Aug-2023 Pass Databricks Databricks-Certified-Professional-Data-Engineer Exam in First Attempt Easily [Q101-Q120]

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Aug-2023 Pass Databricks Databricks-Certified-Professional-Data-Engineer Exam in First Attempt Easily

Free Databricks-Certified-Professional-Data-Engineer Exam Files Downloaded Instantly 100% Dumps & Practice Exam

QUESTION 101
Delete records from the transactions Delta table where transactionDate is greater than current timestamp?

DELETE FROM transactions FORMAT DELTA where transactionDate > cur-renct_timestmap()

DELETE FROM transactions if transctionDate > current_timestamp()

DELETE FROM transactions where transactionDate > current_timestamp()

DELETE FROM transactions where transactionDate > current_timestamp() KEEP_HISTORY

DELET FROM transactions where transactionDate GE current_timestamp()

QUESTION 102
Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?

The ability to write pipelines in Python and/or SQL

The ability to declare and maintain data table dependencies

The ability to automatically scale compute resources

The ability to access previous versions of data tables

The ability to perform batch and streaming queries

QUESTION 103
Which of the following statements describes Delta Lake?

Delta Lake is an open source platform to help manage the complete machine learning lifecycle

Delta Lake is an open format storage layer that delivers reliability, security, and per-formance

Delta Lake is an open source data storage format for distributed data

Delta Lake is an open source analytics engine used for big data workloads

Delta Lake is an open format storage layer that processes data

QUESTION 104
Which of the following commands can be used to query a delta table?

1.%python
2.spark.sql(“select * from table_name”)

1.%sql
2.Select * from table_name

Both A & B
(Correct)

1.%python
2.execute.sql(“select * from table”)

1.%python
2.delta.sql(“select * from table”)

QUESTION 105
When building a DLT s pipeline you have two options to create a live tables, what is the main dif-ference between CREATE STREAMING LIVE TABLE vs CREATE LIVE TABLE?

CREATE STREAMING LIVE table is used in MULTI HOP Architecture

CREATE LIVE TABLE is used when working with Streaming data sources and Incremental data

CREATE STREAMING LIVE TABLE is used when working with Streaming data sources and Incremental data

There is no difference both are the same, CREATE STRAMING LIVE will be deprecated soon

CREATE LIVE TABLE is used in DELTA LIVE TABLES, CREATE STREAMING LIVE can only used in Structured Streaming applications

QUESTION 106
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?

Databricks Repos can be used to design, develop, and trigger Git automation pipelines

Databricks Repos can facilitate the pull request, review, and approval process before merging branches

Databricks Repos can merge changes from a secondary Git branch into a main Git branch

Databricks Repos can commit or push code changes to trigger a CI/CD process

Databricks Repos can store the single-source-of-truth Git repository

QUESTION 107
What are the different ways you can schedule a job in Databricks workspace?

Continuous, Incremental

On-Demand runs, File notification from Cloud object storage

Cron, On Demand runs

Cron, File notification from Cloud object storage

Once, Continuous

QUESTION 108
How does Lakehouse replace the dependency on using Data lakes and Data warehouses in a Data and Analytics solution?

Open, direct access to data stored in standard data formats.

Supports ACID transactions.

Supports BI and Machine learning workloads

Support for end-to-end streaming and batch workloads

All the above

QUESTION 109
Which of the following technique can be used to implement fine-grained access control to rows and columns of the Delta table based on the user’s access?

Use Unity catalog to grant access to rows and columns

Row and column access control lists

Use dynamic view functions

Data access control lists

Dynamic Access control lists with Unity Catalog

QUESTION 110
A data engineer has developed a code block to perform a streaming read on a data source. The code block is
below:
1. (spark
2. .read
3. .schema(schema)
4. .format(“cloudFiles”)
5. .option(“cloudFiles.format”, “json”)
6. .load(dataSource)
7. )
The code block is returning an error.
Which of the following changes should be made to the code block to configure the block to successfully
perform a streaming read?

