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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.

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Certification Exam is a highly respected credential within the data engineering industry. Databricks Certified Professional Data Engineer Exam certification is specifically designed for professionals who have a deep understanding of data engineering principles, practices, and technologies. With this certification, data engineers can demonstrate their expertise in designing and building data pipelines, managing data workflows, and implementing data analytics solutions using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
You noticed a colleague is manually copying the data to the backup folder prior to running an up-date command, incase if the update command did not provide the expected outcome so he can use the backup copy to replace table, which Delta Lake feature would you recommend simplifying the process?

A. Use DEEP CLONE to clone the table prior to update to make a backup copy
B. Cloud object storage retains previous version of the file
C. Use time travel feature to refer old data instead of manually copying
D. Cloud object storage automatically backups the data
E. Use SHADOW copy of the table as preferred backup choice

Answer: C

Explanation:
Explanation
The answer is, Use time travel feature to refer old data instead of manually copying.
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000"

NEW QUESTION # 52
You are currently working on a notebook that will populate a reporting table for downstream process consumption, this process needs to run on a schedule every hour, what type of cluster are you going to use to set up this job?

A. Use Azure VM to read and write delta tables in Python
B. Use delta live table pipeline to run in continuous mode
C. Since it's just a single job and we need to run every hour, we can use an all-purpose cluster
D. The job cluster is best suited for this purpose.

Answer: D

Explanation:
Explanation
The answer is, The Job cluster is best suited for this purpose.
Since you don't need to interact with the notebook during the execution especially when it's a scheduled job, job cluster makes sense. Using an all-purpose cluster can be twice as expensive as a job cluster.
FYI,
When you run a job scheduler with option of creating a new cluster when the job is complete it terminates the cluster. You cannot restart a job cluster.

NEW QUESTION # 53
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. Use Pipeline creation UI, select a new pipeline and job cluster
B. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook
C. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file
D. DELTA LIVE TABLES do not support job cluster

Answer: B

Explanation:
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

NEW QUESTION # 54
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
B. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited

Answer: A

Explanation:
Explanation
The configuration that automatically recovers from query failures and keeps costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This means that the cluster resources are only used when the job is running, and no idle costs are incurred. This also ensures that the cluster is always in a clean state and has the latest configuration and libraries for the job1.
Setting retries to unlimited means that the job will automatically restart the query in case of any failure, such as network issues, node failures, or transient errors. This improves the reliability and availability of the streaming job, and avoids data loss or inconsistency2.
Setting maximum concurrent runs to 1 means that only one instance of the job can run at a time. This prevents multiple queries from competing for the same resources or writing to the same output location, which can cause performance degradation or data corruption3.
Therefore, this configuration is the best practice for scheduling Structured Streaming jobs for production, as it ensures that the job is resilient, efficient, and consistent.
References: Job clusters, Job retries, Maximum concurrent runs

NEW QUESTION # 55
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate tableused by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
B. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
C. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
D. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
E. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.

Answer: D

Explanation:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified References:
[Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CREATE VIEW" section.

NEW QUESTION # 56
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