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Databricks Certified Data Engineer Associate Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
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. configuration = {
" resources " : {
" jobs " : {
" name " : " hello-job " ,
" tasks " : {
" task_key " : " hello-task " ,
" existing_cluster_id " : " 1234-567890-abcde123 " ,
" notebook_task " : {
" notebook_path " : " ./hello.py "
}
}
}
}
}
D. resources:
jobs:
hello-job:
name: hello-job
tasks:
- task_key: hello-task
existing_cluster_id: 1234-567890-abcde123
notebook_task:
notebook_path: ./hello.py

Answer: D

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 # 61
A data engineer is migrating pipeline tasks to reduce operational toil. The workspace used Unity Catalog and is in a region that supports serverless. The engineer wants Databricks to auto-select instance types, manage scaling, apply Photon, and handle runtime upgrades automatically for job runs.
How should the data engineer meet this requirement while adhering to Databricks constraints?

A. Use a Pro SQL warehouse and schedule Python notebook tasks to execute as pipeline steps.
B. Create a job with a single-task job cluster and manually set instance families and min/max workers.
C. Run the job on a serverless compute for workflows configuration, ensuring Unity Catalog is enabled and regional support is available.
D. Use an all-purpose cluster with cluster policies to enforce standard sizes and enable autoscaling.

Answer: C

Explanation:
Serverless compute for workflows allows Databricks to automatically manage instance selection, scaling, Photon enablement, and runtime upgrades for job runs, provided Unity Catalog is enabled and the workspace region supports serverless.

NEW QUESTION # 62
What is stored in a Databricks customer's cloud account?

A. Cluster management metadata
B. Data
C. Notebooks
D. Databricks web application

Answer: B

Explanation:
In a Databricks customer's cloud account, the primary elements stored include:
Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.
Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.
Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks.
Reference:
Databricks documentation: Data in Databricks

NEW QUESTION # 63
Which TWO items are characteristics of the Gold Layer?
Choose 2 answers

A. Normalised
B. Historical lineage
C. Read-optimized
D. Raw Data
E. De-normalised

Answer: C,E

NEW QUESTION # 64
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: B

Explanation:
Explanation
https://www.w3schools.com/python/python_functions.asp

NEW QUESTION # 65
......

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