FREE PDF 2025 HIGH HIT-RATE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER: DATABRICKS CERTIFIED PROFESSIONAL DATA ENGINEER EXAM EXAM TRAINING

Free PDF 2025 High Hit-Rate Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Exam Training

Free PDF 2025 High Hit-Rate Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Exam Training

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Databricks Certified Professional Data Engineer exam is a certification program designed for data professionals who want to validate their expertise in building and maintaining data pipelines using Databricks. Databricks is a cloud-based data engineering platform that provides a unified analytics engine for big data processing, machine learning, and streaming analytics. Databricks-Certified-Professional-Data-Engineer exam is designed to test a candidate's ability to design, build, and optimize data pipelines using Databricks, as well as their proficiency in data modeling, data warehousing, and data integration.

Databricks Certified Professional Data Engineer certification exam is designed to test the knowledge and skills of data engineers who work with Databricks. Databricks is a cloud-based platform that provides a unified analytics engine for big data processing and machine learning. It is used by data engineers to manage data pipelines, extract insights from data, and build machine learning models. Databricks Certified Professional Data Engineer Exam certification exam is a comprehensive assessment of the candidate's ability to use Databricks effectively for data engineering tasks.

Databricks Certified Professional Data Engineer certification exam is a challenging exam that requires candidates to demonstrate their understanding of Databricks and data engineering concepts. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions, and candidates have three hours to complete the exam. Databricks-Certified-Professional-Data-Engineer exam covers various topics, including data modeling, data warehousing, data governance, and working with Databricks clusters. To pass the exam, candidates must achieve a minimum passing score of 70%.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?

  • A. They can observe the task as it runs to try and determine why it is failing
  • B. They can utilize a Jobs cluster for each of the tasks in the Job
  • C. They can set up the Job to run multiple times ensuring that at least one will complete
  • D. They can institute a retry policy for the task that periodically fails
  • E. They can institute a retry policy for the entire Job

Answer: D


NEW QUESTION # 85
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

  • A. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
  • B. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
  • C. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
  • D. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
  • E. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.

Answer: B

Explanation:
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed. Reference:
https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html
https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html
https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html


NEW QUESTION # 86
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_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. 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.
  • 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. 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.
  • E. 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.

Answer: B


NEW QUESTION # 87
Which statement regarding stream-static joins and static Delta tables is correct?

  • A. Stream-static joins cannot use static Delta tables because of consistency issues.
  • B. The checkpoint directory will be used to track updates to the static Delta table.
  • C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
  • D. The checkpoint directory will be used to track state information for the unique keys present in the join.
  • E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Answer: E

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.


NEW QUESTION # 88
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

  • A. dbutils.widgets.text("date", "null")
    date = dbutils.widgets.get("date")
  • B. date = spark.conf.get("date")
  • C. import sys
    date = sys.argv[1]
  • D. input_dict = input()
    date= input_dict["date"]
  • E. date = dbutils.notebooks.getParam("date")

Answer: A

Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter1. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.
The other options are not correct, because:
Option A is incorrect because spark.conf.get("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.
Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API. The input() function is used to get user input from the standard input stream, not from the API request3.
Option C is incorrect because sys.argv1 is not a valid way to get a parameter passed through the Databricks Jobs API. The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.
Option D is incorrect because dbutils.notebooks.getParam("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.


NEW QUESTION # 89
......

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