Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A manufacturing company collects sensor data from thousands of IoT devices. The data arrives as a stream of time-stamped readings with a fixed schema (DeviceID, Timestamp, Temperature, Pressure, Vibration). They need to store this data and support both real-time dashboards showing the last hour of data and complex analytical queries over years of historical data. The solution must minimize storage costs and provide sub-second response for real-time queries. Which Azure service is best suited 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
Distractor review
Azure Cosmos DB with SQL API
While Cosmos DB provides low-latency access and global distribution, it is not optimized for time-series analytical workloads. Storing billions of sensor readings would be very expensive and query performance for complex aggregations is not as efficient as a purpose-built time-series solution.
B
Distractor review
Azure SQL Database
Azure SQL Database is a relational database that can handle time-series data but is not designed for high-velocity ingestion (millions of records per second) and its indexing strategies may not support sub-second queries on recent data while also allowing efficient ad-hoc analytics on historical data.
C
Best answer
Azure Data Explorer
Azure Data Explorer is specifically built for time-series and log analytics. It supports high-throughput ingestion, automatic indexing, caching for hot data (sub-second queries), and retention-based tiering to cold storage for historical analysis, minimizing costs.
D
Distractor review
Azure Table Storage
Azure Table Storage is a key-value store that can store large amounts of structured data, but it lacks the ability to run complex analytical queries (aggregations, time-window functions) efficiently. It is not suitable for this workload.
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 Data Explorer — Azure Data Explorer (ADX) is a fast, fully managed data analytics service optimized for time-series and log data. It can ingest high-velocity streaming data, supports sub-second queries on recent data (via caching policies), and can automatically move older data to cheaper storage (via retention and caching policies). It uses Kusto Query Language (KQL) for analytics. Azure Cosmos DB is document-oriented and not cost-effective for massive time-series. Azure SQL Database is not built for high-throughput ingestion and real-time dashboards. Azure Table Storage lacks analytical query capabilities.
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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