PREMIUM DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER QUESTIONS

Premium Databricks Databricks-Certified-Professional-Data-Engineer Questions

Premium Databricks Databricks-Certified-Professional-Data-Engineer Questions

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Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and hands-on, real-world scenarios that test the candidate's ability to design, build, and deploy data pipelines on Databricks. Databricks-Certified-Professional-Data-Engineer exam covers various topics, including data engineering concepts, Databricks architecture, data processing using Spark, and data integration with other systems. Databricks Certified Professional Data Engineer Exam certification program provides a comprehensive learning experience that prepares candidates to become skilled data engineers and provides them with a competitive edge in the job market.

Databricks Certified Professional Data Engineer exam is a comprehensive assessment that covers a wide range of topics related to data engineering using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design, build, and optimize data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer exam is available online and can be taken from anywhere in the world, making it a convenient option for data professionals who want to validate their expertise in Databricks. Upon successful completion of the exam, candidates will receive a Databricks Certified Professional Data Engineer certification, which will demonstrate their proficiency in data engineering using Databricks.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which of the following are stored in the control pane of Databricks Architecture?

  • A. Job Clusters
  • B. Databricks Web Application
  • C. Delta tables
  • D. All Purpose Clusters
  • E. Databricks Filesystem

Answer: B

Explanation:
Explanation
The answer is Databricks Web Application
Azure Databricks architecture overview - Azure Databricks | Microsoft Docs Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
* The control plane includes the backend services that Databricks manages in its own Azure account.
Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
* The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Timeline Description automatically generated

Bottom of Form
Top of Form


NEW QUESTION # 109
Define an external SQL table by connecting to a local instance of an SQLite database using JDBC

  • A. 1.CREATE TABLE users_jdbc
    2.USING org.apache.spark.sql.jdbc.sqlite
    3.OPTIONS (
    4. url = "jdbc:/sqmple_db",
    5. dbtable = "users"
    6.)
  • B. 1.CREATE TABLE users_jdbc
    2.USING SQL
    3.URL = {server:"jdbc:/sqmple_db",dbtable: "users"}
  • C. 1.CREATE TABLE users_jdbc
    2.USING SQL
    3.OPTIONS (
    4. url = "jdbc:sqlite:/sqmple_db",
    5. dbtable = "users"
    6.)
  • D. 1.CREATE TABLE users_jdbc
    2.USING SQLITE
    3.OPTIONS (
    4. url = "jdbc:/sqmple_db",
    5. dbtable = "users"
    6.)
  • E. 1.CREATE TABLE users_jdbc
    2.USING org.apache.spark.sql.jdbc
    3.OPTIONS (
    4. url = "jdbc:sqlite:/sqmple_db",
    5. dbtable = "users"
    6.)

Answer: A

Explanation:
Explanation
The answer is,
1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
Databricks runtime currently supports connecting to a few flavors of SQL Database including SQL Server, My SQL, SQL Lite and Snowflake using JDBC.
1.CREATE TABLE <jdbcTable>
2.USING org.apache.spark.sql.jdbc or JDBC
3.OPTIONS (
4. url = "jdbc:<databaseServerType>://<jdbcHostname>:<jdbcPort>",
5. dbtable " = <jdbcDatabase>.atable",
6. user = "<jdbcUsername>",
7. password = "<jdbcPassword>"
8.)
For more detailed documentation
SQL databases using JDBC - Azure Databricks | Microsoft Docs


NEW QUESTION # 110
Which of the following data workloads will utilize a gold table as its source?

  • A. A job that enriches data by parsing its timestamps into a human-readable format
  • B. A job that ingests raw data from a streaming source into the Lakehouse
  • C. A job that queries aggregated data that already feeds into a dashboard
  • D. A job that cleans data by removing malformatted records
  • E. A job that aggregates cleaned data to create standard summary statistics

Answer: C

Explanation:
Explanation
The answer is, A job that queries aggregated data that already feeds into a dashboard The gold layer is used to store aggregated data, which are typically used for dashboards and reporting.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture


NEW QUESTION # 111
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

  • A. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
  • B. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
  • C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
  • D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
  • E. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Answer: E

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. Reference:
transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html


NEW QUESTION # 112
What is true for Delta Lake?

  • A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
  • B. Z-ORDERcan only be applied to numeric values stored in Delta Lake tables.
  • C. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
  • D. Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters.

Answer: D

Explanation:
* Delta Lake automaticallycollects statisticson the first32 columnsof each table. These statistics helpoptimize query performancethroughdata skipping, which allows Databricks to scan only relevant parts of a table.
* This feature significantly improves query efficiency, especially when dealing with large datasets.
Why Other Options Are Incorrect:
* Option A:Views do not cache the most recent versions of the source table; they are recomputed when queried.
* Option C:Z-ORDERcan be applied to any data type, including strings, to optimize read performance.
* Option D:Delta Lake does not enforce primary or foreign key constraints.


NEW QUESTION # 113
......

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