- A
Azure Cosmos DB with SQL API
Why wrong: Incorrect. While Cosmos DB can handle high throughput and time-series data, it is more expensive and often unnecessary for the stated requirement for a startup's cost-sensitive workload; simpler alternatives exist.
- B
Azure SQL Database with columnstore index
Why wrong: Incorrect. Although SQL Database can manage time-series data with columnstore indexes, it is not optimized for extremely high write throughput and can be costly.
- C
Azure Table Storage
Correct. Azure Table Storage is designed for high-volume structured data and supports efficient point queries and range scans on PartitionKey and RowKey, making it ideal for time-series data at low cost.
- D
Azure Data Lake Storage Gen2
Why wrong: Incorrect. Data Lake Storage Gen2 is optimized for big data analytics and batch processing, not for low-latency, high-throughput time-series writes and range queries.
Quick Answer
The answer is Azure Table Storage. This NoSQL key-value store is the most suitable choice for a time-series data store with high write throughput because it supports low-latency, high-volume writes and efficient timestamp-based range queries when you structure the RowKey as a timestamp, leveraging the PartitionKey and RowKey for fast retrieval. On the Microsoft Azure Solutions Architect Expert AZ-305 exam, this scenario tests your ability to match workload patterns to Azure data services, often contrasting Table Storage with Cosmos DB or SQL Database; a common trap is overcomplicating the solution by choosing Cosmos DB for its indexing features, but Table Storage excels here due to its schema-less design and ability to scale to massive throughput without sharding overhead. To remember this, think of Table Storage as the "ticking time-series table"—its simple key-value structure with a timestamp RowKey makes it a natural fit for streaming event ingestion and fast time-based queries.
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 startup is building a social media analytics platform that processes streaming data. They need a data store for time-series events with high write throughput and fast timestamp-based range queries. Which Azure data store is most suitable for this workload?
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 Table Storage
Azure Table Storage is a NoSQL key-value store that supports high-volume, low-latency writes and efficient range queries on the PartitionKey and RowKey, which can be structured as a timestamp for time-series data. Its schema-less design and ability to scale to massive throughput without sharding overhead make it ideal for streaming event ingestion and timestamp-based retrieval.
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 with SQL API
Why it's wrong here
Incorrect. While Cosmos DB can handle high throughput and time-series data, it is more expensive and often unnecessary for the stated requirement for a startup's cost-sensitive workload; simpler alternatives exist.
- ✗
Azure SQL Database with columnstore index
Why it's wrong here
Incorrect. Although SQL Database can manage time-series data with columnstore indexes, it is not optimized for extremely high write throughput and can be costly.
- ✓
Azure Table Storage
Why this is correct
Correct. Azure Table Storage is designed for high-volume structured data and supports efficient point queries and range scans on PartitionKey and RowKey, making it ideal for time-series data at low cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Incorrect. Data Lake Storage Gen2 is optimized for big data analytics and batch processing, not for low-latency, high-throughput time-series writes and range queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Cosmos DB for its flexibility and global distribution, but for a simple, high-throughput time-series workload with timestamp-based queries, Azure Table Storage is the most cost-effective and performant choice, as Cosmos DB adds unnecessary complexity and cost.
Detailed technical explanation
How to think about this question
Azure Table Storage uses a flat namespace with PartitionKey and RowKey as a composite primary key; for time-series data, you can set PartitionKey to a date/hour bucket and RowKey to a timestamp (e.g., inverted Ticks) to enable fast range scans within a partition. The service automatically partitions data based on load, supporting up to 20,000 transactions per second per partition, and can scale to handle billions of events with consistent single-digit millisecond latency for point queries.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Design data storage solutions — study guide chapter
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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 Table Storage — Azure Table Storage is a NoSQL key-value store that supports high-volume, low-latency writes and efficient range queries on the PartitionKey and RowKey, which can be structured as a timestamp for time-series data. Its schema-less design and ability to scale to massive throughput without sharding overhead make it ideal for streaming event ingestion and timestamp-based retrieval.
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
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Last reviewed: Jun 11, 2026
This AZ-305 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-305 exam.
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