Describe considerations for working with non-relational data on Azure →hardMultiple ChoiceObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
You are designing a solution to store and analyze large volumes of streaming data from social media feeds. The data is semi-structured (JSON) and will be used for real-time dashboards. You need to choose a storage solution that can handle high-ingestion throughput and support querying with Azure Synapse Serverless SQL. Which storage option should you choose?
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
✓
Azure Data Lake Storage Gen2
(Azure Data Lake Storage Gen2) is correct because it is built on Azure Blob Storage, supports high-throughput ingestion of streaming data, and can be directly queried using Azure Synapse Serverless SQL. Option A (Azure Table Storage) is wrong because it is designed for structured NoSQL key-value data, not for analytics or semi-structured JSON. Option C (Azure Cosmos DB) is optimized for transactional workloads and real-time applications; although it can be integrated with Synapse via Synapse Link, it is not the primary choice for direct Serverless SQL queries on streaming data. Option D (Azure Cache for Redis) is an in-memory cache, not a durable storage solution for analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a NoSQL key-value store designed for high-volume, low-latency transactional access to semi-structured data. It does not provide a SQL query engine, columnar storage, or partitioning that supports large-scale analytical scans like Synapse Serverless SQL requires. As a result, it is not suitable for analytics on large volumes of data, which is why it is incorrect for this solution.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is a hierarchical file system built on Azure Blob Storage that stores data in open formats such as Parquet and ORC, enabling massive parallel ingestion. Synapse Serverless SQL can query files directly using the OPENROWSET function with predicate pushdown to the storage layer, making it both fast and cost-efficient for big data analytics. This alignment with the analytic workload makes it the correct choice.
- ✗
Azure Cosmos DB
Why it's wrong here
Cosmos DB is a globally distributed, multi-model NoSQL database optimized for single-digit millisecond reads and writes on transactional data, but it cannot be directly queried by Synapse Serverless SQL for ad hoc analytical queries over large datasets. Its analytical features require a separate analytical store and Synapse Link, adding complexity and not matching the simplicity of querying files. Therefore it is not ideal as a large-volume analytical store.
- ✗
Azure Cache for Redis
Why it's wrong here
Redis is an in-memory key-value store used primarily to cache frequently accessed data and reduce latency, but it does not persist data to disk by default, offers no SQL schema, and cannot be scanned by Synapse Serverless SQL for analytical purposes. It is meant for sub-millisecond data retrieval within an application, not for storing historical or bulk data for analytics. Hence it is unsuitable for this solution.
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
Redis
Redis is an open-source, in-memory data structure store used as a database, cache, and message broker for high-speed data access.
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
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