The answer is to partition by date first, then by device type. This strategy minimizes query cost because Azure Data Lake Storage Gen2 uses partition elimination to skip entire directories, so filtering by the last 30 days first drastically reduces the data scanned before applying the device-type filter. On the DP-203 exam, this tests your understanding of partition pruning for common query patterns—the trap is choosing device type first, which would force scanning all date directories for every device query. Remember that the most selective predicate (date range) should always be the top-level folder to maximize efficiency. A useful memory tip: think of a filing cabinet—you open the year drawer first, then the month folder, then look for the sensor type tab.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is designing a data lake in Azure Data Lake Storage Gen2 (ADLS Gen2) to store IoT sensor data from millions of devices. The data is ingested in Parquet format, partitioned by date and device ID. The analytics team frequently queries the last 30 days of data for specific device types. Which partition strategy minimizes query cost and optimizes performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Partition by date (yyyy/MM/dd) first, then by device type (e.g., sensor_type=temp).
Option C is correct because partitioning by date first enables efficient partition pruning for the common query pattern (last 30 days), and then by device type further filters the data within those date partitions. In ADLS Gen2, queries using partition elimination skip entire directories, reducing the amount of data scanned and minimizing query cost. This strategy aligns with the typical query workload, where date-range filtering is the most selective predicate.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Partition by device ID first, then by date.
Why it's wrong here
Queries for the last 30 days would need to scan all device partitions, then filter dates.
✗
Partition by device ID only, with a separate directory for each device.
Why it's wrong here
Queries filtering by date would scan all device partitions, increasing cost.
✓
Partition by date (yyyy/MM/dd) first, then by device type (e.g., sensor_type=temp).
Why this is correct
This allows date pruning first, then efficient filtering by device type within each day.
Related concept
Read the scenario before looking for a memorised answer.
✗
Partition by device type only, with a directory for each type.
Why it's wrong here
Queries filtering by date would scan all type partitions for recent data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume partitioning by the most granular attribute (device ID) first will provide the best performance, but they overlook that query patterns typically filter by time range, making date the most effective first-level partition for cost and performance optimization.
Detailed technical explanation
How to think about this question
Under the hood, ADLS Gen2 uses a hierarchical namespace that allows query engines like Azure Synapse or Spark to perform partition elimination by listing only the relevant directories. When partitioning by date first, the engine can quickly skip all directories outside the target date range, and then within the remaining directories, it can further filter by device type using metadata or file-level statistics. In real-world scenarios, this strategy also simplifies data lifecycle management, such as archiving or deleting old partitions, without affecting active data.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition by date (yyyy/MM/dd) first, then by device type (e.g., sensor_type=temp). — Option C is correct because partitioning by date first enables efficient partition pruning for the common query pattern (last 30 days), and then by device type further filters the data within those date partitions. In ADLS Gen2, queries using partition elimination skip entire directories, reducing the amount of data scanned and minimizing query cost. This strategy aligns with the typical query workload, where date-range filtering is the most selective predicate.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Variation 1. 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?
medium
A.Use Azure SQL Database with clustered columnstore index on date and device ID.
✓ B.Organize folders as /YYYY/MM/DD/DeviceID/ in ADLS Gen2 and use file naming that includes timestamp.
C.Use Azure Table Storage with PartitionKey set to date and RowKey set to device ID.
D.Use Azure Cosmos DB with partition key on (date, device ID) and TTL for data retention.
Why B: Option B is correct because 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.
Last reviewed: Jun 24, 2026
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