Exam Questions Databricks-Certified-Professional-Data-Engineer Vce, Databricks-Certified-Professional-Data-Engineer Authentic Exam Questions

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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
Unity catalog helps you manage the below resources in Databricks at account level

Answer: E

Explanation:
Explanation
The answer is all of the above.
Unity Catalog is a unified governance solution for all data and AI assets including files, tables, machine learning models, and dashboards in your lakehouse on any cloud.


NEW QUESTION # 93
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

Answer: A

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the "cloudFiles.schemaLocation" option, which is required for the schema detection and evolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. Reference:
Configure schema inference and evolution in Auto Loader: https://docs.databricks.com/en/ingestion/auto-loader/schema.html Write streaming data: https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html


NEW QUESTION # 94
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

Answer: E

Explanation:
Explanation
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table


NEW QUESTION # 95
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

Answer: A

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the
"cloudFiles.schemaLocation" option, which is required for the schema detection andevolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html
* Write streaming data:
https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html


NEW QUESTION # 96
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

Answer: A

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
Reference:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.html#schema-evolution


NEW QUESTION # 97
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