Databricks-Certified-Data-Engineer-Associate證照資訊 & Databricks-Certified-Data-Engineer-Associate在線題庫

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

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

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q213-Q218):

問題 #213
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

答案：D

解題說明：
1: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources


問題 #214
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：C

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


問題 #215
Which of the following describes a scenario in which a data team will want to utilize cluster pools?

答案：B

解題說明：
Cluster pools in Databricks are used to ensure that a set of pre-warmed clusters is readily available to run workloads. This means that when a job is submitted, it can be executed more quickly because there is no need to wait for a cluster to spin up. Therefore, if a data team needs to refresh an automated report as quickly as possible, they will want to utilize cluster pools to ensure that the job can be executed as quickly as possible.


問題 #216
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

答案：A


問題 #217
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?

答案：C

解題說明：
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
:
1: OPTIMIZE | Databricks on AWS
2: REDUCE | Databricks on AWS
3: repartition | Databricks on AWS
4: VACUUM | Databricks on AWS


問題 #218
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Databricks-Certified-Data-Engineer-Associate在線題庫: https://www.vcesoft.com/Databricks-Certified-Data-Engineer-Associate-pdf.html

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