New Year Sale

Why Buy Databricks-Certified-Professional-Data-Engineer Exam Dumps From Passin1Day?

Having thousands of Databricks-Certified-Professional-Data-Engineer customers with 99% passing rate, passin1day has a big success story. We are providing fully Databricks exam passing assurance to our customers. You can purchase Databricks Certified Data Engineer Professional exam dumps with full confidence and pass exam.

Databricks-Certified-Professional-Data-Engineer Practice Questions

Question # 1
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema: key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely. Which of the following solutions meets the requirements?
A. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
B. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
C. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
D. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
E. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
Explanation:

Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions.

References:

Table Access Control: https://docs.databricks.com/security/access-control/tableacls/index.html

DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table


Question # 2
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code. Which statement describes a main benefit that offset this additional effort?
A. Improves the quality of your data
B. Validates a complete use case of your application
C. Troubleshooting is easier since all steps are isolated and tested individually
D. Yields faster deployment and execution times
E. Ensures that all steps interact correctly to achieve the desired end result


A. Improves the quality of your data


Question # 3
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount(). Which of the following statements is correct?
A. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
D. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
E. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.


A. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.

Explanation:

DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE

Question # 4
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. configure
B. fs
C. jobs
D. libraries
E. workspace


B. fs

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/mylib0.1-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/devtools/cli/libraries-cli.html

Library operations:

https://docs.databricks.com/en/devtools/cli/commands.html#library-operations

Install or update the Databricks CLI:

https://docs.databricks.com/en/devtools/cli/install.html


Question # 5
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 field pk_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?
A. 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.
B. 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.
C. 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.
D. 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.
E. 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.


B. 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.

Explanation:

This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns.

This way, the silver table will always reflect the current state of the source table, while the bronze table will keep the history of all changes. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Upsert into a table using merge” section.


Question # 6
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.

Which statement describes the outcome of this batch insert?

A. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
D. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.


B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.

Explanation:

The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated.

References:

Constraints:

https://docs.delta.io/latest/delta-constraints.html

ACID Transactions:

https://docs.delta.io/latest/delta-intro.html#acid-transactions


Question # 7
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
B. Stop the existing pipeline; use the returned settings in a reset command
C. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline


A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

Explanation:

The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get -- pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.

References:

Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines


Question # 8
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. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
D. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
E. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.


D. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

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.

Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Data skipping” section.


Databricks-Certified-Professional-Data-Engineer Dumps
  • Up-to-Date Databricks-Certified-Professional-Data-Engineer Exam Dumps
  • Valid Questions Answers
  • Databricks Certified Data Engineer Professional PDF & Online Test Engine Format
  • 3 Months Free Updates
  • Dedicated Customer Support
  • Databricks Certification Pass in 1 Day For Sure
  • SSL Secure Protected Site
  • Exam Passing Assurance
  • 98% Databricks-Certified-Professional-Data-Engineer Exam Success Rate
  • Valid for All Countries

Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps

Exam Name: Databricks Certified Data Engineer Professional
Certification Name: Databricks Certification

Databricks Databricks-Certified-Professional-Data-Engineer exam dumps are created by industry top professionals and after that its also verified by expert team. We are providing you updated Databricks Certified Data Engineer Professional exam questions answers. We keep updating our Databricks Certification practice test according to real exam. So prepare from our latest questions answers and pass your exam.

  • Total Questions: 120
  • Last Updation Date: 16-Jan-2025

PDF Price

$19.99
$55.99 Updates:

Engine Price

$25.99
$73.99 Updates:

PDF + Engine

$29.99
$84.99 Updates:


Up-to-Date

We always provide up-to-date Databricks-Certified-Professional-Data-Engineer exam dumps to our clients. Keep checking website for updates and download.

Excellence

Quality and excellence of our Databricks Certified Data Engineer Professional practice questions are above customers expectations. Contact live chat to know more.

Success

Your SUCCESS is assured with the Databricks-Certified-Professional-Data-Engineer exam questions of passin1day.com. Just Buy, Prepare and PASS!

Quality

All our braindumps are verified with their correct answers. Download Databricks Certification Practice tests in a printable PDF format.

Basic

$80

Any 3 Exams of Your Choice

3 Exams PDF + Online Test Engine

Buy Now
Premium

$100

Any 4 Exams of Your Choice

4 Exams PDF + Online Test Engine

Buy Now
Gold

$125

Any 5 Exams of Your Choice

5 Exams PDF + Online Test Engine

Buy Now

About Us

Passin1Day has a big success story in last 12 years with a long list of satisfied customers.

We are UK based company, selling Databricks-Certified-Professional-Data-Engineer practice test questions answers. We have a team of 34 people in Research, Writing, QA, Sales, Support and Marketing departments and helping people get success in their life.

We dont have a single unsatisfied Databricks customer in this time. Our customers are our asset and precious to us more than their money.

WE FEEL SATISFIED WHEN YOU GET SATISFIED!

Databricks-Certified-Professional-Data-Engineer Dumps

We have recently updated Databricks Databricks-Certified-Professional-Data-Engineer dumps study guide. You can use our Databricks Certification braindumps and pass your exam in just 24 hours. Our Databricks Certified Data Engineer Professional real exam contains latest questions. We are providing Databricks Databricks-Certified-Professional-Data-Engineer dumps with updates for 3 months. You can purchase in advance and start studying. Whenever Databricks update Databricks Certified Data Engineer Professional exam, we also update our file with new questions. Passin1day is here to provide real Databricks-Certified-Professional-Data-Engineer exam questions to people who find it difficult to pass exam

Databricks Certification can advance your marketability and prove to be a key to differentiating you from those who have no certification and Passin1day is there to help you pass exam with Databricks-Certified-Professional-Data-Engineer dumps. Databricks Certifications demonstrate your competence and make your discerning employers recognize that Databricks Certified Data Engineer Professional certified employees are more valuable to their organizations and customers.


We have helped thousands of customers so far in achieving their goals. Our excellent comprehensive Databricks exam dumps will enable you to pass your certification Databricks Certification exam in just a single try. Passin1day is offering Databricks-Certified-Professional-Data-Engineer braindumps which are accurate and of high-quality verified by the IT professionals.

Candidates can instantly download Databricks Certification dumps and access them at any device after purchase. Online Databricks Certified Data Engineer Professional practice tests are planned and designed to prepare you completely for the real Databricks exam condition. Free Databricks-Certified-Professional-Data-Engineer dumps demos can be available on customer’s demand to check before placing an order.


What Our Customers Say

Testimonial

Jeff Brown

Thanks you so much passin1day.com team for all the help that you have provided me in my Databricks exam. I will use your dumps for next certification as well.
Testimonial

Mareena Frederick

You guys are awesome. Even 1 day is too much. I prepared my exam in just 3 hours with your Databricks-Certified-Professional-Data-Engineer exam dumps and passed it in first attempt :)
Testimonial

Ralph Donald

I am the fully satisfied customer of passin1day.com. I have passed my exam using your Databricks Certified Data Engineer Professional braindumps in first attempt. You guys are the secret behind my success ;)
Testimonial

Lilly Solomon

I was so depressed when I get failed in my Cisco exam but thanks GOD you guys exist and helped me in passing my exams. I am nothing without you.