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Databricks Certified Professional Data Engineer Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
Kevin is the owner of both the sales table and regional_sales_vw view which uses the sales table as the underlying source for the data, and Kevin is looking to grant select privilege on the view regional_sales_vw to one of newly joined team members Steven. Which of the following is a true statement?

A. Kevin can not grant access to Steven since he does have workspace admin privilege
B. Kevin can not grant access to Steven since he does not have security admin privilege
C. Kevin although is the owner but does not have ALL PRIVILEGES permission
D. Kevin can grant access to the view, because he is the owner of the view and the under-lying table
E. Steve will also require SELECT access on the underlying table

Answer: D

Explanation:
Explanation
The answer is, Kevin can grant access to the view, because he is the owner of the view and the un-derlying table, Ownership determines whether or not you can grant privileges on derived objects to other users, a user who creates a schema, table, view, or function becomes its owner. The owner is granted all privileges and can grant privileges to other users

NEW QUESTION # 75
Data engineering team is required to share the data with Data science team and both the teams are using different workspaces in the same organizationwhich of the following techniques can be used to simplify sharing data across?
*Please note the question is asking how data is shared within an organization across multiple workspaces.

A. Unity Catalog
B. Use a single storage location
C. DELTA lake
D. Data Sharing
E. DELTA LIVE Pipelines

Answer: A

Explanation:
Explanation
The answer is the Unity catalog.
Diagram Description automatically generated

Unity Catalog works at the Account level, it has the ability to create a meta store and attach that meta store to many workspaces see the below diagram to understand how Unity Catalog Works, as you can see a metastore can now be shared with both workspaces using Unity Catalog, prior to Unity Catalog the options was to use single cloud object storage manually mount in the second databricks workspace, and you can see here Unity Catalog really simplifies that.
Diagram Description automatically generated with medium confidence

sorry for the inconvenience watermark was added because other people on Udemy are copying my questions and images.
duct features
https://databricks.com/product/unity-catalog

NEW QUESTION # 76
A table customerLocations exists with the following schema:
1. id STRING,
2. date STRING,
3. city STRING,
4. country STRING
A senior data engineer wants to create a new table from this table using the following command:
1. CREATE TABLE customersPerCountry AS
2. SELECT country,
3. COUNT(*) AS customers
4. FROM customerLocations
5. GROUP BY country;
A junior data engineer asks why the schema is not being declared for the new table. Which of the following
responses explains why declaring the schema is not necessary?

A. CREATE TABLE AS SELECT statements adopt schema details from the source table and query
B. CREATE TABLE AS SELECT statements infer the schema by scanning the data
C. CREATE TABLE AS SELECT statements result in tables where schemas are optional
D. CREATE TABLE AS SELECT statements assign all columns the type STRING
E. CREATE TABLE AS SELECT statements result in tables that do not support schemas

Answer: A

NEW QUESTION # 77
Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?

A. MERGE INTO table_name
B. UPDATE table_name
C. INSERT INTO OVERWRITE table_name
D. INSERT IF EXISTS table_name
E. COPY INTO table_name

Answer: A

Explanation:
Explanation
here is the additional documentation for your review.
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-merge-into.html
1.MERGE INTO target_table_name [target_alias]
2. USING source_table_reference [source_alias]
3. ON merge_condition
4. [ WHEN MATCHED [ AND condition ] THEN matched_action ] [...]
5. [ WHEN NOT MATCHED [ AND condition ] THEN not_matched_action ] [...]
6.
7.matched_action
8. { DELETE |
9. UPDATE SET * |
10. UPDATE SET { column1 = value1 } [, ...] }
11.
12.not_matched_action
13. { INSERT * |
14. INSERT (column1 [, ...] ) VALUES (value1 [, ...])

NEW QUESTION # 78
Which of the following SQL statements can replace python variables in Databricks SQL code, when the notebook is set in SQL mode?
1.%python
2.table_name = "sales"
3.schema_name = "bronze"
4.
5.%sql
6.SELECT * FROM ____________________

A. SELECT * FROM ${schema_name}.${table_name}
B. SELECT * FROM {schem_name.table_name}
C. SELECT * FROM schema_name.table_name
D. SELECT * FROM f{schema_name.table_name}

Answer: A

Explanation:
Explanation
The answer is, SELECT * FROM ${schema_name}.${table_name}
%python
table_name = "sales"
schema_name = "bronze"
%sql
SELECT * FROM ${schema_name}.${table_name}
${python variable} - Python variables in Databricks SQL code

NEW QUESTION # 79
......

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