Question 711 of 760
DP-203 Develop data processing Practice Question
You need to transform data in Azure Databricks using Apache Spark. The data is stored in Delta Lake format in Azure Data Lake Storage Gen2. Which method should you use to read the data into a Spark DataFrame?
⚠ Common exam trap
A common mix-up: candidates assume Delta Lake files are just Parquet files and use `spark.read.parquet()`, missing the critical role of the Delta transaction log for consistency and ACID compliance.
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
✓
spark.read.format('delta').load('abfss://container@storage.dfs.core.windows.net/path')
The data is stored in Delta Lake format, which requires using the 'delta' format reader in Spark to properly read the transaction log and schema. The `spark.read.format('delta').load()` method is the standard way to read Delta tables, leveraging the Delta Lake protocol for ACID transactions and time travel capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
spark.read.parquet('abfss://container@storage.dfs.core.windows.net/path')
Why it's wrong here
Parquet reads only the data files, missing Delta features.
- ✓
spark.read.format('delta').load('abfss://container@storage.dfs.core.windows.net/path')
Why this is correct
Delta format correctly reads the table including transaction log.
- ✗
spark.read.csv('abfss://container@storage.dfs.core.windows.net/path')
Why it's wrong here
CSV format will not interpret Delta Lake correctly.
- ✗
spark.read.json('abfss://container@storage.dfs.core.windows.net/path')
Why it's wrong here
JSON is not the correct format for Delta Lake.
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Last reviewed: Jun 24, 2026
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