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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam that measures an individual's knowledge and skills in building and maintaining data pipelines using Databricks. Databricks-Certified-Data-Engineer-Associate Exam is designed to assess the candidate's ability to work with different data sources and formats, handle data processing and transformations, and build and maintain scalable data pipelines.

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After successful competition of the Databricks Databricks-Certified-Data-Engineer-Associate certification, the certified candidates can put their career on the right track and achieve their professional career objectives in a short time period. For the recognition of skills and knowledge, more career opportunities, professional development, and higher salary potential, the Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate) certification exam is the proven way to achieve these tasks quickly.

GAQM Databricks-Certified-Data-Engineer-Associate is a certification exam designed to test the skills and knowledge of individuals in the field of data engineering. Databricks-Certified-Data-Engineer-Associate exam is created by GAQM, a leading global provider of certification programs for professionals in various industries. Databricks Certified Data Engineer Associate Exam certification is intended for those who work with Databricks and want to demonstrate their expertise in designing, building, and maintaining data pipelines using this technology.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q206-Q211):

NEW QUESTION # 206
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

A. dbfs:/user/hive/database/customer360
B. dbfs:/user/hive/warehouse
C. dbfs:/user/hive/customer360
D. More information is needed to determine the correct response

Answer: B

Explanation:
dbfs:/user/hive/warehouse Thereby showing "dbfs:/user/hive/warehouse/customer360.db The location of the customer360 database depends on the value of the spark.sql.warehouse.dir configuration property, which specifies the default location for managed databases and tables. If the property is not set, the default value is dbfs:/user/hive/warehouse. Therefore, the customer360 database will be located in dbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such as dbfs:/user/hive/database, then the customer360 database will be located in dbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.
Option B is not correct, as dbfs:/user/hive/warehouse is the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with .db to the directory path, such as dbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.
References:
* Databases and Tables
* [Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 207
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to fail before the new task begins
B. When another task has the same dependency libraries as the new task
C. When another task needs to be replaced by the new task
D. When another task needs to use as little compute resources as possible
E. When another task needs to successfully complete before the new task begins

Answer: E

Explanation:
A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other's outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:
* Task A: Ingest data from a source using Auto Loader
* Task B: Transform the data using Spark SQL
* Task C: Write the data to a Delta Lake table
* Task D: Analyze the data using Spark ML
* Task E: Visualize the data using Databricks SQL
In this case, the data engineer can set the dependencies of each task as follows:
* Task A: No dependencies
* Task B: Depends on Task A
* Task C: Depends on Task B
* Task D: Depends on Task C
* Task E: Depends on Task D
This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.
The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:
* Whether the task needs to be replaced by another task
* Whether the task needs to fail before another task begins
* Whether the task has the same dependency libraries as another task
* Whether the task needs to use as little compute resources as possible References: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.

NEW QUESTION # 208
Which Databricks Asset Bundle format is valid?

A. {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}
B. resources {
jobs {
name = " hello-job "
tasks {
task_key = " hello-task "
existing_cluster_id = " 1234-567890-abcde123 "
notebook_task {
notebook_path = " ./hello.py "
}
}
}
}
C. resources:
jobs:
hello-job:
name: hello-job
tasks:
- task_key: hello-task
existing_cluster_id: 1234-567890-abcde123
notebook_task:
notebook_path: ./hello.py
D. configuration = {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}

Answer: C

Explanation:
Databricks Asset Bundles use YAML configuration, and Databricks documentation specifies that bundle configuration is expressed in a root databricks.yml file. The resources mapping is used to define supported objects such as jobs , and job definitions include keys like name and tasks. Tasks in a job are defined under the tasks key, and a notebook task is represented with notebook_task and notebook_path. Option A is the only answer written in the correct general YAML bundle structure and matches the documented style of defining bundle resources. Options B and C use JSON- and Python-style syntax rather than bundle YAML. Option D uses Terraform/HCL-style syntax, which is not the Databricks Asset Bundle format. While a full production bundle often includes more top-level settings such as bundle, targets, or workspace, the question asks which format is valid, and only option A matches the documented Databricks Asset Bundles configuration model.
=========

NEW QUESTION # 209
Which file format is used for storing Delta Lake Table?

A. SV
B. JSON
C. Delta
D. Parquet

Answer: D

NEW QUESTION # 210
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables. Which change will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in SQL.
B. The pipeline can have different notebook sources in SQL & Python.
C. The pipeline will need to be written entirely in Python.
D. The pipeline will need to use a batch source in place of a streaming source.

Answer: B

Explanation:
When migrating to Delta Live Tables (DLT), you can use both SQL and Python in the same pipeline. DLT supports multi-language notebooks, allowing data engineers to continue using Python for certain transformations and data analysts to use SQL for their specific requirements.

NEW QUESTION # 211
......

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