The answer is Azure Data Explorer. This is the correct choice because it is a fully managed, high-performance analytics service specifically optimized for ingesting, indexing, and querying large volumes of time-series and log data from IoT devices, using the Kusto Query Language (KQL) to perform long-term trend analysis and anomaly detection with built-in functions like `series_decompose()`. On the AZ-900 exam, this question tests your understanding of which Azure service handles time-series IoT analytics, often appearing as a distractor against Azure Time Series Insights (which is being retired) or Azure SQL Database. A common trap is confusing Azure Data Explorer with Azure Monitor Logs, but remember that ADX is the dedicated big data analytics engine for ad-hoc, high-speed queries on streaming telemetry. Memory tip: think of "ADX" as "Analytics for Data eXploration" — it’s the go-to for deep-dive time-series analysis on IoT data.
AZ-900 Describe Azure architecture and services Practice Question
This AZ-900 practice question tests your understanding of describe azure architecture and services. 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.
Which Azure database service stores time-series data from IoT devices for long-term trend analysis and anomaly detection?
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 a fully managed, high-performance big data analytics service optimized for interactive analysis of large volumes of time-series and log data. It uses the Kusto Query Language (KQL) to ingest, index, and query streaming telemetry from IoT devices, enabling long-term trend analysis and anomaly detection through built-in time-series functions like `series_decompose()` and `series_fit_line()`.
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 it's wrong here
Cosmos DB is a general-purpose multi-model database; Azure Data Explorer is optimized for time-series analytics.
✓
Azure Data Explorer
Why this is correct
Azure Data Explorer (ADX) is optimized for real-time analytics on time-series data from IoT and telemetry sources.
Related concept
Read the scenario before looking for a memorised answer.
SQL Database handles OLTP workloads; ADX is purpose-built for time-series analytics at scale.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Cosmos DB's support for IoT device state storage with the need for a dedicated time-series analytics engine, overlooking that Cosmos DB lacks native time-series decomposition and anomaly detection functions required for long-term trend analysis.
Detailed technical explanation
How to think about this question
Under the hood, Azure Data Explorer uses a columnar storage engine with automatic indexing and data sharding, allowing it to ingest millions of events per second while maintaining sub-second query performance on terabytes of data. Its native time-series capabilities include functions like `series_periods_detect()` for periodicity detection and `series_less()` for anomaly scoring, which are implemented as compiled operators in the Kusto engine. In a real-world scenario, a smart factory might use ADX to ingest sensor data from thousands of PLCs, then run KQL queries to detect vibration anomalies months after the data was collected.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-900 question in full detail.
Describe Azure architecture and services — This question tests Describe Azure architecture and services — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Data Explorer — Azure Data Explorer (ADX) is a fully managed, high-performance big data analytics service optimized for interactive analysis of large volumes of time-series and log data. It uses the Kusto Query Language (KQL) to ingest, index, and query streaming telemetry from IoT devices, enabling long-term trend analysis and anomaly detection through built-in time-series functions like `series_decompose()` and `series_fit_line()`.
What should I do if I get this AZ-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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What type of data does Azure Table Storage store?
easy
A.Unstructured binary data like images and videos
✓ B.Structured NoSQL data in a key-attribute entity model
C.Relational data with complex joins and foreign keys
D.Files shared via SMB protocol across Windows machines
Why B: Azure Table Storage is a NoSQL key-attribute store that stores structured, schema-less data. Each entity is a set of properties (attributes) with a partition key and row key, enabling fast access to semi-structured data like user profiles or device metadata.
Last reviewed: Jun 11, 2026
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