The .read line should be replaced with .readStream

The .format(“cloudFiles”) line should be replaced with .format(“stream”)

A new .stream line should be added after the spark line

A new .stream line should be added after the .read line

A new .stream line should be added after the .load(dataSource) line

QUESTION 111
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

Data plane

Control plane

Databricks Filesystem

JDBC data source

Databricks web application

QUESTION 112
you are currently working on creating a spark stream process to read and write in for a one-time micro batch, and also rewrite the existing target table, fill in the blanks to complete the below command sucesfully.
1.spark.table(“source_table”)
2..writeStream
3..option(“____”, “dbfs:/location/silver”)
4..outputMode(“____”)
5..trigger(Once=____)
6..table(“target_table”)

checkpointlocation, complete, True

targetlocation, overwrite, True

checkpointlocation, True, overwrite

checkpointlocation, True, complete

checkpointlocation, overwrite, True

QUESTION 113
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?

SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = ‘timestamp’

TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)

SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)

SHOW HISTORY table_name AS OF date_sub(current_date(), 1)

QUESTION 114
What is the type of table created when you issue SQL DDL command CREATE TABLE sales (id int, units int)

Query fails due to missing location

Query fails due to missing format

Managed Delta table

External Table

Managed Parquet table

QUESTION 115
What is the main difference between the silver layer and gold layer in medallion architecture?

Silver optimized to perform ETL, Gold is optimized query performance

Gold is optimized go perform ETL, Silver is optimized for query performance

Silver is copy of Bronze, Gold is a copy of Silver

Silver is stored in Delta Lake, Gold is stored in memory

Silver may contain aggregated data, gold may preserve the granularity of original data

QUESTION 116
The data engineering team noticed that one of the job normally finishes in 15 mins but gets stuck randomly when reading remote databases due to a network packet drop, which of the following steps can be used to improve the stability of the job?

Use Databrick REST API to monitor long running jobs and issue a kill command

Use Jobs runs, active runs UI section to monitor and kill long running job

Modify the task, to include a timeout to kill the job if it runs more than 15 mins.

Use Spark job time out setting in the Spark UI

Use Cluster timeout setting in the Job cluster UI

QUESTION 117
Create a schema called bronze using location ‘/mnt/delta/bronze’, and check if the schema exists before creating.

CREATE SCHEMA IF NOT EXISTS bronze LOCATION ‘/mnt/delta/bronze’

CREATE SCHEMA bronze IF NOT EXISTS LOCATION ‘/mnt/delta/bronze’

if IS_SCHEMA(‘bronze’): CREATE SCHEMA bronze LOCATION ‘/mnt/delta/bronze’

Schema creation is not available in metastore, it can only be done in Unity catalog UI

Cannot create schema without a database

QUESTION 118
Which of the following is not a privilege in the Unity catalog?

SELECT

MODIFY

DELETE

CREATE TABLE

EXECUTE

QUESTION 119
Which of the statements is correct when choosing between lakehouse and Datawarehouse?

Traditional Data warehouses have special indexes which are optimized for Machine learning

Traditional Data warehouses can serve low query latency with high reliability for BI workloads

SQL support is only available for Traditional Datawarehouse’s, Lakehouses support Python and Scala

Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.

Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.

QUESTION 120
When you drop an external DELTA table using the SQL Command DROP TABLE table_name, how does it impact metadata(delta log, history), and data stored in the storage?

Drops table from metastore, metadata(delta log, history)and data in storage

Drops table from metastore, data but keeps metadata(delta log, history) in storage

Drops table from metastore, metadata(delta log, history)but keeps the data in storage

Drops table from metastore, but keeps metadata(delta log, history)and data in storage

Drops table from metastore and data in storage but keeps metadata(delta log, history)

Databricks Certified Professional Data Engineer certification exam is a prestigious certification that validates the skills and expertise of data professionals in building and managing complex data solutions on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows in a collaborative environment. Databricks-Certified-Professional-Data-Engineer exam validates the ability to design and implement scalable and reliable data solutions using Databricks technologies.

Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Exam is a certification exam designed to test the knowledge and skills of data engineers who use Databricks to build and manage data pipelines. Databricks is a cloud-based data processing and analytics platform that provides a unified workspace for data scientists, data engineers, and business analysts to collaborate and work with large-scale data. Databricks-Certified-Professional-Data-Engineer exam is intended for data engineers who have experience in developing and maintaining data pipelines using Databricks and are looking to validate their skills and knowledge.

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Aug-2023 Pass Databricks Databricks-Certified-Professional-Data-Engineer Exam in First Attempt Easily [Q101-Q120]

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