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Databricks Certified Professional Data Engineer Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which of the following is a Continuous Probability Distributions?

A. Negative binomial distribution
B. Binomial probability distribution
C. Poisson probability distribution
D. Normal probability distribution

Answer: D

NEW QUESTION # 18
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

Answer: D

Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
References:
* Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

NEW QUESTION # 19
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. input_dict = input()
date= input_dict["date"]
B. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
C. date = dbutils.notebooks.getParam("date")
D. date = spark.conf.get("date")
E. import sys
date = sys.argv[1]

Answer: C

Explanation:
Explanation
This is the correct way to get a parameter passed to a notebook by the Databricks Jobs API. The dbutils.notebooks.getParam method returns the value of a parameter passed to a notebook as a string. If no parameter with that name is passed, it returns None by default. You can also specify a default value as a second argument. Verified References: Databricks Certified Data Engineer Professional, under "Databricks Tooling" section; Databricks Documentation, under "Pass parameters to a notebook" section.

NEW QUESTION # 20
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A. DESCRIBE DETAIL dev.pii test
B. SHOW TABLES dev
C. SHOW TBLPROPERTIES dev.pii test
D. DESCRIBE EXTENDED dev.pii test
E. DESCRIBE HISTORY dev.pii test

Answer: D

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.

NEW QUESTION # 21
A dataset has been defined using Delta Live Tables and includes an expectations clause:
1. CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01')
What is the expected behaviour when a batch of data containing data that violates these constraints is
processed?

A. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table
B. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset
C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log
D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log
E. Records that violate the expectation cause the job to fail

Answer: D

NEW QUESTION # 22
......

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q17-Q22):

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