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Databricks Certified Professional Data Engineer Certification Exam is created to challenge data engineers with the significant knowledge of Databricks’ data engineering principles and techniques. To become Databricks certified, a candidate must pass the online certification exam designed for data engineers. Databricks-Certified-Professional-Data-Engineer Exam is scenario-based, comprises of 80 multiple-choice questions, and has a time limit of 120 minutes. The Certification exam tests the candidate's knowledge in topics such as data ingestion, data processing, data engineering, ETL, and data warehousing.
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Databricks Certified Professional Data Engineer is an exam designed for professionals who are willing to demonstrate their expertise in building and managing big data pipelines using Databricks. Databricks is a unified analytics platform that provides a collaborative environment for processing large-scale data. The Databricks Certified Professional Data Engineer exam validates the candidate's ability to design, build, and deploy large-scale data processing solutions using Databricks.
Databricks Certified Professional Data Engineer exam is a valuable certification for professionals who want to showcase their expertise in big data processing using Databricks. Databricks Certified Professional Data Engineer Exam certification demonstrates that the candidate has the necessary skills and knowledge to design and implement scalable data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification also provides a competitive advantage to professionals in the job market and opens up new career opportunities in the field of big data engineering.
NEW QUESTION # 180
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?
Answer: C
Explanation:
Explanation
The answer is Commit and push code.
See the below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workflow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Exam focus: Please study the below image carefully to understand all of the steps in the CI/CD flow to understand the tasks that are implemented in Databricks Repo vs Git Provider, exam may ask a different type of questions based on this flow.
Diagram Description automatically generated
NEW QUESTION # 181
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?
Answer: B
Explanation:
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
* CAN ATTACH TO:
* Allows users to attach notebooks to a cluster but does not allow them to view logs.
* Not sufficient for viewing driver logs.
* CAN MANAGE:
* Grants full control over the cluster, including starting, stopping, and editing configurations.
* Too broad for this requirement.
* CAN VIEW (Correct Answer):
* Allows users to view cluster details, logs, and status but not modify any configurations.
* Minimal required permission for viewing logs.
* CAN RESTART:
* Grants permission to restart the cluster, but does not include log access.
* Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
References:
Databricks Cluster Permissions Documentation
NEW QUESTION # 182
While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?
Answer: D
Explanation:
Explanation
The answer is Run SQL command DESCRIBE HISTORY table_name.
here is the sample data of how DESCRIBE HISTORY table_name looks
* +-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------
* |version| timestamp|userId|userName|operation| operationParameters|
job|notebook|clusterId|readVersion|isolationLevel|isBlindAppend| operationMetrics|
* +-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------
* | 5|2019-07-29 14:07:47| null| null| DELETE|[predicate -> ["(...|null| null| null| 4| Serializable| false|[numTotalRows -> ...|
* | 4|2019-07-29 14:07:41| null| null| UPDATE|[predicate -> (id...|null| null| null| 3| Serializable| false|[numTotalRows -> ...|
* | 3|2019-07-29 14:07:29| null| null| DELETE|[predicate -> ["(...|null| null| null| 2| Serializable| false|[numTotalRows -> ...|
* | 2|2019-07-29 14:06:56| null| null| UPDATE|[predicate -> (id...|null| null| null| 1| Serializable| false|[numTotalRows -> ...|
* | 1|2019-07-29 14:04:31| null| null| DELETE|[predicate -> ["(...|null| null| null| 0| Serializable| false|[numTotalRows -> ...|
* | 0|2019-07-29 14:01:40| null| null| WRITE|[mode -> ErrorIfE...|null| null| null| null| Serializable| true|[numFiles -> 2, n...|
+-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------------+
NEW QUESTION # 183
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
Answer: A
NEW QUESTION # 184
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?
Answer: C
NEW QUESTION # 185
......
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