Upgrade Databricks-Certified-Professional-Data-Engineer Dumps, Databricks-Certified-Professional-Data-Engineer Exam Tests

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
Below sample input data contains two columns, one cartId also known as session id, and the second column is called items, every time a customer makes a change to the cart this is stored as an array in the table, the Marketing team asked you to create a unique list of item's that were ever added to the cart by each customer, fill in blanks by choosing the appropriate array function so the query produces below expected result as shown below.
Schema: cartId INT, items Array<INT>
Sample Data

1.SELECT cartId, ___ (___(items)) as items
2.FROM carts GROUP BY cartId
Expected result:
cartId items
1 [1,100,200,300,250]

A. FLATTEN, COLLECT_UNION
B. ARRAY_DISTINCT, ARRAY_UNION
C. ARRAY_UNION, COLLECT_SET
D. ARRAY_UNION, ARRAY_DISTINT
E. ARRAY_UNION, FLATTEN

Answer: C

Explanation:
Explanation
COLLECT SET is a kind of aggregate function that combines a column value from all rows into a unique list ARRAY_UNION combines and removes any duplicates, Graphical user interface, application Description automatically generated with medium confidence

NEW QUESTION # 76
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

A. Nothing will occur until a COMMIT command is executed.
B. Data will be marked as deleted but still recoverable with Time Travel.
C. The table will be removed from the catalog but the data will remain in storage.
D. The table will be removed from the catalog and the data will be deleted.
E. An error will occur because Delta Lake prevents the deletion of production data.

Answer: D

Explanation:
Explanation
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table

NEW QUESTION # 77
Which of the following results in the creation of an external table?

A. CREATE TABLE transactions (id int, desc string)
B. CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION EX-TERNAL
C. CREATE EXTERNAL TABLE transactions (id int, desc string)
D. CREATE TABLE transactions (id int, desc string) TYPE EXTERNAL
E. CREATE TABLE transactions (id int, desc string) LOCATION '/mnt/delta/transactions'

Answer: E

Explanation:
Explanation
Answer is CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION
'/mnt/delta/transactions'
Anytime a table is created using Location it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/"

NEW QUESTION # 78
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
B. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
C. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
D. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
E. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

Answer: A

Explanation:
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will always reflect the current state of the source table, while the bronze table will keep the history of all changes. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 79
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
B. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
C. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
D. The activity details table already exists; CHECK constraints can only be added during initial table creation.
E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

Answer: E

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 80
......

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Upgrade Databricks-Certified-Professional-Data-Engineer Dumps, Databricks-Certified-Professional-Data-Engineer Exam Tests

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q75-Q80):

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