The answer is Azure Data Explorer for real-time analytics and Azure Blob Storage for historical data. This combination works because Azure Data Explorer is a purpose-built analytics engine that ingests millions of small JSON events per second and delivers sub-second query latency on the most recent 24 hours of data, while Azure Blob Storage provides cost-optimized Cool or Archive tiers for older data that only needs occasional compliance queries. On the AZ-305 exam, this scenario tests your ability to match workload characteristics—high velocity and low latency versus long-term cost efficiency—to the correct Azure data services. A common trap is choosing Azure Stream Analytics or Azure SQL Database, which lack ADX’s native ingestion speed for massive IoT streams or Blob’s archival cost savings. Remember the memory tip: “Hot in ADX, cold in Blob”—keep recent data blazing fast in Data Explorer, then freeze historical data in Blob Storage.
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.
You are designing a data storage solution for an IoT application that ingests millions of events per second. Each event is a small JSON message (under 1 KB). The solution must support real-time analytics and allow queries on recent data (last 24 hours) with low latency. Historical data (older than 24 hours) should be stored in a cost-optimized manner for occasional compliance queries. Which combination of Azure services should you recommend?
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 for real-time analytics and Azure Blob Storage for historical data
Azure Data Explorer (ADX) is purpose-built for real-time analytics on high-velocity data streams, ingesting millions of events per second with sub-second query latency on recent data. Azure Blob Storage provides a cost-optimized tier (e.g., Cool or Archive) for historical data older than 24 hours, which can be queried occasionally via ADX’s continuous export or external table feature. This combination meets both the low-latency real-time analytics requirement and the cost-effective long-term storage need.
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 for both real-time and historical data
Why it's wrong here
Cosmos DB has limited query performance for this scale and is not cost-effective for historical data.
✗
Azure Event Hubs for ingestion and Azure Functions for querying
Why it's wrong here
Event Hubs is not a queryable store; Functions are not suitable for real-time analytics.
✗
Azure SQL Database with elastic pool
Why it's wrong here
Not designed for high-throughput streaming ingest.
✓
Azure Data Explorer for real-time analytics and Azure Blob Storage for historical data
Why this is correct
ADX handles high-throughput ingest and real-time queries; Blob Storage is cost-effective for cold data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse high-throughput ingestion with query capability, assuming that any service that can ingest data (like Event Hubs) can also serve real-time queries, or that a general-purpose database (like Cosmos DB or SQL Database) can handle the extreme volume and analytics pattern of IoT telemetry.
Detailed technical explanation
How to think about this question
Azure Data Explorer uses a columnar storage engine with a distributed query execution model that achieves sub-second response times on petabyte-scale data by leveraging a Kusto Query Language (KQL) optimized for time-series and log analytics. The continuous export feature can automatically move data older than 24 hours to Azure Blob Storage in formats like Parquet or CSV, and ADX can query that historical data in place using external tables without re-ingesting it, balancing performance and cost. In a real-world IoT scenario, this allows dashboards to query last-minute data with millisecond latency while compliance audits access year-old data from Blob Storage at a fraction of the cost.
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 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.
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 for real-time analytics and Azure Blob Storage for historical data — Azure Data Explorer (ADX) is purpose-built for real-time analytics on high-velocity data streams, ingesting millions of events per second with sub-second query latency on recent data. Azure Blob Storage provides a cost-optimized tier (e.g., Cool or Archive) for historical data older than 24 hours, which can be queried occasionally via ADX’s continuous export or external table feature. This combination meets both the low-latency real-time analytics requirement and the cost-effective long-term storage need.
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.
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Question Discussion
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