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100% Pass Quiz Databricks - Databricks-Certified-Professional-Data-Engineer - Latest Databricks Certified Professional Data Engineer Exam Dumps

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
B. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
C. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
D. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
E. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Answer: E

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 116
How does a Delta Lake differ from a traditional data lake?

A. Delta lake is a caching layer on top of data lake that can provide reliability, security, and performance
B. Delta lake is proprietary software designed by Databricks that can provide reliability, security, and performance
C. Delta lake is an open storage format like parquet with additional capabilities that can provide reliability, security, and performance
D. Delta lake is an open storage format designed to replace flat files with additional capa-bilities that can provide reliability, security, and performance
E. Delta lake is Datawarehouse service on top of data lake that can provide reliability, se-curity, and performance

Answer: C

Explanation:
Explanation
Answer is, Delta lake is an open storage format like parquet with additional capabilities that can provide reliability, security, and performance Delta lake is
* Open source
* Builds up on standard data format
* Optimized for cloud object storage
* Built for scalable metadata handling
Delta lake is not
* Proprietary technology
* Storage format
* Storage medium
* Database service or data warehouse

NEW QUESTION # 117
Your team member is trying to set up a delta pipeline and build a second gold table to the same pipeline with aggregated metrics based on an existing Delta Live table called sales_orders_cleaned but he is facing a problem in starting the pipeline, the pipeline is failing to state it cannot find the table sales_orders_cleaned, you are asked to identify and fix the problem.
1.CREATE LIVE TABLE sales_order_in_chicago
2.AS
3.SELECT order_date, city, sum(price) as sales,
4.FROM sales_orders_cleaned
5.WHERE city = 'Chicago')
6.GROUP BY order_date, city

A. Delta live table can be used in a group by clause
B. The pipeline needs to be deployed so the first table is created before we add a second table
C. Delta live tables pipeline can only have one table
D. Sales_orders_cleaned table is missing schema name LIVE
E. Use STREAMING LIVE instead of LIVE table

Answer: D

Explanation:
Explanation
The answer is, Sales_orders_cleaned table is missing schema name LIVE
Every Delta live table should have schema LIVE
Here is the correct syntax,
1.CREATE LIVE TABLE sales_order_in_chicago
2.AS
3.SELECT order_date, city, sum(price) as sales,
4.FROM LIVE.sales_orders_cleaned
5.WHERE city = 'Chicago')
6.GROUP BY order_date, city

NEW QUESTION # 118
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. date = spark.conf.get("date")
B. input_dict = input()
date= input_dict["date"]
C. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
D. date = dbutils.notebooks.getParam("date")
E. import sys
date = sys.argv[1]

Answer: C

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.argv1 is 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.
References: Widgets, Spark Configuration, input(), sys.argv, Notebooks

NEW QUESTION # 119
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch nameddev-2.3.9is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
B. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
C. Merge all changes back to the main branch in the remote Git repository and clone the repo again
D. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
E. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

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 References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.

NEW QUESTION # 120
......

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