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Ingest and Transform DatamediumMultiple ChoiceObjective-mapped

DP-700 Ingest and Transform Data Practice Question

You are designing a data lakehouse architecture. Which file format is recommended for optimal performance in Fabric?

⚠ Common exam trap

Candidates often suggest traditional formats like Parquet or CSV, forgetting that Delta Lake is the native, optimized format for Fabric that enables ACID transactions and time travel.

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

Delta Lake

Delta Lake is the gold standard format for Fabric. It is an open-source storage layer that brings ACID transactions to data lakes. It enables high-performance querying and supports time travel, schema evolution, and efficient metadata management. Because Fabric is built on the foundation of Delta Lake, using this format allows you to take advantage of native optimizations, providing superior performance compared to traditional formats like CSV or Parquet alone.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • CSV

    Why it's wrong here

    CSV is a text-based, row-oriented format that is extremely slow for analytical queries. It lacks metadata, does not support ACID transactions, and requires the engine to parse the entire file to extract column information, making it unsuitable for high-performance data engineering tasks in a modern Lakehouse architecture.

  • Delta Lake

    Why this is correct

    Delta Lake is the optimized format for Fabric, providing ACID compliance, time travel, and schema evolution. Its native integration with the Spark engine allows for fast data reads and writes, making it the recommended choice for building scalable, high-performance data platforms in the Microsoft Fabric ecosystem.

  • JSON

    Why it's wrong here

    JSON is a semi-structured format that is inefficient for large-scale analytical processing. It consumes significant storage space and requires intensive parsing during query execution. It is generally used for data interchange between systems but should be converted into a structured format like Delta for analytical storage.

  • Avro

    Why it's wrong here

    Avro is a row-oriented format primarily used for data serialization in streaming pipelines. While efficient for writing, it is not optimized for analytical read queries. For data that will be stored in a Lakehouse for long-term analysis, column-oriented formats like Delta are significantly faster for querying large datasets.

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Last reviewed September 2026 · checked against the official Microsoft exam blueprint

This DP-700 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-700 exam.