Databricks Data Engineer Associate exam dumps

Databricks Data Engineer Associate practice question 9 of 532

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

Databricks Data Engineer Associate Question 9

Select 3

A retail company is modernizing its data infrastructure and is considering adopting a data lakehouse architecture instead of maintaining separate data lake and data warehouse systems. Which of the following are key advantages of a data lakehouse compared to a traditional data warehouse?

  1. A

    It provides support for both structured and unstructured data.

  2. B

    It eliminates the need for ETL pipelines between the data lake and data warehouse.

  3. C

    It offers lower storage costs by only supporting structured data.

  4. D

    It enables real-time analytics and machine learning workloads on the same platform.

  5. E

    It sacrifices ACID transactions for scalability.

Show answer and explanation

Correct answers: A, B, D

Explanation

The data lakehouse architecture bridges the gap between data lakes and traditional data warehouses by combining the advantages of both systems. It supports a wide variety of data types, eliminates the need for separate systems and ETL pipelines, and enables real-time analytics and machine learning workloads. Unlike traditional data warehouses, it also ensures cost efficiency and scalability without compromising on ACID transaction support.

  • A. Correct.

    Correct: A data lakehouse allows the storage and processing of both structured (e.g., tables) and unstructured (e.g., images, videos) data in a single system, unlike traditional data warehouses that primarily focus on structured data.

  • B. Correct.

    Correct: By combining the capabilities of a data lake and data warehouse, a data lakehouse removes the need for complex ETL pipelines to move data between separate systems.

  • C. Incorrect.

    Incorrect: A data lakehouse supports both structured and unstructured data, and its storage costs are generally lower because it leverages cost-efficient cloud storage solutions. However, the claim that it only supports structured data is incorrect.

  • D. Correct.

    Correct: A data lakehouse enables real-time analytics and machine learning workloads on the same storage layer, thanks to its unified architecture and support for modern processing engines like Apache Spark.

  • E. Incorrect.

    Incorrect: A data lakehouse supports ACID transactions through technologies like Delta Lake, ensuring data reliability and consistency while maintaining scalability.

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