認定するDatabricks-Certified-Data-Engineer-Associate的中関連問題試験-試験の準備方法-素晴らしいDatabricks-Certified-Data-Engineer-Associate試験概要激変なネット情報時代で、質の良いDatabricksのDatabricks-Certified-Data-Engineer-Associate問題集を見つけるために、あなたは悩むことがありませんか。私たちは君がCertShikenを選ぶことと正確性の高いDatabricksのDatabricks-Certified-Data-Engineer-Associate問題集を祝っています。CertShikenのDatabricksのDatabricks-Certified-Data-Engineer-Associate問題集が君の認定試験に合格するのに大変役に立ちます。
GAQMのDatabricks認定データエンジニアアソシエイト（Databricks Certified Data Engineer Associate）試験は、Databricksを使用したデータエンジニアリングの専門知識を評価するために設計された認定プログラムです。この試験は、クラウドベースのデータ分析プラットフォームであるDatabricksを使用してビッグデータを扱うために必要な実践的な知識とスキルをテストするように設計されています。 Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q114-Q119):質問 # 114
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
正解：A
質問 # 115
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
A. spark.delta.table("sales")
B. SELECT * FROM sales
C. spark.table("sales")
D. spark.sql("sales")
E. There is no way to share data between PySpark and SQL.
正解：C
解説：
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.
forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation
質問 # 116
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
A. Option B
B. Option A
C. Option E
D. Option C
E. Option D
正解：B
解説：
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.
質問 # 117
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?
A. They can attach the cell to a SQL endpoint rather than a Databricks cluster
B. It is not possible to use SQL in a Python notebook
C. They can change the default language of the notebook to SQL
D. They can simply write SQL syntax in the cell
E. They can add %sql to the first line of the cell
正解：E
質問 # 118
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution.
Which compute option should the data engineer use?
無料でクラウドストレージから最新のCertShiken Databricks-Certified-Data-Engineer-Associate PDFダンプをダウンロードする：https://drive.google.com/open?id=1jCkxFk2R-Xqqf58gW6gBp7QF6SAohDjO Author: paulcar964 Time: 12 hour before
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