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Databricks-Certified-Data-Engineer-Associate最新題庫 -最新Databricks-Certified-Data-Engineer-Associate考證

2025 VCESoft最新的Databricks-Certified-Data-Engineer-Associate PDF版考試題庫和Databricks-Certified-Data-Engineer-Associate考試問題和答案免費分享：https://drive.google.com/open?id=1gGrF_DCQGJnKdaI2uha-GXiayPV16T58

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最新Databricks-Certified-Data-Engineer-Associate考證 & Databricks-Certified-Data-Engineer-Associate熱門考題

在你的職業生涯中，你正面臨著挑戰嗎？你想提高自己的技能更好地向別人證明你自己嗎？你想得到更多的機會晉升嗎？那麼快報名參加IT認證考試獲得認證資格吧。Databricks的認證考試是IT領域很重要的考試之一，如果獲得Databricks的認證資格，那麼你就可以得到很大的幫助。你可以先從通過Databricks-Certified-Data-Engineer-Associate認證考試開始，因為這是Databricks的一個非常重要的考試。那麼，想知道怎麼快速地通過考試嗎？VCESoft的考試資料可以幫助你達到自己的目標。

最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q87-Q92):

問題 #87
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?

A. There is no way to share data between PySpark and SQL.
B. spark.sql
C. spark.delta.table
D. spark.table
E. SELECT * FROM sales

答案：B

解題說明：
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.

問題 #88
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

A. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
C. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
D. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
E. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.

答案：E

解題說明：
A streaming live table or view processes data that has been added only since the last pipeline update. Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

問題 #89
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(once="5 seconds")
B. trigger(continuous="5 seconds")
C. trigger("5 seconds")
D. trigger()
E. trigger(processingTime="5 seconds")

答案：E

解題說明：
Explanation
# ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream
format("console")
trigger(processingTime='2 seconds')
start()
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers

問題 #90
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

A. More information is needed to determine the correct response
B. dbfs:/user/hive/warehouse
C. dbfs:/user/hive/customer360
D. dbfs:/user/hive/database/customer360

答案：B

解題說明：
dbfs:/user/hive/warehouse Thereby showing "dbfs:/user/hive/warehouse/customer360.db The location of the customer360 database depends on the value of the spark.sql.warehouse.dir configuration property, which specifies the default location for managed databases and tables. If the property is not set, the default value is dbfs:/user/hive/warehouse. Therefore, the customer360 database will be located in dbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such as dbfs:/user/hive/database, then the customer360 database will be located in dbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.
Option B is not correct, as dbfs:/user/hive/warehouse is the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with .db to the directory path, such as dbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.
Reference:
Databases and Tables
[Databricks Data Engineer Professional Exam Guide]

問題 #91
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

A. GRANT VIEW ON CATALOG customers TO team;
B. GRANT USAGE ON DATABASE customers TO team;
C. GRANT USAGE ON CATALOG team TO customers;
D. GRANT CREATE ON DATABASE customers TO team;
E. GRANT CREATE ON DATABASE team TO customers;

答案：B

解題說明：
The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). Reference: GRANT, Privilege types, Securable objects, Principals

問題 #92
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Sean Rogers Sean Rogers

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