Databricks-Certified-Professional-Data-Engineer Exam Dumps Demo - 100% Databricks-Certified-Professional-Data-Engineer Correct Answers

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Databricks Certified Professional Data Engineer certification exam is a highly sought-after certification in the data engineering industry. It is designed to test the skills and knowledge of data engineers who work with Databricks, a cloud-based platform that helps organizations manage large amounts of data and perform advanced analytics.

Databricks Certified Professional Data Engineer Certification Exam is a rigorous and comprehensive certification exam that validates a professional's competence in Databricks data engineering principles and techniques. With the exponential growth of data management and processing, data professionals need certification to show that they are experts in handling data. With Databricks Certified Professional Data Engineer certification, data engineers can confidently build, implement and maintain effective and scalable data engineering solutions.

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The Databricks Databricks-Certified-Professional-Data-Engineer Exam is intended for data engineers with experience in designing and implementing data solutions using Databricks. Candidates for this certification should have a good understanding of data engineering concepts, data processing frameworks, and programming languages such as Python and SQL. They should also be familiar with cloud platforms such as AWS, Azure, and Google Cloud Platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q214-Q219):

NEW QUESTION # 214
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 no data is changed.
B. The table name change is recorded in the Delta transaction log.
C. A new Delta transaction log Is created for the renamed table.
D. All related files and metadata are dropped and recreated in a single ACID transaction.
E. The table reference in the metastore is updated and all data files are moved.

Answer: A

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 References:
[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 # 215
Which of the below SQL commands create a Global temporary view?

A. 1. CREATE OR REPLACE LOCAL VIEW view_name
2. AS SELECT * FROM table_name
B. 1. CREATE OR REPLACE LOCAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
C. 1. CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
(Correct)
D. 1.CREATE OR REPLACE TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
E. 1.CREATE OR REPLACE VIEW view_name
2. AS SELECT * FROM table_name

Answer: C

Explanation:
Explanation
1. CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
There are two types of temporary views that can be created Local and Global
*A session-scoped temporary view is only available with a spark session, so another note-book in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
*A global temporary view is available to all the notebooks in the cluster but if a cluster re-starts a global temporary view is lost.

NEW QUESTION # 216
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 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)
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 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
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 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

Answer: A

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 # 217
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Answer: A

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
References:
* Databricks documentation on schema evolution in Delta Lake:
https://docs.databricks.com/delta/delta-batch.html#schema-evolution

NEW QUESTION # 218
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A. A managed table will be created in the DBFS root storage container.
B. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
C. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
D. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
E. An external table will be created in the storage container mounted to /mnt/finance eda bucket.

Answer: E

Explanation:
Explanation
The code uses the CREATE TABLE USING DELTA command to create a Delta Lake table from an existing Parquet file stored in an external object storage container mounted to /mnt/finance_eda_bucket. The code 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 code 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. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Create an external table" section.

NEW QUESTION # 219
......

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