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Associate-Developer-Apache-Spark學習資料的內容全部由行業專家根據多年來的考試大綱和行業發展趨勢編制而成。它與市場上問題庫的內容不重疊，避免了反复練習引起的疲勞。 Associate-Developer-Apache-Spark考試指南不是一個拼湊的測試題，而是有自己的系統和層次結構，可以使用戶有效地提高效率。我們的學習材料包含由考試專家根據不同科目的特點和範圍編寫的試題。模擬真實的Databricks Certified Associate Developer for Apache Spark 3.0 Exam測試環境。測試結束後，系統還會給出總分和正確率。

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在此之前，您可能需要數月甚至一年的時間來準備專業考試，但使用Associate-Developer-Apache-Spark考試指南，您只需要在考試前花費20-30小時進行複習即可。並且使用我們的學習材料，您將不再需要任何其他復習材料，因為我們的學習材料已包含所有重要的測試點。與此同時，Associate-Developer-Apache-Spark學習材料將為您提供全新的學習方法 - 讓您練習過程中的掌握知識。有許多人因閱讀書籍而感到頭疼，因為裡面有很多難以理解的知識。與此同時，教科書中那些無聊的描述常常讓人感到困倦。但是使用Associate-Developer-Apache-Spark測試題庫：Databricks Certified Associate Developer for Apache Spark 3.0 Exam，你將不再有這些煩惱。

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最新的 Databricks Certification Associate-Developer-Apache-Spark 免費考試真題:

1. The code block displayed below contains an error. The code block should configure Spark to split data in 20 parts when exchanging data between executors for joins or aggregations. Find the error.
Code block:
spark.conf.set(spark.sql.shuffle.partitions, 20)

A) The code block is missing a parameter.
B) The code block uses the wrong command for setting an option.
C) The code block expresses the option incorrectly.
D) The code block sets the incorrect number of parts.
E) The code block sets the wrong option.

2. The code block shown below should write DataFrame transactionsDf to disk at path csvPath as a single CSV file, using tabs (\t characters) as separators between columns, expressing missing values as string n/a, and omitting a header row with column names. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.write.__2__(__3__, " ").__4__.__5__(csvPath)

A) 1. coalesce(1)
2. option
3. "colsep"
4. option("nullValue", "n/a")
5. path
B) 1. repartition(1)
2. option
3. "sep"
4. option("nullValue", "n/a")
5. csv
(Correct)
C) 1. csv
2. option
3. "sep"
4. option("emptyValue", "n/a")
5. path
* 1. repartition(1)
2. mode
3. "sep"
4. mode("nullValue", "n/a")
5. csv
D) 1. coalesce(1)
2. option
3. "sep"
4. option("header", True)
5. path

3. The code block displayed below contains an error. The code block is intended to perform an outer join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively.
Find the error.
Code block:
transactionsDf.join(itemsDf, [itemsDf.itemId, transactionsDf.productId], "outer")

A) The join type needs to be appended to the join() operator, like join().outer() instead of listing it as the last argument inside the join() call.
B) The "outer" argument should be eliminated from the call and join should be replaced by joinOuter.
C) The term [itemsDf.itemId, transactionsDf.productId] should be replaced by itemsDf.col("itemId") == transactionsDf.col("productId").
D) The term [itemsDf.itemId, transactionsDf.productId] should be replaced by itemsDf.itemId == transactionsDf.productId.
E) The "outer" argument should be eliminated, since "outer" is the default join type.

4. The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.__1__(__2__).select(__3__, __4__)

A) 1. where
2. col("supplier").contains("Sports")
3. "itemName"
4. "attributes"
B) 1. where
2. col(supplier).contains("Sports")
3. explode(attributes)
4. itemName
C) 1. filter
2. col("supplier").isin("Sports")
3. "itemName"
4. explode(col("attributes"))
D) 1. where
2. "Sports".isin(col("Supplier"))
3. "itemName"
4. array_explode("attributes")
E) 1. filter
2. col("supplier").contains("Sports")
3. "itemName"
4. explode("attributes")

5. Which of the elements in the labeled panels represent the operation performed for broadcast variables?
Larger image

A) 2, 3
B) 3
C) 1, 3, 4
D) 1, 2
E) 2, 5

問題與答案：