FREE PDF QUIZ 2025 DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER PERFECT RELIABLE CRAM MATERIALS

Free PDF Quiz 2025 Databricks Databricks-Certified-Professional-Data-Engineer Perfect Reliable Cram Materials

Free PDF Quiz 2025 Databricks Databricks-Certified-Professional-Data-Engineer Perfect Reliable Cram Materials

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Databricks Certified Professional Data Engineer exam is a hands-on exam that requires the candidate to complete a set of tasks using Databricks. Databricks-Certified-Professional-Data-Engineer exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks-Certified-Professional-Data-Engineer Exam also tests the candidate's ability to optimize and tune data pipelines for performance and reliability.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
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. The table will be removed from the catalog but the data will remain in storage.
  • C. The table will be removed from the catalog and the data will be deleted.
  • D. Data will be marked as deleted but still recoverable with Time Travel.
  • E. An error will occur because Delta Lake prevents the deletion of production data.

Answer: C

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. Reference:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table


NEW QUESTION # 45
Which statement describes the correct use of pyspark.sql.functions.broadcast?

  • A. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
  • B. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
  • C. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
  • D. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
  • E. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

Answer: C

Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
Thepyspark.sql.functions.broadcastfunction in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation.
In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.
References:
* Databricks Documentation on Broadcast Joins: Databricks Broadcast Join Guide
* PySpark API Reference: pyspark.sql.functions.broadcast


NEW QUESTION # 46
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

  • A. Ensures that all steps interact correctly to achieve the desired end result
  • B. Troubleshooting is easier since all steps are isolated and tested individually
  • C. Validates a complete use case of your application
  • D. Improves the quality of your data
  • E. Yields faster deployment and execution times

Answer: B


NEW QUESTION # 47
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"

  • A. USING CSV
    TYPE ( "true","|")
  • B. USING CSV
    OPTIONS ( header ="true", delimiter = "|")
    (Correct)
  • C. FORMAT CSV
    OPTIONS ( "true","|")
  • D. FORMAT CSV
    FORMAT TYPE ( header ="true", delimiter = "|")
  • E. FORMAT CSV
    TYPE ( header ="true", delimiter = "|")

Answer: B

Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"


NEW QUESTION # 48
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?

  • A. .processingTime(1)
  • B. .processingTime("once")
  • C. .trigger(continuous="once")
  • D. .trigger(processingTime="once")
  • E. .trigger(once=True)

Answer: E


NEW QUESTION # 49
......

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