- A
Azure Blob Storage with Azure Data Lake Storage Gen2
Why wrong: Blob Storage is for object storage and does not natively support low-latency queries required for real-time dashboards.
- B
Azure Cosmos DB with SQL API
Why wrong: Cosmos DB is a globally distributed NoSQL database, but it is not optimized for high-ingestion time-series workloads and can be cost-prohibitive at this scale.
- C
Azure Event Hubs and Azure Data Explorer
Event Hubs ingests high volumes of event data, and Azure Data Explorer provides fast, real-time analytics on time-series data. This combination is best suited for IoT sensor data.
- D
Azure Table Storage
Why wrong: Table Storage is a key-value store with limited query capabilities and does not support the low-latency analytical queries required for this scenario.
Quick Answer
The correct answer is Azure Event Hubs and Azure Data Explorer because this combination directly addresses the need for high ingestion rates and low-latency queries on time-series IoT sensor data. Event Hubs is built for massive throughput, easily handling millions of events per second from devices, while Azure Data Explorer (ADX) is a purpose-built analytics engine for time-series data, offering sub-second query performance on recent data and efficient historical analysis. On the AZ-305 exam, this scenario tests your ability to pair ingestion and storage/query services for real-time analytics pipelines; a common trap is choosing Azure Stream Analytics alone, which processes but doesn’t natively store for historical queries, or Azure SQL Database, which struggles with high-velocity time-series ingestion. Remember the pairing: Event Hubs for the firehose, ADX for the fast queries—think “ingest with Event Hubs, analyze with ADX” to avoid mixing up Azure Data Lake Storage, which is better for batch, not sub-second, analytics.
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 needs to store sensor data from IoT devices. Each device sends a message every second. The data is time-series and will be queried for real-time dashboards and historical analysis. The solution must support high ingestion rates and low-latency queries on recent data. Which Azure service should they use?
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 Event Hubs and Azure Data Explorer
Azure Event Hubs is designed for high-throughput data ingestion from millions of IoT devices, capable of handling millions of events per second. Azure Data Explorer (ADX) is optimized for time-series data, providing sub-second query latency on recent data and efficient historical analysis. Together, they form a serverless pipeline that ingests sensor data via Event Hubs and stores it in ADX for real-time dashboards and long-term analytics.
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 Blob Storage with Azure Data Lake Storage Gen2
Why it's wrong here
Blob Storage is for object storage and does not natively support low-latency queries required for real-time dashboards.
- ✗
Azure Cosmos DB with SQL API
Why it's wrong here
Cosmos DB is a globally distributed NoSQL database, but it is not optimized for high-ingestion time-series workloads and can be cost-prohibitive at this scale.
- ✓
Azure Event Hubs and Azure Data Explorer
Why this is correct
Event Hubs ingests high volumes of event data, and Azure Data Explorer provides fast, real-time analytics on time-series data. This combination is best suited for IoT sensor data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Table Storage
Why it's wrong here
Table Storage is a key-value store with limited query capabilities and does not support the low-latency analytical queries required for this scenario.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Azure Cosmos DB (Option B) because they associate it with 'low latency' and 'IoT', but they overlook that Cosmos DB is not purpose-built for time-series data and lacks the ingestion throughput and query optimizations that Azure Data Explorer provides for this specific workload.
Trap categories for this question
Scenario analysis trap
Table Storage is a key-value store with limited query capabilities and does not support the low-latency analytical queries required for this scenario.
Detailed technical explanation
How to think about this question
Event Hubs uses AMQP 1.0 or HTTPS for ingestion and supports automatic partitioning to scale throughput; it can buffer events for up to 7 days. Azure Data Explorer uses a columnar storage engine with a Kusto Query Language (KQL) that includes time-series functions like `series_decompose` and `make-series`, enabling real-time aggregation and anomaly detection. A common real-world scenario is ingesting telemetry from thousands of wind turbines, where ADX can answer 'average wind speed per minute for the last hour' in under 100ms.
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
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FAQ
Questions learners often ask
What does this AZ-305 question test?
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 Event Hubs and Azure Data Explorer — Azure Event Hubs is designed for high-throughput data ingestion from millions of IoT devices, capable of handling millions of events per second. Azure Data Explorer (ADX) is optimized for time-series data, providing sub-second query latency on recent data and efficient historical analysis. Together, they form a serverless pipeline that ingests sensor data via Event Hubs and stores it in ADX for real-time dashboards and long-term analytics.
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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Last reviewed: Jun 11, 2026
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