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

SectionObjectives
Topic 1: Data Governance and Quality- Data access control and governance
- Unity Catalog basics
- Data quality concepts and management
Topic 2: Productionizing Data Pipelines- Pipeline deployment and operationalization
- Databricks Workflows / Jobs orchestration
- Scheduling and monitoring jobs
Topic 3: Databricks Lakehouse Platform Fundamentals- Clusters, notebooks, and basic Databricks environment usage
- Workspace, architecture, and core platform concepts
Topic 4: Data Processing and Transformations- User-defined functions (UDFs)
- Apache Spark SQL operations (joins, aggregations, filtering)
- Delta Lake fundamentals (tables, transactions, optimization)
- PySpark DataFrame transformations
Topic 5: Data Ingestion and ELT Development- Data ingestion using Spark SQL and PySpark
- Handling structured and semi-structured data
- ETL patterns and transformations

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q139-Q144):

質問 # 139
A data engineer wants to delete records from a Delta table where the status column equals
"inactive". The operation should modify only the affected rows without rewriting the entire dataset.
Which SQL command should be used?

正解：B


質問 # 140
An organization needs to share a dataset stored in its Databricks Unity Catalog with an external partner who uses a different data platform that is not Databricks. The goal is to maintain data security and ensure the partner can access the data efficiently. Which method should the data engineer use to securely share the dataset with the external partner?

正解：B

解説：
Delta Sharing with the open sharing protocol allows secure data sharing from Unity Catalog to external partners, even if they are not using Databricks, while maintaining governance and efficient access.


質問 # 141
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

正解：E


質問 # 142
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

正解：C

解説：
The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql("SELECT * FROM sales") to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A). References: Databricks Documentation - Run SQL queries, Databricks Documentation - Spark SQL and DataFrames.


質問 # 143
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT. Which code snippet should the data engineer use?

正解：B

解説：
The correct approach is to use a DLT expectation with constraint CONSTRAINT valid_date EXPECT (order_datetime IS NOT NULL) ON VIOLATION DROP ROW This ensures rows with NULL order_datetime are filtered out automatically during streaming ingestion while keeping the pipeline valid.


質問 # 144
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