The answer is Azure Data Explorer (ADX). This is the correct choice because ADX is purpose-built for interactive analytics on large volumes of streaming, time-series data, supporting append-only ingestion and low-latency queries over time ranges via its Kusto Query Language (KQL), while also offering native automated retention policies such as soft-delete and hard-delete periods that require no manual management. On the Microsoft Azure Solutions Architect Expert AZ-305 exam, this scenario tests your ability to match workload characteristics—specifically high-ingest, append-only, time-range query patterns—to the correct Azure data service; a common trap is choosing Azure Time Series Insights (which is deprecated) or Azure Cosmos DB (which is not optimized for append-only analytics). Remember the memory tip: “ADX for append-only X-ray vision”—ADX excels when you need to rapidly query and retain massive time-series streams without updates.
AZ-305 Design data storage solutions Practice Question
This AZ-305 practice question tests your understanding of design data storage solutions. 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 company is designing a data storage solution for an IoT pipeline that ingests time-series data from millions of devices. The data is append-only and queried by time range. The solution must support low-latency queries and automated retention policies. Which Azure data store should they choose?
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 (ADX)
Azure Data Explorer (ADX) is purpose-built for interactive analytics on large volumes of streaming, time-series data. It supports append-only ingestion, low-latency queries over time ranges via its Kusto Query Language (KQL), and native automated retention policies (e.g., soft-delete and hard-delete periods) without manual management.
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 SQL Database
Why it's wrong here
SQL Database is relational and not designed for high-throughput append-only IoT data.
✗
Azure Cosmos DB
Why it's wrong here
Cosmos DB is for real-time transactional workloads, not optimized for append-only time-series.
✓
Azure Data Explorer (ADX)
Why this is correct
ADX is purpose-built for time-series and IoT data, with fast ingestion and querying.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Blob Storage with Azure Data Lake Storage Gen2
Why it's wrong here
This is for data lakes, not low-latency interactive queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Cosmos DB's low-latency individual item access with the need for time-series range queries, overlooking that Cosmos DB lacks native time-series indexing and automated retention policies, while ADX is the only Azure service explicitly designed for high-throughput append-only time-series analytics with built-in lifecycle management.
Detailed technical explanation
How to think about this question
ADX uses a columnar storage engine with a distributed, sharded architecture that partitions data by ingestion time, enabling efficient time-range queries via min/max statistics and zone maps. Its automated retention policies are enforced at the table level using a soft-delete period (data remains queryable) and a hard-delete period (data is physically removed), which is critical for compliance and cost management in IoT scenarios. In real-world deployments, ADX can ingest millions of events per second from IoT hubs and return aggregation queries over months of data in sub-seconds.
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-305 question in full detail.
Design data storage solutions — This question tests Design data storage solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Data Explorer (ADX) — Azure Data Explorer (ADX) is purpose-built for interactive analytics on large volumes of streaming, time-series data. It supports append-only ingestion, low-latency queries over time ranges via its Kusto Query Language (KQL), and native automated retention policies (e.g., soft-delete and hard-delete periods) without manual management.
What should I do if I get this AZ-305 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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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. A company ingests millions of IoT sensor data points per second. They need a fully managed analytics service optimized for time-series data that can ingest high-velocity data, perform real-time analytics, and store data for historical analysis. The solution must integrate with Azure Stream Analytics for stream processing. Which Azure data service should they choose?
medium
A.Azure Cosmos DB
B.Azure SQL Database
✓ C.Azure Data Explorer (ADX)
D.Azure Blob Storage
Why C: Azure Data Explorer (ADX) is the correct choice because it is a fully managed, high-performance analytics service optimized for time-series and log data. It can ingest millions of IoT sensor data points per second, perform real-time analytics with sub-second query latency, and store data for historical analysis. ADX natively integrates with Azure Stream Analytics for stream processing, making it ideal for this scenario.
Last reviewed: Jun 24, 2026
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