Quick Answer
The answer is to use a partition key that distributes load evenly, such as UserID. This design reduces read latency because Azure Table Storage physically partitions data by partition key value, and a key like UserID spreads requests across many partition servers, preventing any single partition from becoming a hot spot during peak traffic. On the DP-900 exam, this concept tests your understanding of how partition key selection directly impacts performance and scalability in Azure Table Storage, often appearing in scenario-based questions about session state or telemetry data. A common trap is choosing a key like Timestamp or SessionID that creates sequential writes to one partition, causing throttling. Remember the memory tip: “Spread the load, not the data”—a good partition key should have high cardinality and even distribution, like UserID, to keep latency low under load.
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 company uses Azure Table storage to store session state for a web application. They notice that read latency increases during peak hours. Which design change should they implement to reduce latency?
Change to Azure Blob storage
Why wrong: Blob storage is not optimized for low-latency session state access.
Store large attributes in a separate table
Why wrong: This complicates schema and may not reduce latency.
Switch to Azure Queue storage
Why wrong: Queue storage is for messaging, not session state.
Use a partition key that distributes load evenly, such as UserID
Even distribution avoids hot partitions and reduces latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a partition key that distributes load evenly, such as UserID
Option D is correct because Azure Table storage partitions data based on the partition key. Using a partition key that distributes load evenly, such as UserID, ensures that read requests are spread across multiple partition servers, preventing hot partitions and reducing latency during peak hours.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Change to Azure Blob storage
Why it's wrong here
Blob storage is not optimized for low-latency session state access.
Store large attributes in a separate table
Why it's wrong here
This complicates schema and may not reduce latency.
Switch to Azure Queue storage
Why it's wrong here
Queue storage is for messaging, not session state.
Use a partition key that distributes load evenly, such as UserID
Why this is correct
Even distribution avoids hot partitions and reduces latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may confuse Azure Table storage with other Azure storage services (Blob, Queue) or focus on data size optimization (Option B) instead of understanding how partition key design directly impacts read performance in a partitioned NoSQL store.
Detailed technical explanation
Azure Table storage uses a partition key to distribute data across multiple storage nodes. When a partition key is poorly chosen (e.g., a timestamp that creates a hot partition), all reads target the same node, causing throttling and increased latency. Using a high-cardinality key like UserID ensures even distribution, leveraging Azure's automatic load balancing to maintain consistent sub-10ms read latencies under load.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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..
The correct answer is: Use a partition key that distributes load evenly, such as UserID — Option D is correct because Azure Table storage partitions data based on the partition key. Using a partition key that distributes load evenly, such as UserID, ensures that read requests are spread across multiple partition servers, preventing hot partitions and reducing latency during peak hours.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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