Databricks Certified Data Engineer Associate Exam Questions
Certified Data Engineer Associate (Page 4 )

Updated On: 23-Apr-2026

What can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

  1. Delta Lake
  2. Data lake
  3. Data warehouse
  4. Data lakehouse

Answer(s): D

Explanation:

A data lakehouse is a unified architecture that combines the best features of data lakes and data warehouses.
It simplifies and unifies siloed data architectures by allowing both structured and unstructured data to coexist in the same system, supporting a wide variety of use cases, such as analytics, machine learning, and business intelligence.
Data lakes are good for storing large amounts of raw data but lack the structure and performance needed for high-performance analytics.
Data warehouses are designed for structured data and analytics but aren't optimized for large volumes of unstructured or semi-structured data.
Delta Lake is a storage layer that brings reliability to data lakes, but a lakehouse is the broader architecture that unifies these approaches.



How can Git operations be performed outside of Databricks Repos?

  1. Commit
  2. Pull
  3. Merge
  4. Clone

Answer(s): D

Explanation:

In Databricks Repos, you can perform Git operations such as commit, pull, and merge directly within the Databricks interface. However, cloning a repository is an operation that must be performed outside of Databricks Repos using a Git client or command-line interface. After cloning the repository to a local machine, it can then be imported into Databricks as a Repo.



A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.

How could Data lakehouse alleviate this issue?

  1. Both teams would respond more quickly to ad-hoc requests
  2. Both teams would use the same source of truth for their work
  3. Both teams would reorganize to report to the same department
  4. Both teams would be able to collaborate on projects in real-time

Answer(s): B

Explanation:

A data lakehouse combines the benefits of data lakes and data warehouses, providing a unified architecture for storing and processing data. By using a data lakehouse, both the data engineering and data analysis teams can work from the same source of truth, ensuring consistency in data processing, analysis, and reporting.
This eliminates discrepancies caused by using separate data silos or architectures, which is the primary issue the leader is concerned about.



What is stored in a Databricks customer's cloud account?

  1. Databricks web application
  2. Cluster management metadata
  3. Notebooks
  4. Data

Answer(s): D

Explanation:

In the Databricks architecture, the data is stored in the Databricks customer's cloud account, such as AWS S3, Azure Data Lake Storage (ADLS), or Google Cloud Storage (GCS). This allows customers to maintain ownership and control of their data, ensuring that it stays within their cloud environment while leveraging Databricks for processing and analytics.



Which component provides an optimized storage layer that brings ACID transactions, schema enforcement, and data reliability to cloud object storage?

  1. Delta Lake
  2. Unity Catalog
  3. Cloud File Storage
  4. Data lake

Answer(s): A

Explanation:

Delta Lake is the optimized storage layer that adds ACID transactions, schema enforcement, and reliability on top of cloud object storage, making it ideal for managing large-scale data workloads.



A data engineer is attempting to write a Python function and a SQL SELECT statement within the same code cell in a Databricks notebook, but the cell fails to execute. Which of the following statements explains why this error occurs?

  1. Databricks supports language interoperability in the same cell but only between Scala and SQL.
  2. Databricks supports multiple languages but only one per notebook.
  3. Databricks supports one language per cell.
  4. Databricks supports language interoperability but only if a special character is used.

Answer(s): C

Explanation:

In Databricks, each notebook cell supports only one language. While a notebook can contain multiple languages (Python, SQL, Scala, R), you must define the language per cell, so mixing Python and SQL directly in the same cell causes the error.



Which compute option should be chosen in a scenario where small-scale ad-hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

  1. All-purpose Cluster
  2. Job Cluster
  3. Serverless Compute
  4. SQL Warehouse

Answer(s): C

Explanation:

Serverless Compute is ideal for small-scale, frequent, ad-hoc Python scripts because it provisions automatically, scales quickly, and terminates when queries finish, minimizing operational overhead and costs.



How does Databricks Connect simplify the development process for a data engineer?

  1. By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using a specific IDE that is required by Databricks
  2. By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code only through Databricks' own web interface
  3. By allowing direct execution of Spark jobs from the local machine without needing a network connection
  4. By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using their preferred IDE

Answer(s): D

Explanation:

Databricks Connect lets engineers use their preferred local IDE while connecting to Databricks clusters remotely. It mimics the Databricks runtime locally, enabling seamless development, testing, and debugging while executing workloads on the Databricks cluster.



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