The answer is Blob index tags. This feature allows you to attach custom key-value metadata—such as device ID and timestamp—directly to each blob, and then run a filtered query across containers or even storage accounts to retrieve blobs matching specific criteria. For the manufacturing scenario, instead of parsing blob names or maintaining a separate database, you simply tag each blob with its device ID and date, then query for all blobs where device ID equals a given value and timestamp falls within a date range. On the DP-900 exam, this tests your understanding of Azure Blob Storage’s native indexing capabilities versus alternatives like blob naming conventions or Azure Table Storage. A common trap is confusing blob index tags with blob metadata (which is not queryable) or with Azure Cognitive Search. Remember the mnemonic: “Tag it, then bag it”—apply tags first, then query to retrieve your blobs efficiently.
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
This DP-900 practice question tests your understanding of describe considerations for working with non-relational data on azure. 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 manufacturing company stores IoT sensor data as blobs in Azure Blob Storage. Each blob is named with a device ID and a timestamp, and they need to quickly find all blobs for a specific device within a date range. Which Azure Blob Storage feature should they use to query blobs based on custom metadata?
Why wrong: Blob snapshots capture a point-in-time read-only copy of a blob, but they do not support querying blobs by custom metadata.
B
Blob soft delete
Why wrong: Blob soft delete protects blobs from accidental deletion by retaining them for a specified period, but it does not provide a way to query blobs by metadata.
C
Blob index tags
Blob index tags let you apply custom key-value pairs as metadata and then filter and query blobs based on those tags, enabling efficient retrieval of blobs by device ID and timestamp.
D
Blob lifecycle management
Why wrong: Blob lifecycle management automates moving blobs to different access tiers or deleting them based on rules, but it does not allow querying blobs by custom metadata.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Blob index tags
Blob index tags allow you to apply custom key-value metadata to blobs and then query them using a filtered query across containers or storage accounts. This enables efficient retrieval of blobs by device ID and timestamp without scanning all blob names or maintaining a separate index.
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.
✗
Blob snapshots
Why it's wrong here
Blob snapshots capture a point-in-time read-only copy of a blob, but they do not support querying blobs by custom metadata.
✗
Blob soft delete
Why it's wrong here
Blob soft delete protects blobs from accidental deletion by retaining them for a specified period, but it does not provide a way to query blobs by metadata.
✓
Blob index tags
Why this is correct
Blob index tags let you apply custom key-value pairs as metadata and then filter and query blobs based on those tags, enabling efficient retrieval of blobs by device ID and timestamp.
Related concept
Read the scenario before looking for a memorised answer.
✗
Blob lifecycle management
Why it's wrong here
Blob lifecycle management automates moving blobs to different access tiers or deleting them based on rules, but it does not allow querying blobs by custom metadata.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse blob index tags with blob naming conventions or metadata stored in a separate database, thinking that blob name patterns alone are sufficient for efficient querying, but Azure Blob Storage does not natively support server-side filtering by name patterns.
Detailed technical explanation
How to think about this question
Blob index tags are stored as a flat key-value pair on the blob object and are indexed automatically by Azure Storage, enabling queries with the `Find Blobs by Tags` REST API or the `az storage blob list` CLI with `--tag-filter`. This feature supports up to 10 tags per blob and is ideal for scenarios like IoT telemetry where blobs must be filtered by device ID and timestamp without relying on hierarchical namespace or custom indexing solutions.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe considerations for working with non-relational data on Azure — This question tests Describe considerations for working with non-relational data on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Blob index tags — Blob index tags allow you to apply custom key-value metadata to blobs and then query them using a filtered query across containers or storage accounts. This enables efficient retrieval of blobs by device ID and timestamp without scanning all blob names or maintaining a separate index.
What should I do if I get this DP-900 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.
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Question Discussion
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