AUTHORIZED DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER TEST DUMPS, DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM GUIDE MATERIALS

Authorized Databricks-Certified-Professional-Data-Engineer Test Dumps, Databricks-Certified-Professional-Data-Engineer Exam Guide Materials

Authorized Databricks-Certified-Professional-Data-Engineer Test Dumps, Databricks-Certified-Professional-Data-Engineer Exam Guide Materials

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
A team member is leaving the team and he/she is currently the owner of the few tables, instead of transfering the ownership to a user you have decided to transfer the ownership to a group so in the future anyone in the group can manage the permissions rather than a single individual, which of the following commands help you accomplish this?

  • A. ALTER TABLE table_name OWNER to 'group'
  • B. TRANSFER OWNER table_name to 'group'
  • C. GRANT OWNER table_name to 'group'*
  • D. ALTER OWNER ON table_name to 'group'
  • E. GRANT OWNER On table_name to 'group'

Answer: A

Explanation:
Explanation
The answer is ALTER TABLE table_name OWNER to 'group'
Assign owner to object


NEW QUESTION # 126
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

  • A. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
  • B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
  • C. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
  • D. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.

Answer: B

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
References:
* Databricks documentation on using SQL views in Delta Lake:
https://docs.databricks.com/delta/quick-start.html#sql-views


NEW QUESTION # 127
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

  • A. Control plane
  • B. JDBC data source
  • C. Data plane
  • D. Databricks Filesystem
  • E. Databricks web application

Answer: C

Explanation:
Explanation
The answer is Data Plane, which is where compute(all-purpose, Job Cluster, DLT) are stored this is generally a customer cloud account, there is one exception SQL Warehouses, currently there are 3 types of SQL Warehouse compute available(classic, pro, serverless), in classic and pro compute is located in customer cloud account but serverless computed is located in Databricks cloud account.
Diagram, timeline Description automatically generated


NEW QUESTION # 128
Once a cluster is deleted, below additional actions need to performed by the administrator

  • A. Remove networking but Virtual machines and storage disks are automatically dropped
  • B. Remove virtual machines but storage and networking are automatically dropped
  • C. No action needs to be performed. All resources are automatically removed.
  • D. Remove logs
  • E. Drop storage disks but Virtual machines and networking are automatically dropped

Answer: C

Explanation:
Explanation
What is Delta?
Delta lake is
* Open source
* Builds up on standard data format
* Optimized for cloud object storage
* Built for scalable metadata handling
Delta lake is not
* Proprietary technology
* Storage format
* Storage medium
* Database service or data warehouse


NEW QUESTION # 129
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?

  • A. Updating the table schema will invalidate the Delta transaction log metadata.
  • B. New fields not be computed for historic records.
  • C. Updating the table schema requires a default value provided for each file added.
  • D. Spark cannot capture the topic partition fields from the kafka source.

Answer: B

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.
References:
* Databricks documentation on Delta Lake schema management:
https://docs.databricks.com/delta/delta-batch.html#schema-management


NEW QUESTION # 130
......

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