DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
A company stores terabytes of customer support chat transcripts in JSON format. The data is rarely modified and needs to be accessed by analysts using SQL queries. The analysts do not want to manage servers or provision throughput. Which Azure service should be used to store and query this data?
⚠ Common exam trap
Many candidates confuse Azure Cosmos DB's SQL API with traditional SQL querying, overlooking the requirement to avoid provisioning throughput, or they assume Azure Table Storage supports SQL queries when it only supports key-value lookups via REST or OData.
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 Blob Storage (with Azure Data Lake Storage Gen2) and query using Azure Synapse Serverless SQL
Azure Blob Storage with Azure Data Lake Storage Gen2 provides a cost-effective, scalable solution for storing large volumes of JSON data in its native format. By using Azure Synapse Serverless SQL, analysts can query this data directly with standard T-SQL without provisioning any infrastructure or managing throughput, meeting the requirement for serverless, on-demand querying of rarely modified data.
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 Blob Storage (with Azure Data Lake Storage Gen2) and query using Azure Synapse Serverless SQL
Why this is correct
Azure Data Lake Storage Gen2 holds the JSON files cheaply, and Synapse serverless SQL pools query them directly with T-SQL, charging per terabyte scanned. This meets the no-server, no-provisioned-throughput constraint while giving analysts SQL access.
- ✗
Azure Cosmos DB
Why it's wrong here
Cosmos DB is a globally distributed operational NoSQL database requiring provisioned or autoscale RU/s, and its SQL API is not the T-SQL analysts expect over static files. It suits low-latency transactional apps; the scenario needs serverless query over stored JSON without throughput provisioning.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a key-value NoSQL store without a SQL query engine, so analysts cannot run SQL statements against the JSON transcripts. It suits cheap high-volume key lookups; the scenario requires serverless SQL querying over files without provisioning throughput.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational engine requiring provisioned compute and a fixed schema, so it cannot query terabytes of raw JSON files without servers or throughput provisioning. It suits structured transactional data; the scenario needs serverless SQL over files in place.
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 Formats: JSON, CSV, Parquet, and Avro
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
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
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