📘 Free CERTIFIED-DATA-ENGINEER-ASSOCIATE Sample Questions
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.
Which of the following describes how a data lakehouse could alleviate this issue?
A
Both teams would autoscale their work as data size evolves
B
Both teams would use the same source of truth for their work
C
Both teams would reorganize to report to the same department
D
Both teams would be able to collaborate on projects in real-time
Correct Answer:
B. Both teams would use the same source of truth for their work
Explanation:
A data lakehouse is designed to integrate the benefits of data lakes and data warehouses by providing a single,
unified platform for both analytical and data engineering workflows. By combining structured and unstructured data
in one place, a lakehouse enables both data engineers and data analysts to access and work from the same source of
truth. This eliminates data silos, reducing discrepancies in reports that can arise from each team working with
different datasets or versions of data.
Which of the following describes a scenario in which a data team will want to utilize cluster pools?
A
An automated report needs to be refreshed as quickly as possible.
B
An automated report needs to be made reproducible.
C
An automated report needs to be tested to identify errors
D
An automated report needs to be version-controlled across multiple collaborators.
Correct Answer:
A. An automated report needs to be refreshed as quickly as possible.
Explanation:
Cluster pools are used in Databricks to reduce the time needed to create and scale clusters by maintaining a set of
pre-configured, ready-to-use instances. When an automated report needs to be refreshed quickly, cluster pools help
by minimizing cluster startup time, allowing the report generation process to start almost immediately. This is
especially beneficial in scenarios where low latency is required to ensure data is updated in near real-time.
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
A
Worker node
B
JDBC data source
C
Databricks web application
D
Databricks Filesystem
Correct Answer:
C. Databricks web application
Explanation:
In the classic Databricks architecture, the control plane includes components like the Databricks web application, the
Databricks REST API, and the Databricks Workspace. These components are responsible for managing and controlling
the Databricks environment, including cluster provisioning, notebook management, access control, and job
scheduling. The other options, such as worker nodes, JDBC data sources, Databricks Filesystem (DBFS), and driver
nodes, are typically part of the data plane or the execution environment, which is separate from the control plane.
Worker nodes are responsible for executing tasks and computations, JDBC data sources are used to connect to
external databases, DBFS is a distributed file system for data storage, and driver nodes are responsible for
coordinating the execution of Spark jobs.
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
A
The ability to manipulate the same data using a variety of languages
B
The ability to collaborate in real time on a single notebook
C
The ability to set up alerts for query failures
D
The ability to support batch and streaming workloads
Correct Answer:
D. The ability to support batch and streaming workloads
Explanation:
Delta Lake is a key component of the Databricks Lakehouse Platform that provides several benefits, and one of the
most significant benefits is its ability to support both batch and streaming workloads seamlessly. Delta Lake allows
you to process and analyze data in real-time (streaming) as well as in batch, making it a versatile choice for various
data processing needs. While the other options may be benefits or capabilities of Databricks or the Lakehouse
Platform in general, they are not specifically associated with Delta Lake.
Which of the following describes the storage organization of a Delta table?
A
Delta tables are stored in a single file that contains data, history, metadata, and other attributes
B
Delta tables store their data in a single file and all metadata in a collection of files in a separate location
C
Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
D
. Delta tables are stored in a collection of files that contain only the data stored within the table.
Correct Answer:
C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
Explanation:
Delta tables store data in a structured manner using Parquet files, and they also maintain metadata and transaction
logs in separate directories. This organization allows for versioning, transactional capabilities, and metadata tracking
in Delta Lake. Thank you for pointing out the error, and I appreciate your understanding.
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the
existing Delta table my_table and save the updated table?
A
SELECT * FROM my_table WHERE age > 25;
B
UPDATE my_table WHERE age > 25;
C
DELETE FROM my_table WHERE age > 25;
D
UPDATE my_table WHERE age <= 25;
Correct Answer:
C. DELETE FROM my_table WHERE age > 25;
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use
Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to
time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
A
The VACUUM command was run on the table
B
The DELETE HISTORY command was run on the table
C
The TIME TRAVEL command was run on the table
D
The HISTORY command was run on the table
Correct Answer:
A. The VACUUM command was run on the table
Explanation:
The VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer
needed for time travel or query purposes. When you run VACUUM with certain retention settings, it can delete older
data files, which might include versions of data that are older than the specified retention period. If the data
engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it's
likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup.
Which of the following Git operations must be performed outside of Databricks Repos?
A
Commit
B
Pull
C
Push
D
Merge
Correct Answer:
D. Merge
Explanation:
Cloning a repository creates a local copy of the repository on your machine and must be done using a local Git client
or command line. Once the repository is cloned, you can work with it in Databricks Repos, but the initial clone
operation itself is outside the Databricks interface. Other operations like commit, pull, push, and merge can be
managed within Databricks Repos or through other Git tools.
Which of the following data lakehouse features results in improved data quality over a traditional data lake?
A
A data lakehouse provides storage solutions for structured and unstructured data.
B
A data lakehouse supports ACID-compliant transactions.
C
A data lakehouse allows the use of SQL queries to examine data
D
A data lakehouse stores data in open formats.
Correct Answer:
B. A data lakehouse supports ACID-compliant transactions.
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their
project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
A
Databricks Repos automatically saves development progress
B
Databricks Repos supports the use of multiple branches
C
Databricks Repos allows users to revert to previous versions of a notebook
Correct Answer:
B. Databricks Repos supports the use of multiple branches
Explanation:
While both Databricks Notebooks versioning and Databricks Repos allow for version control of code, Databricks
Repos provides the additional benefit of supporting the use of multiple branches. This allows for multiple versions of
a notebook or project to be developed in parallel, facilitating collaboration among team members and simplifying
the process of merging changes into a single main branch.
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