The correct answer is In-Memory OLTP because it directly addresses write contention by eliminating the page latch mechanism that causes blocking in high-concurrency scenarios. When many concurrent transactions insert or update rows in the same table, traditional disk-based tables rely on page latches to coordinate access, creating a bottleneck. In-Memory OLTP replaces these latches with memory-optimized tables and optimistic multi-version concurrency control (MVCC), allowing transactions to operate directly on in-memory data structures without locking or latching, while still preserving ACID properties through conflict detection and automatic retry. On the DP-900 exam, this scenario tests your understanding of how to scale write-heavy workloads in Azure SQL Database without sacrificing transactional integrity. A common trap is confusing In-Memory OLTP with read-only caching features like the buffer pool extension or read replicas, but remember: In-Memory OLTP is specifically designed for high-concurrency writes, not just reads. Memory tip: "Latch-free writes for high-concurrency fights."
DP-900 Practice Question: Identify considerations for relational data on Azure
This DP-900 practice question tests your understanding of identify considerations for 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 operates a high-volume order processing system on Azure SQL Database. During peak hours, many concurrent transactions try to insert and update rows in the same table, causing contention on page latches. Indexing and query optimization are already tuned. Which feature should the company implement to reduce write contention while preserving ACID properties?
Why wrong: Read Scale-out creates read-only replicas to offload read queries but does not reduce write contention on the primary.
B
In-Memory OLTP
In-Memory OLTP improves write performance by eliminating latch contention through memory-optimized tables and optimistic concurrency, ideal for high-concurrency transactional workloads.
C
Elastic Database Query
Why wrong: Elastic Database Query allows querying across multiple databases but does not address contention within a single database.
D
Transparent Data Encryption (TDE)
Why wrong: TDE encrypts data at rest for security purposes and has no impact on write contention or transaction performance.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
In-Memory OLTP
In-Memory OLTP is correct because it uses memory-optimized tables and natively compiled stored procedures to reduce latch contention by eliminating the need for page latches entirely. Transactions operate directly on in-memory data structures, using optimistic multi-version concurrency control (MVCC) to detect conflicts without blocking, which preserves ACID properties while allowing high concurrency.
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.
✗
Read Scale-out
Why it's wrong here
Read Scale-out creates read-only replicas to offload read queries but does not reduce write contention on the primary.
✓
In-Memory OLTP
Why this is correct
In-Memory OLTP improves write performance by eliminating latch contention through memory-optimized tables and optimistic concurrency, ideal for high-concurrency transactional workloads.
Related concept
Read the scenario before looking for a memorised answer.
✗
Elastic Database Query
Why it's wrong here
Elastic Database Query allows querying across multiple databases but does not address contention within a single database.
✗
Transparent Data Encryption (TDE)
Why it's wrong here
TDE encrypts data at rest for security purposes and has no impact on write contention or transaction performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse In-Memory OLTP with caching or read optimization, but the question specifically targets write contention and ACID preservation, which In-Memory OLTP uniquely addresses through latch-free design and optimistic concurrency.
Detailed technical explanation
How to think about this question
In-Memory OLTP uses lock-free and latch-free data structures, such as hash indexes and range indexes, that operate entirely in memory. Under the hood, it relies on optimistic concurrency control where write conflicts are detected at commit time via validation phases, and conflicting transactions are rolled back and retried, which is ideal for high-volume insert/update patterns like order processing. A real-world scenario is a stock trading system where thousands of orders per second must be inserted without blocking, and In-Memory OLTP can achieve orders-of-magnitude throughput improvement over disk-based tables.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Identify considerations for relational data on Azure — This question tests Identify considerations for 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: In-Memory OLTP — In-Memory OLTP is correct because it uses memory-optimized tables and natively compiled stored procedures to reduce latch contention by eliminating the need for page latches entirely. Transactions operate directly on in-memory data structures, using optimistic multi-version concurrency control (MVCC) to detect conflicts without blocking, which preserves ACID properties while allowing high concurrency.
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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