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Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to validate their skills and expertise in using Databricks. Databricks Certified Professional Data Engineer Exam certification demonstrates to employers and clients that the candidate has the knowledge and skills required to design and implement data solutions using Databricks. It also helps data engineers differentiate themselves in a competitive job market and provides opportunities for career advancement.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. The table reference in the metastore is updated and all data files are moved.
B. All related files and metadata are dropped and recreated in a single ACID transaction.
C. A new Delta transaction log Is created for the renamed table.
D. The table reference in the metastore is updated and no data is changed.
E. The table name change is recorded in the Delta transaction log.

Answer: D

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

NEW QUESTION # 152
What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

A. 1/6
B. 2/6
C. 2/3
D. 1/3

Answer: C

NEW QUESTION # 153
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
B. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
C. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
D. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
E. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

Answer: D

Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.

NEW QUESTION # 154
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 representing a PySpark DataFrame.
B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
C. Both commands will fail. No new variables, tables, or views will be created.
D. 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.
E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

Answer: E

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 # 155
identifies if its needs to be converted to Fahrenheit or Celcius with a one-word letter F or C?
select udf_convert(60,'C') will result in 15.5
select udf_convert(10,'F') will result in 50

A. 1.CREATE USER FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5.END
B. 1. CREATE UDF FUNCTION udf_convert(temp DOUBLE, measure STRING)
2. RETURNS DOUBLE
3. RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5. END
C. 1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5. END
(Correct)
D. 1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
3. ELSE (temp - 33 ) * 5/9
4. END
E. 1.CREATE UDF FUNCTION udf_convert(temp DOUBLE, measure STRING)
2. RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
3. ELSE (temp - 33 ) * 5/9
4. END

Answer: C

Explanation:
Explanation
The answer is
1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5. END

NEW QUESTION # 156
......

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