hardmultiple choiceObjective-mapped

A company stores IoT temperature readings in Azure Cosmos DB using the NoSQL API. Each document contains: DeviceID, Timestamp, Temperature, Location. Data is ingested at a rate of 10,000 documents per second from thousands of devices. The most common query is 'Get all readings for a specific DeviceID in the last hour.' Which partition key should be chosen to avoid hot partitions while still supporting the query efficiently?

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A company stores IoT temperature readings in Azure Cosmos DB using the NoSQL API. Each document contains: DeviceID, Timestamp, Temperature, Location. Data is ingested at a rate of 10,000 documents per second from thousands of devices. The most common query is 'Get all readings for a specific DeviceID in the last hour.' Which partition key should be chosen to avoid hot partitions while still supporting the query efficiently?

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

DeviceID

While DeviceID makes per-device queries efficient (point reads), it can create a hot partition if a single device generates a disproportionate amount of writes, because all documents for that device would reside on one partition.

B

Distractor review

Timestamp (e.g., per minute)

Timestamp as partition key spreads writes evenly, but queries for a specific device across time would need to read every partition (cross-partition query), increasing RU consumption and latency.

C

Distractor review

Location

Location is unlikely to be unique per device; many devices share a location, leading to hot partitions. Moreover, queries by DeviceID would still require a cross-partition scan.

D

Best answer

A synthetic key combining DeviceID and Timestamp (e.g., DeviceID_yyyy-MM-dd-HH)

This distributes writes across partitions because the suffix changes each hour, preventing a single device from overloading one partition. For the query 'get readings for DeviceID in the last hour', the application can compute the exact partition key(s) for the relevant hour(s) and perform efficient point or limited cross-partition queries.

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: A synthetic key combining DeviceID and Timestamp (e.g., DeviceID_yyyy-MM-dd-HH) — To avoid hot partitions, the partition key should distribute write traffic evenly across partitions. Using a synthetic key that combines DeviceID with a time component (e.g., DeviceID + date-hour) ensures that writes from a single device are spread across multiple partitions (because the time part changes every hour), while queries for a specific device within a given hour still target only the relevant partition(s) for that hour. Option A (DeviceID) could cause a hot partition if a single device writes very frequently (e.g., thousands of readings per second) because all that data would land on the same partition. Option B (Timestamp) would scatter all devices across time, forcing every per-device query to fan out to all partitions. Option C (Location) would not help per-device queries and could also cause hot partitions if many devices are in the same location. Option D provides a good balance.

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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