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Databricks Associate-Developer-Apache-Spark 考試大綱主題:

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：