DP-900 Describe core data concepts Practice Question
Which TWO Azure services can be used to perform data transformation in a data pipeline? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse storage or ingestion services (like Blob Storage or Event Hubs) with compute services that actually execute transformation logic, leading them to select options that only move or store data.
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 Databricks
Azure Databricks (C) is correct because it is an Apache Spark-based analytics platform whose notebooks and jobs can run transformations such as filtering, aggregating, joining, and reshaping data at scale within a pipeline. Azure Data Factory (E) is correct because its Mapping Data Flows and Data Flow activities provide a visual, code-free way to transform data (derived columns, joins, aggregations, pivots) as part of a pipeline, and it can also orchestrate transformation jobs on compute such as Databricks or HDInsight. Azure Blob Storage (A) is only a storage service for holding data, not a transformation engine. Azure SQL Database (B) is a relational database that can run T-SQL queries, but it is not the designated data-transformation service in a pipeline context here. Azure Event Hubs (D) is a big-data streaming ingestion and event-brokering service, not a transformation service.
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
Why it's wrong here
Azure Blob Storage is an object storage service for unstructured data such as text, binaries, and flat files. It provides durable, tiered storage and lifecycle policies, but it has no built-in compute engine to apply transformations to the data it holds. To transform blobs, you must attach a separate service such as Azure Databricks or Azure Data Factory to read, process, and write results back.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational database engine focused on transactional (OLTP) workloads. While you can write T-SQL stored procedures or views to manipulate data within the database, it is not designed as a broad data transformation pipeline for heterogenous sources. Its scaling model is based on database compute and storage, not on parallel, distributed data-flow execution across file stores and data lakes.
- ✓
Azure Databricks
Why this is correct
Azure Databricks is a managed Apache Spark platform that provides interactive workspaces and clusters for large-scale data processing. It lets you transform data using Python, Scala, SQL, or R, and supports both batch and streaming workloads. This makes it a primary Azure service for complex ETL and ELT transformations, especially when combining structured and unstructured data across data lakes.
- ✗
Azure Event Hubs
Why it's wrong here
Azure Event Hubs is a real-time data ingestion and event streaming service that can handle millions of events per second. It acts as a buffer or broker for telemetry and streaming data, but it does not include a transformation engine. Downstream consumers—such as Azure Stream Analytics, Databricks, or Functions—must perform any enrichment or reshaping of the event data.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory is a dedicated cloud ETL/ELT service that lets you create pipelines to move and transform data. Its mapping data flows use serverless Spark under the hood to perform code-free transformations, while also supporting expressive code through Azure Integration Runtime. It can orchestrate transformations across hundreds of on-premises and cloud data sources, making it a core answer for Azure transformation services.
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 Ingestion Patterns: Batch vs Streaming
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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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