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 manufacturing company installs IoT sensors on equipment in a factory. Each sensor sends a reading (device ID, timestamp, temperature, vibration) every second. The application must store these readings with extremely low write latency, support queries for the latest reading per device, and allow range queries over the last hour for a specific device. The development team expects high throughput writes (millions per day) and does not require complex joins. Which Azure data store is most appropriate for this workload?
⚠ Common exam trap
Watch out — candidates often choose Azure Table Storage because it is a low-cost, schema-less NoSQL option, but they overlook its lack of guaranteed single-digit millisecond latency and the need for manual partition key design to avoid throttling under high-throughput IoT workloads.
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
Azure Cosmos DB is the most appropriate because it offers single-digit millisecond write and read latencies at any scale, which is critical for the high-throughput, low-latency IoT sensor ingestion described. Its support for automatic indexing and efficient point reads (by device ID and timestamp) enables fast retrieval of the latest reading per device, while its native time-to-live (TTL) and range query capabilities on the timestamp field allow efficient queries over the last hour for a specific device. Additionally, Cosmos DB's schema-agnostic, non-relational model fits the simple key-value structure of sensor readings without requiring complex joins.
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 Cosmos DB
Why this is correct
Azure Cosmos DB is a multi-model NoSQL database with single-digit-millisecond write and read latencies, automatic indexing, and tunable consistency, making it ideal for high-throughput IoT telemetry from manufacturing sensors. Its schema-agnostic JSON documents easily accommodate varying sensor payloads, and its partitioning enables efficient point reads and time-based range queries. Support for global distribution and a 99.999% SLA further justify it as the purpose-built choice for per-second equipment data ingestion.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a schemaless key-value store that partitions data by partition key and row key, but it lacks the automatic secondary indexes and rich query engine of Cosmos DB. Range queries on fields like sensor ID or timestamp outside the row key require full partition scans, and achievable latencies are higher than Cosmos DB's single-digit-millisecond guarantees. While cost-effective for cold or archival data, it is not optimized for the millions of latency-sensitive writes and frequent point reads required by real-time IoT sensor workloads.
When this WOULD be correct
A question requiring a cost-effective, schema-less store for high-volume telemetry data where queries are always by device ID (exact match) and no range queries or ordering by timestamp are needed, e.g., 'Store IoT sensor readings with simple key-value lookups by device ID, no complex queries, and low cost.'
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is designed for storing massive volumes of unstructured objects such as backups, images, and logs, not for low-latency per-record ingestion or point reads by device/time. Though an Append Blob can collect time-series telemetry, reading or filtering individual sensor readings requires downloading and scanning entire blobs, which is inefficient. Neither automatic indexing nor a query surface for per-reading retrieval exists, so it fails to meet the interactive query and quick lookup needs of the described manufacturing scenario.
When this WOULD be correct
A company needs to store historical sensor data as JSON files for batch analytics, with no requirement for real-time queries per device. The workload involves infrequent writes and reads of entire files, and cost-effective storage for large volumes of data is the priority.
- ✗
Azure SQL Database
Why it's wrong here
Incorrect. Azure SQL Database is a relational database that can handle this workload, but its write latency and cost for millions of writes per day would be higher than a purpose-built NoSQL store like Cosmos DB.
When this WOULD be correct
A question requiring complex joins, ACID transactions, or structured relational queries (e.g., 'Store customer orders with line items and enforce referential integrity') would make Azure SQL Database the correct choice.
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 DBCorrect answer▾
Why this is correct
Azure Cosmos DB is a multi-model NoSQL database with single-digit-millisecond write and read latencies, automatic indexing, and tunable consistency, making it ideal for high-throughput IoT telemetry from manufacturing sensors. Its schema-agnostic JSON documents easily accommodate varying sensor payloads, and its partitioning enables efficient point reads and time-based range queries. Support for global distribution and a 99.999% SLA further justify it as the purpose-built choice for per-second equipment data ingestion.
✗Azure Table StorageWrong answer — click to see why▾
Why this is wrong here
Azure Table Storage does not support range queries on timestamps efficiently because its partition key design typically requires equality filters on partition key; range queries across time for a specific device would be inefficient without proper partitioning, and it lacks native support for ordering by timestamp across partitions.
★ When this WOULD be the correct answer
A question requiring a cost-effective, schema-less store for high-volume telemetry data where queries are always by device ID (exact match) and no range queries or ordering by timestamp are needed, e.g., 'Store IoT sensor readings with simple key-value lookups by device ID, no complex queries, and low cost.'
Why candidates choose this
Candidates may confuse Azure Table Storage with a time-series store due to its ability to handle large volumes of structured data and its low cost, overlooking its limitations in range queries and ordering across partitions.
✗Azure Blob StorageWrong answer — click to see why▾
Why this is wrong here
Azure Blob Storage is optimized for storing large unstructured data (e.g., files, images, logs) but does not support low-latency point reads by device ID or efficient range queries over time, nor does it provide native indexing for querying individual sensor readings.
★ When this WOULD be the correct answer
A company needs to store historical sensor data as JSON files for batch analytics, with no requirement for real-time queries per device. The workload involves infrequent writes and reads of entire files, and cost-effective storage for large volumes of data is the priority.
Why candidates choose this
Candidates may associate IoT sensor data with 'big data' and assume blob storage is suitable for high-volume writes, overlooking the need for low-latency queries on individual records.
✗Azure SQL DatabaseWrong answer — click to see why▾
Why this is wrong here
Azure SQL Database is a relational database with higher write latency and overhead for schema enforcement, making it unsuitable for the extreme low-latency, high-throughput write workload of millions of IoT sensor readings per day.
★ When this WOULD be the correct answer
A question requiring complex joins, ACID transactions, or structured relational queries (e.g., 'Store customer orders with line items and enforce referential integrity') would make Azure SQL Database the correct choice.
Why candidates choose this
Candidates may assume that any structured data with timestamps and device IDs needs a relational database, overlooking the non-relational, high-throughput nature of IoT time-series workloads.
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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