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Databricks-Certified-Professional-Data-Engineer Test Braindumps: Databricks Certified Professional Data Engineer Exam & Databricks-Certified-Professional-Data-Engineer Exam Guide & Databricks-Certified-Professional-Data-Engineer Study Guide

Databricks-Certified-Professional-Data-Engineer Test Braindumps: Databricks Certified Professional Data Engineer Exam & Databricks-Certified-Professional-Data-Engineer Exam Guide & Databricks-Certified-Professional-Data-Engineer Study Guide

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
What is the output of below function when executed with input parameters 1, 3 :
1.def check_input(x,y):
2. if x < y:
3. x= x+1
4. if x>y:
5. x= x+1
6. if x <y:
7. x = x+1
8. return x

  • A. 0
  • B. 1
  • C. 2
  • D. 3
  • E. 4

Answer: B


NEW QUESTION # 89
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.
address THEN
UPDATE SET current = false, end_date = staged_updates.effective_date
WHEN NOT MATCHED THEN
INSERT (customer_id, address, current, effective_date, end_date)
VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
* The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

  • A. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
  • B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
  • C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Answer: C

Explanation:
The providedMERGEstatement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in thecustomerstable is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References:
Databricks documentation on implementing SCDs using Delta Lake and theMERGEstatement (https://docs.
databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).


NEW QUESTION # 90
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

  • A. C++
  • B. Regex
  • C. Julia
  • D. Scala Datasets
  • E. pyspsark.ml.feature

Answer: B

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References:
* https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions
* https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs
* https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
* https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html


NEW QUESTION # 91
A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?

  • A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
  • B. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
  • C. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
  • D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
  • E. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Answer: E


NEW QUESTION # 92
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

  • A. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions
  • B. Appends the data to the current version, you can time travel to previous versions
  • C. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.
  • D. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
  • E. By default, overwrites the data and schema, you cannot perform time travel

Answer: A

Explanation:
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customer_sales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customer_id = c.customer_id
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM table_name as of [version number]
with customer_sales example
SELECT * FROM customer_sales as of 1 -- previous version
SELECT * FROM customer_sales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY table_name Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.


NEW QUESTION # 93
......

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