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

Section	Weight	Objectives
Topic 1: Delta Lake Fundamentals	20-25%	- Create and manage Delta tables - Write to and read from Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel
Topic 2: Data Pipeline Architecture	15-20%	- Implement incremental data processing - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns
Topic 3: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Topic 4: Apache Spark Data Processing Fundamentals	20-25%	- Create and use Spark DataFrames - Use Spark SQL for data processing - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs)
Topic 5: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs)
Topic 6: Spark SQL and DataFrames	15-20%	- Write and execute Spark SQL queries - Aggregate and group data - Handle null values and data quality - Join and union DataFrames

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q143-Q148):

問題 #143
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. Q They can turn on the Auto Stop feature for the SQL endpoint.
B. O They can set up the dashboard's SQL endpoint to be serverless.
C. O They can reduce the cluster size of the SQL endpoint.
D. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

答案：A

解題說明：
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.

問題 #144
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

答案：D

解題說明：
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

問題 #145
A data engineer observes that an ETL job in Databricks has increased its execution time from a five-minute baseline to 12 minutes over the past week. Using the Lakeflow Jobs run-history view, the engineer needs to identify whether the slowdown is consistent or intermittent and determine its root cause.
Which action should the engineer take to analyze execution-time trends against the baseline?

A. Automatically increase the cluster size whenever execution time exceeds the previous run's duration.
B. Archive old job runs and reset the baseline counter to ensure an accurate comparison of current performance.
C. Compare cluster metrics in the Lakeflow Jobs run history to identify resource spikes that are causing delays.
D. Rerun the job immediately to establish a new baseline because historical data becomes irrelevant after seven days.

答案：C

問題 #146
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The DELETE HISTORY command was run on the table
B. The VACUUM command was run on the table
C. The OPTIMIZE command was nun on the table
D. The HISTORY command was run on the table
E. The TIME TRAVEL command was run on the table

答案：B

解題說明：
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. Reference: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]

問題 #147
A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?

A. failOnNewColumns
B. none
C. addNewColumns
D. rescue

答案：A

問題 #148
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