DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL PASS-FOR-SURE BRAINDUMPS: DATABRICKS CERTIFIED DATA ENGINEER PROFESSIONAL EXAM & DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL QUIZ GUIDE

Databricks-Certified-Data-Engineer-Professional Pass-For-Sure Braindumps: Databricks Certified Data Engineer Professional Exam & Databricks-Certified-Data-Engineer-Professional Quiz Guide

Databricks-Certified-Data-Engineer-Professional Pass-For-Sure Braindumps: Databricks Certified Data Engineer Professional Exam & Databricks-Certified-Data-Engineer-Professional Quiz Guide

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Real Databricks-Certified-Data-Engineer-Professional Exam | Exam Databricks-Certified-Data-Engineer-Professional Syllabus

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
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. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
  • B. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
  • C. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
  • D. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
  • E. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

Answer: B

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.


NEW QUESTION # 78
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

  • A. Regex
  • B. C++
  • C. Scala Datasets
  • D. Julia
  • E. pyspsark.ml.feature

Answer: A

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks.


NEW QUESTION # 79
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

  • A. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
  • B. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
  • C. Instruct the marketing team to download results as a CSV and email them to the sales organization.
  • D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
  • E. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

Answer: E

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.


NEW QUESTION # 80
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. date = dbutils.notebooks.getParam("date")
  • B. date = spark.conf.get("date")
  • C. import sys
    date = sys.argv[1]
  • D. dbutils.widgets.text("date", "null")
    date = dbutils.widgets.get("date")
  • E. input_dict = input()
    date= input_dict["date"]

Answer: D

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 parameter. 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.


NEW QUESTION # 81
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

  • A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
  • B. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
  • C. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
  • D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
  • E. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Answer: E

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.


NEW QUESTION # 82
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