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The Databricks Certified Data Engineer Associate Exam certification exam tests the candidate's knowledge of Databricks, Apache Spark, and data engineering concepts. It covers various topics such as data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Data-Engineer-Associate Exam also evaluates the candidate's ability to design and implement data pipelines using Databricks, as well as their knowledge of best practices for data management and governance.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification program designed to assess an individual's expertise in data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the practical knowledge and skills required to work with big data using Databricks, a cloud-based data analytics platform.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can create a new job from scratch and add both tasks to run concurrently.
B. They can clone the existing task to a new Job and then edit it to run the new notebook.
C. They can create a new task in the existing Job and then add it as a dependency of the original task.
D. They can clone the existing task in the existing Job and update it to run the new notebook.
E. They can create a new task in the existing Job and then add the original task as a dependency of the new task.

Answer: C

Explanation:
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

NEW QUESTION # 82
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Dashboards
B. Jobs
C. Databricks Filesystem
D. Data Explorer
E. Repos

Answer: D

Explanation:
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the
%sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]

NEW QUESTION # 83
A data engineer runs CREATE TABLE gold.revenue AS SELECT * FROM silver.orders in Unity Catalog without specifying a LOCATION.
What is the result?

A. A temporary view is created that is dropped at the end of the session.
B. A managed Delta table is created in the metastore/schema's default storage location.
C. An external table is created and files are written to the workspace root DBFS location.
D. The command fails because a LOCATION clause is mandatory in Unity Catalog.

Answer: B

Explanation:
A CTAS statement without a LOCATION clause creates a Unity Catalog managed table in Delta format, stored in the managed storage location configured for the schema, catalog, or metastore.
Unity Catalog then controls the storage lifecycle, including file cleanup on drop.

NEW QUESTION # 84
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with data in a variety of types at once
B. An ability to work with time-related data in specified intervals
C. An ability to work with an array of tables for procedural automation
D. An ability to work with complex, nested data ingested from JSON files
E. An ability to work with data within certain partitions and windows

Answer: D

Explanation:
Explanation
Array functions in Spark SQL are primarily used for working with arrays and complex, nested data structures, such as those often encountered when ingesting JSON files. These functions allow you to manipulate and query nested arrays and structures within your data, making it easier to extract and work with specific elements or values within complex data formats. While some of the other options (such as option A for working with different data types) are features of Spark SQL or SQL in general, array functions specifically excel at handling complex, nested data structures like those found in JSON files.

NEW QUESTION # 85
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A. A data lakehouse supports ACID-compliant transactions.
B. A data lakehouse enables machine learning and artificial Intelligence workloads.
C. A data lakehouse allows the use of SQL queries to examine data.
D. A data lakehouse provides storage solutions for structured and unstructured data.
E. A data lakehouse stores data in open formats.

Answer: A

NEW QUESTION # 86
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