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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Objectives
Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines
Data Modeling and Storage	- Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization
Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads
Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
What statement is true regarding the retention of job run history?

A. It is retained until you export or delete job run logs
B. It is retained for 90 days or until the run-id is re-used through custom run configuration
C. t is retained for 60 days, during which you can export notebook run results to HTML
D. It is retained for 60 days, after which logs are archived
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Answer: C

NEW QUESTION # 61
Create a schema called bronze using location '/mnt/delta/bronze', and check if the schema exists before creating.

A. Cannot create schema without a database
B. if IS_SCHEMA('bronze'): CREATE SCHEMA bronze LOCATION '/mnt/delta/bronze'
C. Schema creation is not available in metastore, it can only be done in Unity catalog UI
D. CREATE SCHEMA IF NOT EXISTS bronze LOCATION '/mnt/delta/bronze'
E. CREATE SCHEMA bronze IF NOT EXISTS LOCATION '/mnt/delta/bronze'

Answer: D

Explanation:
Explanation
https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-create-schema.html
1.CREATE SCHEMA [ IF NOT EXISTS ] schema_name [ LOCATION schema_directory ]

NEW QUESTION # 62
Which statement regarding stream-static joins and static Delta tables is correct?

A. Stream-static joins cannot use static Delta tables because of consistency issues.
B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
C. The checkpoint directory will be used to track state information for the unique keys present in the join.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
E. The checkpoint directory will be used to track updates to the static Delta table.

Answer: B

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 63
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can run
B. Can manage
C. Can edit
D. Can Read

Answer: D

Explanation:
Granting a user ' Can Read ' permissions on a notebook within Databricks allows them to view the notebook ' s content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook ' s code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.: Databricks documentation on access control and permissions for notebooks within the workspace (
https://docs.databricks.com/security/access-control/workspace-acl.html ).

NEW QUESTION # 64
Which of the following is correct for the global temporary view?

A. global temporary views can be still accessed even if the cluster is restarted
B. global temporary views can be still accessed even if the notebook is detached and at-tached
C. global temporary views can be accessed across many clusters
D. global temporary views cannot be accessed once the notebook is detached and attached
E. global temporary views are created in a database called temp database

Answer: B

Explanation:
Explanation
The answer is global temporary views can be still accessed even if the notebook is detached and attached There are two types of temporary views that can be created Local and Global
* A local temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
* A global temporary view is available to all the notebooks in the cluster, even if the notebook is detached and reattached it can still be accessible but if a cluster is restarted the global temporary view is lost.

NEW QUESTION # 65
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