Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A manufacturing company installs IoT sensors on equipment in a factory. Each sensor sends a reading (device ID, timestamp, temperature, vibration) every second. The application must store these readings with extremely low write latency, support queries for the latest reading per device, and allow range queries over the last hour for a specific device. The development team expects high throughput writes (millions per day) and does not require complex joins. Which Azure data store is most appropriate for this workload?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
Azure Cosmos DB
Correct. Azure Cosmos DB provides low-latency writes, flexible schema, and supports point reads and range queries with automatic indexing. It is designed for high-throughput, globally distributed workloads like IoT time-series data.
B
Distractor review
Azure Table Storage
Incorrect. While Azure Table Storage is a NoSQL key-value store that is cost-effective, it has higher latency and does not support complex range queries on non-partition key fields as efficiently as Cosmos DB.
C
Distractor review
Azure Blob Storage
Incorrect. Azure Blob Storage is optimized for storing large unstructured objects such as files, images, and documents. It is not designed for per-second time-series data with frequent point reads.
D
Distractor review
Azure SQL Database
Incorrect. Azure SQL Database is a relational database that can handle this workload, but its write latency and cost for millions of writes per day would be higher than a purpose-built NoSQL store like Cosmos DB.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Question 2
A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?
Question 3
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Question 4
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
Question 5
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
Question 6
A company collects customer feedback forms. Each form contains always-present fields like CustomerID and SubmissionDate, but also a free-text Comments field and optional fields like Rating or ProductCategory that vary between forms. How should this data be classified?
FAQ
Questions learners often ask
What does this DP-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Azure Cosmos DB — This scenario requires high-velocity data ingestion with low-latency writes and queries focused on a single device and time range. Azure Cosmos DB (using the Cassandra or SQL API) is a globally distributed, NoSQL database that provides millisecond latency for both writes and reads, and scales horizontally to handle massive throughput. Azure Table Storage is cheaper but offers higher latency and less flexible querying. Azure Blob Storage is designed for unstructured data like files, not for per-second time-series records with point queries. Azure SQL Database can handle the work but with higher latency and cost for this volume, and it is relational, which is unnecessary here. Thus, Azure Cosmos DB is the best fit.
What should I do if I get this DP-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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