If you want to clear Databricks Certified Data Engineer Professional Exam on the first attempt, Passcert provides the best quality Databricks Certified Data Engineer Professional Certification Dumps that will help you in the right way. Our Databricks Certified Data Engineer Professional Certification Dumps will help you to understand these topics and obtain required skills & knowledge essential to pass the Databricks Certified Data Engineer Professional Exam. It is highly recommended for you to use Databricks Certified Data Engineer Professional Certification Dumps that will allow you to clear Databricks Certified Data Engineer Professional Exam on the first attempt.

The Databricks Certified Data Engineer Professional certification exam assesses an individual's ability to use Databricks to perform advanced data engineering tasks. This includes an understanding of the Databricks platform and developer tools like Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API. It also assesses the ability to build optimized and cleaned ETL pipelines. Additionally, modeling data into a Lakehouse using knowledge of general data modeling concepts will also be assessed. Finally, ensuring that data pipelines are secure, reliable, monitored, and tested before deployment will also be included in this exam. Individuals who pass this certification exam can be expected to complete advanced data engineering tasks using Databricks and its associated tools.

There are 60 multiple-choice questions on the Databricks Certified Data Engineer Professional certification exam.Testers will have 120 minutes to complete the certification exam. Each attempt of the certification exam will cost the tester $200. This certification exam's code examples will primarily be in Python. However, any and all references to Delta Lake functionality will be made in SQL. This certification is valid for 2 years following the date on which each tester passes the certification exam.

This certification is part of the Data Engineer learning pathway.

Databricks Tooling – 20% 

Data Processing – 30% 

Data Modeling – 20% 

Security and Governance – 10% 

Monitoring and Logging – 10% 

Testing and Deployment – 10%

A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.

The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL query returns the number of minutes since the job's most recent runtime.

Which of the following approaches can enable the data engineering team to be notified if the ELT job has not been run in an hour?

A. This type of alerting is not possible in Databricks

B. They can set up an Alert for the query to notify them if the returned value is greater than 60

C. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60minutes

D. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greaterthan 60

E.  They can set up an Alert for the query to notify when the ELT job fails

Answer: B

A data engineer wants to horizontally combine two tables as a part of a query. They want to use a shared column as a key column, and they only want the query result to contain rows whose value in the key column is present in both tables.

Which of the following SQL commands can they use to accomplish this task?

A. LEFT JOIN

B. INNER JOIN

C. MERGE

D. OUTER JOIN

E.  UNION

Answer: B

Which of the following data workloads will utilize a Bronze table as its source?

A. A job that queries aggregated data to publish key insights into a dashboard

B. A job that enriches data by parsing its timestamps into a human-readable format

C. A job that ingests raw data from a streaming source into the Lakehouse

D. A job that develops a feature set for a machine learning application

E.  A job that aggregates cleaned data to create standard summary statistics

Answer: B

A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The team is noticing that after appending new rows to the data in the external system, their queries within Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this issue.

Which of the following approaches will ensure that the data returned by queries is always up-to-date?

A. The tables should be stored in a cloud-based external system

B. The tables should be converted to the Delta format

C. The tables should be updated before the next query is run

D. The tables should be refreshed in the writing cluster before the next query is run

E. The tables should be altered to include metadata to not cache

Answer: B

A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue is affecting all of their sequentially run queries. They ask the data engineering team for help. The data engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not used by any other user.

Which of the following approaches can the data engineering team use to improve the latency of the data analyst's queries?

A. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to "Reliability Optimized"

B. They can increase the maximum bound of the SQL endpoint's scaling range

C. They can turn on the Auto Stop feature for the SQL endpoint

D. They can increase the cluster size of the SQL endpoint

E. They can turn on the Serverless feature for the SQL endpoint

Answer: D