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DP-203 Develop data processing Practice Question

Your organization uses Azure Data Lake Storage Gen2 (ADLS Gen2) and wants to transform data using Azure Databricks. The data is stored in Parquet format. You need to read the data into a Spark DataFrame. Which DataFrame reader method should you use?

⚠ Common exam trap

It's easy for candidates to confuse file format reader methods (e.g., using `spark.read.avro()` for Parquet data) due to assuming all binary formats are interchangeable, but each reader method is strictly tied to its specific file format.

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.parquet()

The data is stored in Parquet format, and the Spark DataFrame reader method `spark.read.parquet()` is specifically designed to read Parquet files, which is a columnar storage format optimized for big data processing in Azure Databricks.

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.avro()

    Why it's wrong here

    Avro reader is for Avro format.

  • spark.read.json()

    Why it's wrong here

    JSON reader is for JSON files.

  • spark.read.parquet()

    Why this is correct

    The parquet() method reads Parquet files directly.

  • spark.read.csv()

    Why it's wrong here

    CSV reader is for comma-separated values, not Parquet.

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