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

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

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q127-Q132):

질문 # 127
Your team has hundreds of jobs running but it is difficult to track cost of each job run, you are asked to provide a recommendation on how to monitor and track cost across various workloads

정답：D

설명：
Explanation
The answer is Use Tags, during job creation so cost can be easily tracked Review below link for more details
https://docs.databricks.com/administration-guide/account-settings/usage-detail-tags-aws.html Here is a view how tags get propagated from pools to clusters and clusters without pools, Diagram Description automatically generated


질문 # 128
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

정답：C

설명：
Explanation
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%20number,a


질문 # 129
The current ELT pipeline is receiving data from the operations team once a day so you had setup an AUTO LOADER process to run once a day using trigger (Once = True) and scheduled a job to run once a day, operations team recently rolled out a new feature that allows them to send data every 1 min, what changes do you need to make to AUTO LOADER to process the data every 1 min.

정답：E


질문 # 130
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

정답：B

설명：
Explanation
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.


질문 # 131
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?

정답：B

설명：
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).


질문 # 132
......

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