DP-900 Practice Question: Identify considerations for relational data on Azure
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?
⚠ Common exam trap
Candidates often 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.
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
✓
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.
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 is not a write-throughput solution because it redirects read-only queries to a secondary replica while all INSERT, UPDATE, and DELETE operations still target the primary replica. In a high-volume order processing workload, the bottleneck is often transaction log writes and data-page latch contention on the primary, neither of which is alleviated by giving read workloads a separate replica. Therefore, it can reduce performance pressure on the primary from reporting queries, but it does nothing to reduce write contention or improve transaction commit latency.
- ✓
In-Memory OLTP
Why this is correct
In-Memory OLTP creates memory-optimized tables and can use natively compiled stored procedures, which avoid the latch and lock overhead typical of disk-based tables by using optimistic concurrency and row-versioning. For a high-volume order processing system, this directly attacks the primary bottlenecks—latch contention and blocking on hot b-tree pages—so more transactions can commit concurrently with fewer retries and lower wait times. This makes it the appropriate choice from the listed options for improving write performance under heavy concurrent transaction load.
- ✗
Elastic Database Query
Why it's wrong here
Elastic Database Query is a distributed T-SQL query tool that spans multiple sharded or federated Azure SQL databases by breaking queries into per-shard subqueries and aggregating results. It only improves the ability to run cross-database analytical queries; it does not change how transactions are processed, lock data, or commit writes inside any individual database. The original write contention on the primary would remain unchanged unless data is physically redistributed to shards, and even then Elastic Database Query is not performing or coordinating those writes.
- ✗
Transparent Data Encryption (TDE)
Why it's wrong here
Transparent Data Encryption protects data at rest by encrypting the physical database files, whether the database is active or detached, and decryption is performed automatically for authorized connections and written back during backup. It is a security feature, not a concurrency or performance feature; in fact, TDE can add CPU overhead for page-level encryption/decryption, potentially increasing response times. It has no interaction with locks, latches, or the transaction log write path that causes write contention in a high-volume order processing workload.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure SQL Database
Azure SQL Database is a fully managed relational database-as-a-service (DBaaS) in Microsoft Azure, based on the SQL Server engine, that handles scaling, backups, patching, and high availability automatically.
Key term
OLTP
OLTP (Online Transaction Processing) is a data processing system designed to manage and record high volumes of real-time transactions, such as bank deposits or online purchases, quickly and reliably.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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