DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER DESKTOP-BASED PRACTICE PROGRAM

Databricks Databricks-Certified-Professional-Data-Engineer Desktop-Based Practice Program

Databricks Databricks-Certified-Professional-Data-Engineer Desktop-Based Practice Program

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To prepare for the DCPDE exam, candidates should have a solid understanding of data engineering concepts, such as data modeling, data integration, data transformation, and data quality. They should also have experience working with big data technologies, such as Apache Spark, Apache Kafka, and Apache Hadoop.

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Databricks Certified Professional Data Engineer certification is recognized by the industry as a standard for measuring the skills and expertise of data engineers who work with Databricks. It demonstrates that the candidate has the knowledge and skills required to design and build data solutions using Databricks that meet the highest standards of performance, scalability, and reliability. Databricks Certified Professional Data Engineer Exam certification is valuable for data engineers who want to advance their careers by demonstrating their proficiency in using Databricks and for organizations that want to ensure that their data engineers have the skills and expertise needed to build and maintain data solutions using Databricks.

Databricks-Certified-Professional-Data-Engineer exam is a specialized test that focuses on assessing the technical skillsets of candidates in working on cloud-based big data projects. Candidates will be required to demonstrate their proficiency in a wide range of topics, including data structures and algorithms, distributed systems, database design, Hadoop and Spark, and machine learning. Databricks-Certified-Professional-Data-Engineer Exam contains multiple-choice questions that test the candidates’ knowledge of these areas.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
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. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
  • B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
  • C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
  • D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer: A

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.
Reference:
Databricks Documentation on Delta Lake: Delta Lake Guide
Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake


NEW QUESTION # 106
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

  • A. SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'
  • B. DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)
  • C. SHOW HISTORY table_name AS OF date_sub(current_date(), 1)
  • D. TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
  • E. SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

Answer: E

Explanation:
Explanation
The answer is SELECT * FROM table_name TIMESTAMP as of date_sub(current_date(), 1) FYI, Time travel supports two ways one is using timestamp and the second way is using version number, Timestamp:
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000" Version Number:
1.SELECT count(*) FROM my_table VERSION AS OF 5238
2.SELECT count(*) FROM my_table@v5238
3.SELECT count(*) FROM delta.`/path/to/my/table@v5238`
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html


NEW QUESTION # 107
Kevin is the owner of the schema sales, Steve wanted to create new table in sales schema called regional_sales so Kevin grants the create table permissions to Steve. Steve creates the new table called regional_sales in sales schema, who is the owner of the table regional_sales

  • A. Kevin and Smith both are owners of table
  • B. By default ownership is assigned DBO
  • C. Steve is the owner of the table
  • D. Kevin is the owner of sales schema, all the tables in the schema will be owned by Kevin
  • E. By default ownership is assigned to DEFAULT_OWNER

Answer: C

Explanation:
Explanation
A user who creates the object becomes its owner, does not matter who is the owner of the parent object.


NEW QUESTION # 108
Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

  • A. All of the data is stored into one parquet file, log files are broken down into one or many json files.
  • B. All of the data and log are stored in a single parquet file
  • C. All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.
  • D. All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
    (Correct)
  • E. All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.

Answer: D

Explanation:
Explanation
Answer is
All of the data is broken down into one or many parquet files, log files are broken down into one or many json files, and each transaction creates a new data file(s) and log file.
here is sample layout of how DELTA table might look,


NEW QUESTION # 109
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

  • A. workspace
  • B. fs
  • C. configure
  • D. jobs
  • E. libraries

Answer: B

Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl
dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
* Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
* Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
* Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html


NEW QUESTION # 110
......

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