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

SectionObjectives
Data Ingestion and Processing- ETL pipeline design patterns
- Batch and streaming ingestion with Auto Loader
- Structured Streaming fundamentals
Delta Lake and Data Management- Time travel and versioning
- Delta Lake transactions and ACID properties
- Schema evolution and enforcement
Databricks Lakehouse Platform Architecture- Data governance concepts (Unity Catalog basics)
- Medallion architecture (Bronze, Silver, Gold)
- Workspace and cluster architecture
Production Pipelines and Orchestration- Error handling and recovery strategies
- Job scheduling and monitoring
- Databricks Workflows
Data Modeling and Transformation- Spark SQL transformations
- Performance optimization techniques
- Dimensional modeling concepts

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q48-Q53):

質問 # 48
In order to facilitate near real-time workloads, a data engineer is creating a helper function to Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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?

正解：D

解説：
https://docs.databricks.com/en/ingestion/auto-loader/schema.html


質問 # 49
Which statement regarding spark configuration on the Databricks platform is true?

正解：B

解説：
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.


質問 # 50
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

正解：B

解説：
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.
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質問 # 51
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 invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints 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?

正解：B

解説：
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.


質問 # 52
A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

正解：E

解説：
High Concurrency clusters in Databricks are designed for multiple concurrent users and workloads. They provide fine-grained sharing of cluster resources and are optimized for operations such as running multiple parallel queries and updates. This would be suitable for a solution that involves many pipelines with parallel updates, especially with high volume and high velocity data.


質問 # 53
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