DP-900 Describe core data concepts Practice Question
Your company runs a global e-commerce platform that generates over 5 TB of clickstream data daily. The data is currently stored as raw CSV files in Azure Blob Storage. The data engineering team needs to transform this data into a star schema for business intelligence reporting. They want to use a serverless, code-first approach where they can write Python or SQL transformations. The transformed data should be stored in a format that optimizes query performance for Power BI. You also need to ensure that the solution can handle variable data volumes without manual scaling. Which Azure service should you use for the transformation?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Data Factory as a transformation service, but it is actually an orchestration tool that requires a separate compute engine (like Databricks or Synapse) to perform the actual data transformations.
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 is the correct choice because it provides a serverless, code-first environment where data engineers can write Python or SQL transformations using Apache Spark. It can handle variable data volumes without manual scaling, and it can output transformed data in optimized formats like Parquet, which significantly improves query performance for Power BI. This aligns perfectly with the requirement for a serverless, code-first approach and star schema transformation.
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 Stream Analytics
Why it's wrong here
Azure Stream Analytics is a real-time event processing engine that consumes data from sources like Event Hubs or IoT Hub and outputs to sinks, using a SQL-like query language to analyze streaming data. It is not suited for batch processing of static CSV files stored in data lakes; it lacks the general-purpose code execution (Python/Scala) and interactive notebook environment that Databricks provides. Its transient, time-windowed processing model doesn't align with large-scale batch ETL transformations.
- ✓
Azure Databricks
Why this is correct
Azure Databricks is an Apache Spark-based analytics platform that offers collaborative notebooks, autoscaling clusters, and supports Python, Scala, SQL, and R. It allows data engineers to read CSV files from Azure Data Lake Storage or Blob storage, perform complex transformations using DataFrames or SQL, and write results back — exactly the code-first, scalable batch processing required for a global e-commerce workload. Its serverless option removes infrastructure management while providing the flexibility to write custom transformation logic in Python, making it the ideal choice.
- ✗
Azure Synapse Serverless SQL
Why it's wrong here
Synapse Serverless SQL is a query service that runs T-SQL queries directly over files in a data lake, enabling point-in-time queries and lightweight transformation via SELECT statements. However, it is not designed for performing complex, multi-step data transformations with custom code (like Python UDFs) or for building maintainable transformation pipelines; it's primarily for ad-hoc querying and data virtualization. For heavy ETL/ELT workloads requiring iterative development, Databricks offers a more robust and expressive environment.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based ETL and data integration service that orchestrates data movement and transformation at scale using pipelines and activities. While it does allow you to run notebooks or Spark jobs, the primary authoring experience is visual (UI) with configuration-driven activities rather than a code-first interactive environment where you write Python or SQL directly against data. It is best for scheduling and orchestrating copy activities and calling external compute, but it is not the right tool for writing custom, complex transformation logic in a notebook-centric, code-first manner.
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
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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