Databricks-Certified-Professional-Data-Engineer Exam Cram, Databricks-Certified-Professional-Data-Engineer Minimum Pass Score

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for data professionals who want to validate their skills and knowledge in building and deploying data engineering solutions using Databricks. Databricks is a unified data analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data projects. Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics such as data ingestion, data processing, data transformation, and data storage using Databricks.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
What could be the expected output of query SELECT COUNT (DISTINCT *) FROM user on this table

A. NULL
B. 2
(Correct)
C. 0
D. 1
E. 2

Answer: B

Explanation:
Explanation
The answer is 2,
Count(DISTINCT *) removes rows with any column with a NULL value

NEW QUESTION # 126
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
D. Both commands will fail. No new variables, tables, or views will be created.
E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

Answer: A

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

NEW QUESTION # 127
You are currently working on a project that requires the use of SQL and Python in a given note-book, what would be your approach

A. Create two separate notebooks, one for SQL and the second for Python
B. Use an All-purpose cluster for python, SQL endpoint for SQL
C. Use job cluster to run python and SQL Endpoint for SQL
D. A single notebook can support multiple languages, use the magic command to switch between the two.

Answer: D

Explanation:
Explanation
The answer is, A single notebook can support multiple languages, use the magic command to switch between the two.
Use %sql and %python magic commands within the same notebook

NEW QUESTION # 128
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?

A. 1. (spark.table("sales")
2. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. )
B. 1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )
C. 1. (spark.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.)
D. 1. (spark.table("sales")
2. .withColumn("avgPrice", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("append")
6. .table("cleanedSales")
7.)
E. 1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )

Answer: D

NEW QUESTION # 129
How to determine if a table is a managed table vs external table?

A. Run SQL command DESCRIBE EXTENDED table_name and check type
B. Run SQL command SHOW TABLES to see the type of the table
C. Run IS_MANAGED('table_name') function
D. All external tables are stored in data lake, managed tables are stored in DELTA lake
E. All managed tables are stored in unity catalog

Answer: A

Explanation:
Explanation
The answer is Run SQL command DESCRIBE EXTENDED table_name and check type Example of External table Graphical user interface, text, application Description automatically generated

Example of managed table
Graphical user interface, text, application, Teams Description automatically generated

NEW QUESTION # 130
......

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Databricks-Certified-Professional-Data-Engineer Exam Cram, Databricks-Certified-Professional-Data-Engineer Minimum Pass Score

Databricks-Certified-Professional-Data-Engineer Minimum Pass Score - Databricks-Certified-Professional-Data-Engineer Valid Test Prep

Databricks Certified Professional Data Engineer Exam Sample Questions (Q125-Q130):

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