Important Databricks Certified Data Engineer Professional Exam Questions

Answer: D

Explanation:

https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html

Answer: E

Explanation:

This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Databricks Workspace'' section; Databricks Documentation, under ''List secrets'' section.

Answer: B

Explanation:

This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Databricks Tooling'' section; Databricks Documentation, under ''Pull changes from a remote repository'' section.

Answer: E

Explanation:

The code block that should be used to create the date Python variable used in the above code block is:

dbutils.widgets.text(''date'', ''null'') date = dbutils.widgets.get(''date'')

This code block uses the dbutils.widgets API to create and get a text widget named ''date'' that can accept a string value as a parameter1. The default value of the widget is ''null'', which means that if no parameter is passed, the date variable will be ''null''. However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is ''2021-11-01'', the date variable will be ''2021-11-01''. This way, the notebook can use the date variable to load data from the specified path.

The other options are not correct, because:

Option A is incorrect because spark.conf.get(''date'') is not a valid way to get a parameter passed through the Databricks Jobs API.The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.

Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API.The input() function is used to get user input from the standard input stream, not from the API request3.

Option C is incorrect because sys.argv1is not a valid way to get a parameter passed through the Databricks Jobs API.The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.

Option D is incorrect because dbutils.notebooks.getParam(''date'') is not a valid way to get a parameter passed through the Databricks Jobs API.The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.

Answer: D

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 Reference: [Databricks Certified Data Engineer Professional], under ''Databricks Jobs'' section; Databricks Documentation, under ''Get'' section; Databricks Documentation, under ''JobSettings'' section.