Describe considerations for working with non-relational data on Azure →easyMultiple ChoiceObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
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?
⚠ Common exam trap
Test-takers frequently 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.
Answer choices
Why each option matters
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.
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 read-only, point-in-time version of an individual blob at the moment the snapshot is created, commonly used for backup or rollback scenarios. Snapshot capabilities are tied to an existing blob and do not involve any global indexing; you cannot query snapshots by device ID or timestamp because snapshots themselves are not tagged or filtered. They are a versioning feature, not a query feature.
When this WOULD be correct
A question asks how to preserve previous versions of a blob for rollback or comparison purposes, such as restoring a blob to an earlier state without enabling versioning.
- ✗
Blob soft delete
Why it's wrong here
Blob soft delete is a data-protection mechanism that, when enabled, maintains a deleted-blob retention period so you can recover blobs after accidental deletion. It does not examine or index blob metadata, and it provides no query construct for filtering across containers based on device IDs or timestamps. Its sole purpose is to prevent permanent data loss, not to support search or retrieval.
When this WOULD be correct
A company wants to recover blobs that were accidentally deleted or overwritten within the last 30 days. They need a feature that preserves deleted blobs for a configurable period and allows restoration. Blob soft delete would be the correct answer.
- ✓
Blob index tags
Why this is correct
Blob index tags are user-defined key-value pairs that Azure stores as metadata on each blob and maintains in an internal searchable index. You can query blobs using the Find Blobs by Tags API, filtering on tags such as deviceID and sensorTimestamp, without needing to ingest the blob content or scan containers. This is exactly the capability needed to retrieve IoT sensor blobs by device and time criteria.
- ✗
Blob lifecycle management
Why it's wrong here
Blob lifecycle management is an automation feature that applies rules to move blobs between access tiers (hot, cool, archive) or delete them after a defined age or last-modified date. It operates on scheduling and cost optimization, but it does not create an index or support ad-hoc queries for finding blobs by custom key-value metadata. Therefore it cannot answer 'give me all blobs for device X at time Y' efficiently.
When this WOULD be correct
A question asking how to automatically move blobs older than 30 days to cool storage or delete blobs after 90 days to reduce costs would make lifecycle management the correct answer.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Blob index tagsCorrect answer▾
Why this is correct
Blob index tags are user-defined key-value pairs that Azure stores as metadata on each blob and maintains in an internal searchable index. You can query blobs using the Find Blobs by Tags API, filtering on tags such as deviceID and sensorTimestamp, without needing to ingest the blob content or scan containers. This is exactly the capability needed to retrieve IoT sensor blobs by device and time criteria.
✗Blob snapshotsWrong answer — click to see why▾
Why this is wrong here
Blob snapshots capture point-in-time read-only copies of blobs, but they do not support querying blobs based on custom metadata like device ID or timestamp.
★ When this WOULD be the correct answer
A question asks how to preserve previous versions of a blob for rollback or comparison purposes, such as restoring a blob to an earlier state without enabling versioning.
Why candidates choose this
Candidates may confuse snapshots with metadata indexing because both involve blob properties, but snapshots are for versioning, not querying.
✗Blob soft deleteWrong answer — click to see why▾
Why this is wrong here
Blob soft delete protects blobs from accidental deletion or overwriting by retaining them for a specified retention period; it does not support querying blobs based on custom metadata or indexing.
★ When this WOULD be the correct answer
A company wants to recover blobs that were accidentally deleted or overwritten within the last 30 days. They need a feature that preserves deleted blobs for a configurable period and allows restoration. Blob soft delete would be the correct answer.
Why candidates choose this
Candidates may confuse 'soft delete' with a way to filter or search blobs, thinking it provides a queryable state, or they may misremember the purpose of soft delete as a metadata management feature.
✗Blob lifecycle managementWrong answer — click to see why▾
Why this is wrong here
Blob lifecycle management automates tier transitions or deletion based on age or last modification, not querying blobs by custom metadata like device ID and timestamp.
★ When this WOULD be the correct answer
A question asking how to automatically move blobs older than 30 days to cool storage or delete blobs after 90 days to reduce costs would make lifecycle management the correct answer.
Why candidates choose this
Candidates may confuse lifecycle management with indexing because both involve rules based on time, but lifecycle management is for storage optimization, not metadata search.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Blob storage
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.