Describe considerations for working with non-relational data on Azure →mediumMultiple ChoiceObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
A healthcare application stores patient medical records as JSON documents. Each document contains a variable set of fields depending on the patient's conditions. The application needs to query records by any field and support high write throughput. Which Azure data store is most appropriate?
⚠ Common exam trap
Many exam-takers confuse Azure Table Storage's key-value capabilities with JSON document support, but Table Storage does not allow querying on arbitrary fields within a JSON document—it only supports queries on the partition key and row key, making it unsuitable for variable-schema medical records.
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
✓
Azure Cosmos DB with SQL API
Azure Cosmos DB with SQL API is the most appropriate choice because it natively supports storing and querying JSON documents with variable schemas, enabling efficient queries on any field. Its multi-model architecture and configurable indexing policies allow high write throughput while maintaining low-latency queries, which is critical for healthcare applications with dynamic patient records.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is an object store for unstructured binary data such as images, backups, or archived files, not for interactive queries over JSON documents. While you can upload JSON files as blobs, Blob Storage does not index their content or provide a query engine to filter on arbitrary fields; you would need to download and process each blob client-side, with no support for transactional updates or high-throughput point writes. This makes patient records inefficient and costly to search, update, or enforce schema validation at scale.
When this WOULD be correct
An application needs to store and serve large media files (e.g., patient X-ray images or PDF reports) with high durability and scalability, and does not require querying by document fields.
- ✗
Azure Synapse Analytics
Why it's wrong here
Azure Synapse Analytics is a cloud-scale data warehousing service built on a Massively Parallel Processing (MPP) architecture that is optimized for analytics—large aggregations, complex joins, and columnar scans over vast datasets—not for online transaction processing (OLTP) with point reads and writes per second. Its columnar storage and query engine minimize I/O for analytical workloads but make single-document JSON operations inefficient due to overhead and stitched-together modeling. Synapse belongs in the data analytics tier (for example, aggregating population health trends) rather than serving as the operational store for individual patient records.
When this WOULD be correct
A question asks: 'A company needs to run complex analytical queries across petabytes of structured sales data, integrating with Power BI for reporting. Which Azure service should they use?'
- ✓
Azure Cosmos DB with SQL API
Why this is correct
Azure Cosmos DB with the SQL API is the correct choice because it is a schema-agnostic document database that natively stores JSON, automatically indexes every property for efficient point reads and SQL-style queries, and scales horizontally with guaranteed single-digit-millisecond latency and throughput managed in request units. Unlike relational databases, it does not require a fixed schema and is designed for high write and read throughput on document workloads. The SQL API also supports rich queries over nested JSON fields, making it ideal for patient records with varying structures that require fast, interactive access.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a key-value NoSQL store that requires a partition key and row key for every entity, and it stores entities as simple sets of typed properties rather than deeply nested JSON documents. Queries are limited to the primary key, plus a small set of indexed properties, so you cannot efficiently query on arbitrary nested fields inside patient records. It also lacks built-in document APIs, schema-agnostic indexing, and the sophisticated query capabilities (such as JSON path queries) that Cosmos DB provides for healthcare applications.
When this WOULD be correct
An application needs to store large volumes of structured, non-relational data (e.g., device telemetry) with simple key-based lookups and does not require complex queries or indexing on multiple fields. The data has a fixed schema and high throughput for point reads/writes is needed.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure Cosmos DB with SQL APICorrect answer▾
Why this is correct
Azure Cosmos DB with the SQL API is the correct choice because it is a schema-agnostic document database that natively stores JSON, automatically indexes every property for efficient point reads and SQL-style queries, and scales horizontally with guaranteed single-digit-millisecond latency and throughput managed in request units. Unlike relational databases, it does not require a fixed schema and is designed for high write and read throughput on document workloads. The SQL API also supports rich queries over nested JSON fields, making it ideal for patient records with varying structures that require fast, interactive access.
✗Azure Blob StorageWrong answer — click to see why▾
Why this is wrong here
Azure Blob Storage is optimized for storing large unstructured binary data (like images or backups), not for querying individual fields within JSON documents with high write throughput and flexible schema.
★ When this WOULD be the correct answer
An application needs to store and serve large media files (e.g., patient X-ray images or PDF reports) with high durability and scalability, and does not require querying by document fields.
Why candidates choose this
Candidates may think Blob Storage can handle JSON because it supports storing JSON files, but they overlook the need for querying by any field and high write throughput, which Blob Storage does not natively support.
✗Azure Synapse AnalyticsWrong answer — click to see why▾
Why this is wrong here
Azure Synapse Analytics is a data warehouse and analytics service designed for large-scale, structured data processing and complex queries, not for high-throughput, low-latency operations on semi-structured JSON documents with variable fields.
★ When this WOULD be the correct answer
A question asks: 'A company needs to run complex analytical queries across petabytes of structured sales data, integrating with Power BI for reporting. Which Azure service should they use?'
Why candidates choose this
Candidates may confuse Synapse's analytics capabilities with the need for querying JSON data, or assume that any 'analytics' service can handle document queries efficiently.
✗Azure Table StorageWrong answer — click to see why▾
Why this is wrong here
Azure Table Storage is a NoSQL key-value store that does not support querying by arbitrary fields or indexing on multiple properties, making it unsuitable for querying JSON documents by any field. It also lacks native JSON support and flexible schema capabilities required for variable-field documents.
★ When this WOULD be the correct answer
An application needs to store large volumes of structured, non-relational data (e.g., device telemetry) with simple key-based lookups and does not require complex queries or indexing on multiple fields. The data has a fixed schema and high throughput for point reads/writes is needed.
Why candidates choose this
Candidates may confuse Azure Table Storage as a suitable NoSQL option for JSON documents because it is schema-less and supports high throughput, but they overlook its limited query capabilities and lack of native JSON support.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Cosmos DB
Azure Cosmos DB is a fully managed, globally distributed NoSQL database service that offers fast reads and writes anywhere in the world with automatic scaling and multiple consistency models.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
About these practice questions
One of 820 original DP-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.