- A
Azure Cosmos DB with SQL API
Why wrong: While Cosmos DB provides low-latency access and global distribution, it is not optimized for time-series analytical workloads. Storing billions of sensor readings would be very expensive and query performance for complex aggregations is not as efficient as a purpose-built time-series solution.
- B
Azure SQL Database
Why wrong: Azure SQL Database is a relational database that can handle time-series data but is not designed for high-velocity ingestion (millions of records per second) and its indexing strategies may not support sub-second queries on recent data while also allowing efficient ad-hoc analytics on historical data.
- C
Azure Data Explorer
Azure Data Explorer is specifically built for time-series and log analytics. It supports high-throughput ingestion, automatic indexing, caching for hot data (sub-second queries), and retention-based tiering to cold storage for historical analysis, minimizing costs.
- D
Azure Table Storage
Why wrong: Azure Table Storage is a key-value store that can store large amounts of structured data, but it lacks the ability to run complex analytical queries (aggregations, time-window functions) efficiently. It is not suitable for this workload.
Quick Answer
The answer is Azure Data Explorer (ADX), the best-suited service for this workload because it is purpose-built for high-performance time-series analytics on IoT telemetry. ADX handles massive streams of time-stamped sensor data with automatic indexing, enabling sub-second responses for real-time dashboards on the last hour of data, while its hot/cold storage tiering minimizes costs for years of historical queries. On the DP-900 exam, this scenario tests your understanding of Azure’s specialized analytics services—many candidates mistakenly choose Azure Stream Analytics for real-time processing or Azure SQL Database for storage, but ADX uniquely combines both real-time and historical analysis with columnar storage and Kusto Query Language (KQL) optimized for time-series aggregations. A common trap is overlooking the “years of historical data” requirement, which ADX handles cost-effectively through automatic data tiering. Memory tip: think “ADX = Analytics for Data eXploration” and remember that for mixed real-time and historical IoT workloads, ADX is the single service that does it all.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload 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. A key principle to apply: azure Data Explorer is optimized for time-series and log analytics.. 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 collects sensor data from thousands of IoT devices. The data arrives as a stream of time-stamped readings with a fixed schema (DeviceID, Timestamp, Temperature, Pressure, Vibration). They need to store this data and support both real-time dashboards showing the last hour of data and complex analytical queries over years of historical data. The solution must minimize storage costs and provide sub-second response for real-time queries. Which Azure service is best suited for this workload?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 Data Explorer
Azure Data Explorer (ADX) is purpose-built for high-performance analysis of large volumes of streaming telemetry data. It supports ingestion from IoT hubs, automatic indexing for sub-second queries on recent data (e.g., last hour), and cost-effective long-term storage via hot/cold tiering for years of historical analytics. Its columnar storage and Kusto Query Language (KQL) are optimized for time-series and aggregation queries, making it ideal for this mixed real-time and historical workload.
Key principle: Azure Data Explorer is optimized for time-series and log analytics.
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 with SQL API
Why it's wrong here
While Cosmos DB provides low-latency access and global distribution, it is not optimized for time-series analytical workloads. Storing billions of sensor readings would be very expensive and query performance for complex aggregations is not as efficient as a purpose-built time-series solution.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational database that can handle time-series data but is not designed for high-velocity ingestion (millions of records per second) and its indexing strategies may not support sub-second queries on recent data while also allowing efficient ad-hoc analytics on historical data.
- ✓
Azure Data Explorer
Why this is correct
Azure Data Explorer is specifically built for time-series and log analytics. It supports high-throughput ingestion, automatic indexing, caching for hot data (sub-second queries), and retention-based tiering to cold storage for historical analysis, minimizing costs.
Clue confirmation
The clue words "best", "minimum / minimize" in the question point toward this answer.
Related concept
Azure Data Explorer is optimized for time-series and log analytics.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a key-value store that can store large amounts of structured data, but it lacks the ability to run complex analytical queries (aggregations, time-window functions) efficiently. It is not suitable for this workload.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that any database with low-latency reads (like Cosmos DB) can handle both real-time and historical analytics, but the trap is that Cosmos DB lacks the columnar storage and query engine optimized for time-series aggregations, making it cost-prohibitive and slow for complex analytical queries over years of data.
Detailed technical explanation
How to think about this question
Under the hood, Azure Data Explorer uses a distributed columnar storage engine with automatic data sharding and a write-optimized delta store that flushes to read-optimized extents, enabling both fast ingestion and instant queryability. Its hot/cold tiering automatically moves older data to cheaper blob storage while maintaining query access via a unified metadata layer. In real-world scenarios, ADX can ingest millions of events per second and return aggregations over the last hour in under 100ms using its materialized views and data sharding.
KKey Concepts to Remember
- Azure Data Explorer is optimized for time-series and log analytics.
- It supports high-throughput ingestion and sub-second query response for hot data.
- ADX uses columnar storage and advanced indexing for analytical queries.
- It offers cost-effective tiered storage for historical data retention.
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
Azure Data Explorer is optimized for time-series and log analytics.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review azure Data Explorer is optimized for time-series and log analytics., then practise related DP-900 questions on the same topic to reinforce the concept.
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Describe an analytics workload on Azure — study guide chapter
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Azure Data Explorer is optimized for time-series and log analytics..
What is the correct answer to this question?
The correct answer is: Azure Data Explorer — Azure Data Explorer (ADX) is purpose-built for high-performance analysis of large volumes of streaming telemetry data. It supports ingestion from IoT hubs, automatic indexing for sub-second queries on recent data (e.g., last hour), and cost-effective long-term storage via hot/cold tiering for years of historical analytics. Its columnar storage and Kusto Query Language (KQL) are optimized for time-series and aggregation queries, making it ideal for this mixed real-time and historical workload.
What should I do if I get this DP-900 question wrong?
Review azure Data Explorer is optimized for time-series and log analytics., then practise related DP-900 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "best", "minimum / minimize". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Azure Data Explorer is optimized for time-series and log analytics.
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Last reviewed: Jun 30, 2026
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