- A
Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp.
This design groups all data for a given day in one partition, so a time-range query scans exactly one partition, drastically reducing transactions and cost.
- B
Change RowKey to be a composite of TruckID and Timestamp while keeping PartitionKey as TruckID.
Why wrong: This does not change the partition structure; queries across all trucks still need to scan every partition, providing no performance gain.
- C
Use Azure Cosmos DB with a partition key on Timestamp instead of Azure Table Storage.
Why wrong: While Cosmos DB could be used, it is a more expensive and complex solution. The simpler optimization within Azure Table Storage is to adjust the partition key design.
- D
Enable indexing on the Timestamp column in Azure Table Storage.
Why wrong: Azure Table Storage does not support secondary indexes on custom properties; only the RowKey index (within a partition) is available. This option is not feasible.
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
This DP-900 practice question tests your understanding of describe considerations for working with non-relational data on azure. 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 logistics company stores sensor data from delivery trucks in Azure Table Storage. Each sensor reading includes a TruckID, Timestamp, Location, and EngineTemperature. The most common query retrieves all readings for all trucks within a specific one-hour time window (e.g., between 10:00 and 11:00 on a given day). Currently, the table uses PartitionKey = TruckID and RowKey = Timestamp (ISO format). However, queries filtering by time range are slow and consume many transactions. Which design change will most improve the performance of these time-range queries?
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
Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp.
Option A is correct because Azure Table Storage queries are most efficient when they target a specific PartitionKey and a range of RowKey values. By setting PartitionKey to a date-based value (e.g., YYYY-MM-DD), all readings for a given day are co-located in the same partition. Then, using a composite RowKey of TruckID and Timestamp allows the query to filter by time range within that partition using a single partition scan, drastically reducing the number of transactions and improving performance.
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.
- ✓
Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp.
Why this is correct
This design groups all data for a given day in one partition, so a time-range query scans exactly one partition, drastically reducing transactions and cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change RowKey to be a composite of TruckID and Timestamp while keeping PartitionKey as TruckID.
Why it's wrong here
This does not change the partition structure; queries across all trucks still need to scan every partition, providing no performance gain.
- ✗
Use Azure Cosmos DB with a partition key on Timestamp instead of Azure Table Storage.
Why it's wrong here
While Cosmos DB could be used, it is a more expensive and complex solution. The simpler optimization within Azure Table Storage is to adjust the partition key design.
- ✗
Enable indexing on the Timestamp column in Azure Table Storage.
Why it's wrong here
Azure Table Storage does not support secondary indexes on custom properties; only the RowKey index (within a partition) is available. This option is not feasible.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume indexing on a column (like Timestamp) will speed up queries in Azure Table Storage, but Azure Table Storage does not support secondary indexes—only the PartitionKey and RowKey are indexed, so the only way to optimize time-range queries is to redesign the key schema to include the time dimension in the PartitionKey or RowKey.
Detailed technical explanation
How to think about this question
Azure Table Storage uses a hash-based partition scheme where PartitionKey determines the partition server. By using a date-based PartitionKey, all data for a single day resides on the same partition server, allowing point queries (PartitionKey + RowKey range) to be served efficiently. The composite RowKey (e.g., TruckID_Timestamp) ensures uniqueness and enables range scans on Timestamp within that partition. In contrast, using TruckID as PartitionKey distributes data across many partitions, forcing a cross-partition query that incurs a minimum of one transaction per partition scanned, leading to high latency and cost.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe considerations for working with non-relational data on Azure — This question tests Describe considerations for working with non-relational data on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp. — Option A is correct because Azure Table Storage queries are most efficient when they target a specific PartitionKey and a range of RowKey values. By setting PartitionKey to a date-based value (e.g., YYYY-MM-DD), all readings for a given day are co-located in the same partition. Then, using a composite RowKey of TruckID and Timestamp allows the query to filter by time range within that partition using a single partition scan, drastically reducing the number of transactions and improving performance.
What should I do if I get this DP-900 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.
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Last reviewed: Jun 11, 2026
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