Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A logistics company stores sensor data from delivery trucks in Azure Table Storage. Each sensor reading includes a TruckID, Timestamp, Location, and EngineTemperature. The most common query retrieves all readings for all trucks within a specific one-hour time window (e.g., between 10:00 and 11:00 on a given day). Currently, the table uses PartitionKey = TruckID and RowKey = Timestamp (ISO format). However, queries filtering by time range are slow and consume many transactions. Which design change will most improve the performance of these time-range queries?
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
Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp.
This design groups all data for a given day in one partition, so a time-range query scans exactly one partition, drastically reducing transactions and cost.
B
Distractor review
Change RowKey to be a composite of TruckID and Timestamp while keeping PartitionKey as TruckID.
This does not change the partition structure; queries across all trucks still need to scan every partition, providing no performance gain.
C
Distractor review
Use Azure Cosmos DB with a partition key on Timestamp instead of Azure Table Storage.
While Cosmos DB could be used, it is a more expensive and complex solution. The simpler optimization within Azure Table Storage is to adjust the partition key design.
D
Distractor review
Enable indexing on the Timestamp column in Azure Table Storage.
Azure Table Storage does not support secondary indexes on custom properties; only the RowKey index (within a partition) is available. This option is not feasible.
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: Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp. — The current partition key (TruckID) scatters data per truck, but a time-range query across all trucks must scan every partition (since each partition only contains one truck's data). Changing the partition key to a date-based value (e.g., YYYY-MM-DD) groups all readings for that day into a single partition, allowing the query to scan only the relevant partition for the time window. This significantly reduces transaction consumption and improves latency. Secondary indexes are not supported in Azure Table Storage for custom properties, and using a different service is not necessary.
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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