /YYYY/MM/DD/DeviceID/), which directly maps to the query optimization requirement. This structure enables efficient partition pruning for time-range and device-specific queries, and the high-frequency 2 KB JSON payloads are well-suited for append-friendly file naming with timestamps.
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 Azure SQL Database with clustered columnstore index on date and device ID.
Why it's wrong here
Azure SQL Database stores structured rows, not files in a hierarchical namespace, so it cannot satisfy the Data Lake Storage Gen2 requirement at all. It is tempting because clustered columnstore indexes do accelerate analytical queries over date and device ID, which would fit a relational warehouse scenario rather than a data lake.
- ✓
Organize folders as /YYYY/MM/DD/DeviceID/ in ADLS Gen2 and use file naming that includes timestamp.
Why this is correct
ADLS Gen2 hierarchical namespace supports true directory semantics, so /YYYY/MM/DD/DeviceID/ paths let partition pruning skip irrelevant folders during queries. Date-first ordering suits time-range filters, while DeviceID narrows per-device scans, and timestamped filenames preserve ingestion order within each partition.
- ✗
Use Azure Table Storage with PartitionKey set to date and RowKey set to device ID.
Why it's wrong here
Azure Table Storage is a key-value NoSQL store without a hierarchical namespace, so it cannot meet the Data Lake Storage Gen2 requirement. It is tempting because PartitionKey and RowKey do provide date and device ID partitioning for fast point lookups, which suits high-volume telemetry metadata rather than file-based lake storage.
- ✗
Use Azure Cosmos DB with partition key on (date, device ID) and TTL for data retention.
Why it's wrong here
Cosmos DB is a document database, not a hierarchical file system, so it cannot store the JSON payloads in Data Lake Storage Gen2 as required. It is tempting because a (date, device ID) partition key plus TTL handles high-frequency IoT writes and retention, which fits an operational document store rather than a data lake.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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