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

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

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q151-Q156):

問題 #151
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

答案：A

解題說明：
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.


問題 #152
Which statement regarding spark configuration on the Databricks platform is true?

答案：C

解題說明：
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.


問題 #153
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

答案：B

解題說明：
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.


問題 #154
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

答案：A

解題說明：
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time. However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.


問題 #155
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?

答案：A

解題說明：
Setting mergeSchema to true allows Delta Lake to automatically evolve the table schema by incorporating new columns from the source data during writes. This enables append-only pipelines to handle schema changes seamlessly in both batch and streaming workloads without breaking the pipeline.


問題 #156
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