Dependable Databricks-Certified-Professional-Data-Engineer Exam Dumps to Become Databricks Certified [Q17-Q33] : Free Exam Dumps Collection : http://free.examcollectionpass.com

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Dependable Databricks-Certified-Professional-Data-Engineer Exam Dumps to Become Databricks Certified [Q17-Q33]

Dependable Databricks-Certified-Professional-Data-Engineer Exam Dumps to Become Databricks Certified

Get Ready with Databricks-Certified-Professional-Data-Engineer Exam Dumps (2023)

Databricks Certified Professional Data Engineer exam is a comprehensive certification exam that assesses an individual's knowledge and skills in working with big data and cloud computing technologies. Databricks-Certified-Professional-Data-Engineer exam is designed for data professionals who are proficient in using Databricks Unified Analytics Platform for managing and analyzing large volumes of data. Databricks-Certified-Professional-Data-Engineer exam covers a broad range of topics such as data engineering, data transformation, data modeling, and machine learning.

Q17. Which of the below SQL commands create a Global temporary view?

1.CREATE OR REPLACE TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name

1. CREATE OR REPLACE LOCAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name

1. CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
(Correct)

1.CREATE OR REPLACE VIEW view_name
2. AS SELECT * FROM table_name

1. CREATE OR REPLACE LOCAL VIEW view_name
2. AS SELECT * FROM table_name

Q18. The operations team is interested in monitoring the recently launched product, team wants to set up an email alert when the number of units sold increases by more than 10,000 units. They want to monitor this every 5 mins.
Fill in the below blanks to finish the steps we need to take
* Create ___ query that calculates total units sold
* Setup ____ with query on trigger condition Units Sold > 10,000
* Setup ____ to run every 5 mins
* Add destination ______

Python, Job, SQL Cluster, email address

SQL, Alert, Refresh, email address

SQL, Job, SQL Cluster, email address

SQL, Job, Refresh, email address

Python, Job, Refresh, email address

Q19. Which of the following python statements can be used to replace the schema name and table name in the query?

1.table_name = “sales”
2.schema_name = “bronze”
3.query = f”select * from schema_name.table_name”

1.table_name = “sales”
2.query = “select * from {schema_name}.{table_name}”

1.table_name = “sales”
2.query = f”select * from {schema_name}.{table_name}”

1.table_name = “sales”
2.query = f”select * from + schema_name +”.”+table_name”

Q20. Your colleague was walking you through how a job was setup, but you noticed a warning message that said,
“Jobs running on all-purpose cluster are considered all purpose compute”, the colleague was not sure why he was getting the warning message, how do you best explain this warning mes-sage?

All-purpose clusters cannot be used for Job clusters, due to performance issues.

All-purpose clusters take longer to start the cluster vs a job cluster

All-purpose clusters are less expensive than the job clusters

All-purpose clusters are more expensive than the job clusters

All-purpose cluster provide interactive messages that can not be viewed in a job

Q21. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the fieldpk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.

Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake’s versioning ability to create an audit log.

Use Delta Lake’s change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

Q22. A new data engineer [email protected] has been assigned to an ELT project. The new data
engineer will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the table to the new data engineer?

1. GRANT ALL PRIVILEGES ON TABLE [email protected] TO sales;

1. GRANT SELECT ON TABLE sales TO [email protected];

1. GRANT ALL PRIVILEGES ON TABLE sales TO [email protected];

1. GRANT USAGE ON TABLE sales TO [email protected];

1. GRANT SELECT CREATE MODIFY ON TABLE sales TO [email protected];

Q23. You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions

Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions

Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.

Appends the data to the current version, you can time travel to previous versions

By default, overwrites the data and schema, you cannot perform time travel

Q24. 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

Q25. What is the purpose of a silver layer in Multi hop architecture?

Replaces a traditional data lake

Efficient storage and querying of full and unprocessed history of data

A schema is enforced, with data quality checks.

Refined views with aggregated data

Optimized query performance for business-critical data

Q26. Which of the following results in the creation of an external table?

CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION EX-TERNAL

CREATE TABLE transactions (id int, desc string)

CREATE EXTERNAL TABLE transactions (id int, desc string)

CREATE TABLE transactions (id int, desc string) TYPE EXTERNAL

CREATE TABLE transactions (id int, desc string) LOCATION ‘/mnt/delta/transactions’

Q27. Which of the following SQL commands are used to append rows to an existing delta table?

APPEND INTO DELTA table_name

APPEND INTO table_name

COPY DELTA INTO table_name

INSERT INTO table_name

UPDATE table_name

Q28. Which of the following SQL statements can replace a python variable, when the notebook is set in SQL mode
1.table_name = “sales”
2.schema_name = “bronze”

spark.sql(f”SELECT * FROM f{schema_name.table_name}”)

spark.sql(f”SELECT * FROM {schem_name.table_name}”)

spark.sql(f”SELECT * FROM ${schema_name}.${table_name}”)

spark.sql(f”SELECT * FROM {schema_name}.{table_name}”)

spark.sql(“SELECT * FROM schema_name.table_name”)

Q29. The viewupdatesrepresents an incremental batch of all newly ingested data to be inserted or updated in the customerstable.
The following logic is used to process these records.

Which statement describes this implementation?

The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

Q30. 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

Q31. A data engineer has set up a notebook to automatically process using a Job. The data engineer’s manager wants
to version control the schedule due to its complexity.
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of
the Job’s schedule?

They can download the JSON description of the Job from the Job’s page

They can submit the Job once on an all-purpose cluster

They can link the Job to notebooks that are a part of a Databricks Repo

They can submit the Job once on a Job cluster

They can download the XML description of the Job from the Job’s page

Q32. A data engineer wants to create a relational object by pulling data from two tables. The relational object must
be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to
avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

Delta Table

View

Temporary view

Spark SQL Table

Database

Q33. You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year’s worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?

Linear regression

Logistic regression

Decision trees

TF-IDF

Databricks Certified Professional Data Engineer exam covers a wide range of topics, including data engineering concepts, Databricks architecture, data ingestion and processing, data storage and management, and data security. Databricks-Certified-Professional-Data-Engineer exam consists of 60 multiple-choice questions and participants have 90 minutes to complete it. Passing the exam requires a score of 70% or higher, and successful candidates receive a certificate that validates their expertise in building and managing data pipelines on the Databricks platform.

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Post date: 2023-11-02 11:43:51
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