Question 865 of 982

Describe considerations for working with non-relational data on Azure →mediumMultiple ChoiceObjective-mapped

Quick Answer

Azure Cosmos DB is the correct choice because it delivers single-digit millisecond write latency and supports high-throughput ingestion, making it ideal for the millions of IoT sensor readings per second described. Its native support for point reads using device ID and timestamp enables instant retrieval of the latest reading per device, while range queries over the timestamp field efficiently filter the last hour of data for a specific device. On the DP-900 exam, this scenario tests your understanding of when to choose a NoSQL store over a relational one—a common trap is selecting Azure SQL Database or Azure SQL Edge, which add unnecessary complexity and cannot match Cosmos DB’s write throughput at scale. The key concept here is that IoT time-series data is schema-agnostic and key-value in nature, so a globally distributed, schema-free database like Cosmos DB is the natural fit. Memory tip: think “IoT = high write, low latency, no joins” and remember that Cosmos DB’s TTL feature automatically expires old data, keeping your storage lean without manual cleanup.

DP-900 Practice Question: Describe considerations for working with non-relational data on Azure

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 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?

Question 1mediummultiple choice

Full question →

A
Azure Cosmos DB
Correct. Azure Cosmos DB provides low-latency writes, flexible schema, and supports point reads and range queries with automatic indexing. It is designed for high-throughput, globally distributed workloads like IoT time-series data.
B
Azure Table Storage
Why wrong: Incorrect. While Azure Table Storage is a NoSQL key-value store that is cost-effective, it has higher latency and does not support complex range queries on non-partition key fields as efficiently as Cosmos DB.
C
Azure Blob Storage
Why wrong: Incorrect. Azure Blob Storage is optimized for storing large unstructured objects such as files, images, and documents. It is not designed for per-second time-series data with frequent point reads.
D
Azure SQL Database
Why wrong: 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.

Full breakdown with real-world context →

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.

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.

✓
Azure Cosmos DB
Why this is correct
Correct. Azure Cosmos DB provides low-latency writes, flexible schema, and supports point reads and range queries with automatic indexing. It is designed for high-throughput, globally distributed workloads like IoT time-series data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Table Storage
Why it's wrong here
Incorrect. While Azure Table Storage is a NoSQL key-value store that is cost-effective, it has higher latency and does not support complex range queries on non-partition key fields as efficiently as Cosmos DB.
✗
Azure Blob Storage
Why it's wrong here
Incorrect. Azure Blob Storage is optimized for storing large unstructured objects such as files, images, and documents. It is not designed for per-second time-series data with frequent point reads.
✗
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.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that 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.

Detailed technical explanation

How to think about this question

Azure Cosmos DB achieves its low latency through a combination of automatic indexing on all fields, a multi-master replication model, and a wire-protocol-compatible API (e.g., SQL API) that allows direct point reads via partition key and sort key. The 'latest reading per device' query can be implemented using a composite index on (deviceId, timestamp DESC) with a TOP 1 clause, while the last-hour range query leverages Cosmos DB's TTL feature to automatically expire older data, reducing storage costs and query overhead. In a real-world scenario, a factory with 10,000 sensors sending one reading per second would generate 864 million writes per day, which Cosmos DB can handle by distributing writes across multiple physical partitions based on device ID.

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

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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-900 question test?

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..

What is the correct answer to this question?

The correct answer is: 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.

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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