Question # 1
| The DevOps team has configured a production workload as a collection of notebooks
scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the
team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without
allowing accidental changes to production code or data?
| | A. Can Manage | | B. Can Edit | | C. No permissions | | D. Can Read | | E. Can Run |
C. No permissions
Explanation:
This is the correct answer because it is the maximum notebook permissions
that can be granted to the user without allowing accidental changes to production code or
data. Notebook permissions are used to control access to notebooks in Databricks
workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can
Run, and Can Read. Can Manage allows full control over the notebook, including editing,
running, deleting, exporting, and changing permissions. Can Edit allows modifying and
running the notebook, but not changing permissions or deleting it. Can Run allows
executing commands in an existing cluster attached to the notebook, but not modifying or
exporting it. Can Read allows viewing the notebook content, but not running or modifying it.
In this case, granting Can Read permission to the user will allow them to review the
production logic in the notebook without allowing them to make any changes to it or run any
commands that may affect production data. Verified References: [Databricks Certified Data
Engineer Professional], under “Databricks Workspace” section; Databricks Documentation,
under “Notebook permissions” section.
Question # 2
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org. Which of the following solutions addresses the situation while emphasizing simplicity?
| A. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
| B. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
| C. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
| D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
|
A. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
References:
• Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick-start.html#sql-views
Question # 3
| Which REST API call can be used to review the notebooks configured to run as tasks in a
multi-task job? | | A. /jobs/runs/list | | B. /jobs/runs/get-output | | C. /jobs/runs/get | | D. /jobs/get | | E. /jobs/list |
D. /jobs/get
Explanation:
This is the correct answer because it is the REST API call that can be used
to review the notebooks configured to run as tasks in a multi-task job. The REST API is an
interface that allows programmatically interacting with Databricks resources, such as
clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET,
POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint
is a GET method that returns information about a job given its job ID. The information
includes the job settings, such as the name, schedule, timeout, retries, email notifications,
and tasks. The tasks are the units of work that a job executes. A task can be a notebook
task, which runs a notebook with specified parameters; a jar task, which runs a JAR
uploaded to DBFS with specified main class and arguments; or a python task, which runs a
Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has
more than one task configured to run in a specific order or in parallel. By using the /jobs/get
endpoint, one can review the notebooks configured to run as tasks in a multi-task job.
Verified References: [Databricks Certified Data Engineer Professional], under “Databricks
Jobs” section; Databricks Documentation, under “Get” section; Databricks Documentation,
under “JobSettings” section.
Question # 4
| What is the first of a Databricks Python notebook when viewed in a text editor? | | A. %python | | B. % Databricks notebook source | | C. -- Databricks notebook source | | D. //Databricks notebook source |
B. % Databricks notebook source
Explanation:
When viewing a Databricks Python notebook in a text editor, the first line
indicates the format and source type of the notebook. The correct option is % Databricks
notebook source, which is a magic command that specifies the start of a Databricks
notebook source file.
Question # 5
| A junior data engineer is working to implement logic for a Lakehouse table named
silver_device_recordings. The source data contains 100 unique fields in a highly nested
JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a
number of fields, and will also be leveraged by the machine learning team to filter on a
handful of relevant fields, in total, 15 fields have been identified that will often be used for
filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested
fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks
that may Impact their decision-making process? | | A. Because Delta Lake uses Parquet for data storage, Dremel encoding information for
nesting can be directly referenced by the Delta transaction log. | | B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added
native support for querying JSON strings means that string types are always most efficient. | | C. Schema inference and evolution on Databricks ensure that inferred types will always
accurately match the data types used by downstream systems. | | D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics
are leveraged for data skipping when executing selective queries. |
D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics
are leveraged for data skipping when executing selective queries.
Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping,
which is based on the statistics collected for each file in a table. For tables with a large
number of columns, Delta Lake by default collects and stores statistics only for the first 32
columns. These statistics include min/max values and null counts, which are used to
optimize query execution by skipping irrelevant data files. When dealing with highly nested
JSON structures, understanding this behavior is crucial for schema design, especially when
determining which fields should be flattened or prioritized in the table structure to leverage
data skipping efficiently for performance optimization.
References:
Databricks
documentation on Delta Lake optimization techniques, including data skipping and
statistics collection (https://docs.databricks.com/delta/optimizations/index.html).
Question # 6
| A developer has successfully configured credential for Databricks Repos and cloned a
remote Git repository. Hey don not have privileges to make changes to the main branch,
which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to
a branch that appeared as a changes were pulled. | | A. Use Repos to merge all differences and make a pull request back to the remote
repository. | | B. Use repos to merge all difference and make a pull request back to the remote
repository | | C. Use Repos to create a new branch commit all changes and push changes to the remote
Git repertory. | | D. Use repos to create a fork of the remote repository commit all changes and make a pull
request on the source repository |
C. Use Repos to create a new branch commit all changes and push changes to the remote
Git repertory.
Explanation:
In Databricks Repos, when a user does not have privileges to make changes
directly to the main branch of a cloned remote Git repository, the recommended approach
is to create a new branch within the Databricks workspace. The developer can then make
changes in this new branch, commit those changes, and push the new branch to the
remote Git repository. This workflow allows for isolated development without affecting the
main branch, enabling the developer to propose changes via a pull request from the new
branch to the main branch in the remote repository. This method adheres to common Git
collaboration workflows, fostering code review and collaboration while ensuring the integrity
of the main branch.
References:
Databricks documentation on using Repos with Git:
https://docs.databricks.com/repos.html
Question # 7
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from
raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or
updated records in the raw_iot table while recomputing the downstream table when a
pipeline update is run?
| | A. Set the skipChangeCommits flag to true on bpm_stats | | B. Set the SkipChangeCommits flag to true raw_lot | | C. Set the pipelines, reset, allowed property to false on bpm_stats | | D. Set the pipelines, reset, allowed property to false on raw_iot |
D. Set the pipelines, reset, allowed property to false on raw_iot
Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in
the raw_iot table while recomputing downstream tables when a pipeline update is run, the
property pipelines.reset.allowed should be set to false. This property prevents the system
from resetting the state of the table, which includes the removal of the history of changes,
during a pipeline update. By keeping this property as false, any changes to the raw_iot
table, including manual deletes or updates, are retained, and recomputation of downstream
tables, such as bpm_stats, can occur with the full history of data changes intact.
References:
Databricks documentation on DLT pipelines:
https://docs.databricks.com/dataengineering/delta-live-tables/delta-live-tables-overview.html
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FAQs of Databricks-Certified-Professional-Data-Engineer Exam
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This
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Spark and Delta Lake.
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Ideal
candidates are data engineers with at least one year of experience in relevant
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Is
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There
are no prerequisites, but Databricks recommends relevant training to ensure
success.
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