Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 355 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 355

Single answer

You are tasked with efficiently converting a large Pandas DataFrame to a Spark DataFrame in a Databricks environment. To ensure optimal performance during this conversion, which underlying technology should you leverage?

  1. A

    Apache Arrow

  2. B

    Delta Lake

  3. C

    Apache Kafka

  4. D

    Apache Parquet

Show answer and explanation

Correct answer: A

Explanation

Apache Arrow is the key technology enabling efficient in-memory conversion between Pandas and Spark DataFrames. It minimizes serialization costs by using a shared columnar memory format, which significantly improves performance during such operations in Databricks.

  • A. Correct.

    Correct. Apache Arrow is an in-memory columnar data format that facilitates efficient data interchange between Pandas and Spark, making conversions faster and reducing serialization overhead.

  • B. Incorrect.

    Incorrect. Delta Lake is a storage layer for managing large-scale data lakes, but it is not directly related to Pandas <-> Spark conversions.

  • C. Incorrect.

    Incorrect. Apache Kafka is used for real-time data streaming, not for efficient data conversions between Pandas and Spark.

  • D. Incorrect.

    Incorrect. Apache Parquet is a columnar storage file format, but it is not used for in-memory conversions between Pandas and Spark.

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