Associate-Developer-Apache-Spark試験無料問題集（179題）「Databricks Certified Associate Developer for Apache Spark 3.0 認定」

出題：1

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. filter
2. col("supplier").contains("Sports")
3. "itemName"
4. explode("attributes")

B. 1. filter
2. col("supplier").isin("Sports")
3. "itemName"
4. explode(col("attributes"))

C. 1. where
2. "Sports".isin(col("Supplier"))
3. "itemName"
4. array_explode("attributes")

D. 1. where
2. col("supplier").contains("Sports")
3. "itemName"
4. "attributes"

E. 1. where
2. col(supplier).contains("Sports")
3. explode(attributes)
4. itemName

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

Which of the following statements about stages is correct?

A. Stages may contain multiple actions, narrow, and wide transformations.

B. Stages ephemerally store transactions, before they are committed through actions.

C. Tasks in a stage may be executed by multiple machines at the same time.

D. Different stages in a job may be executed in parallel.

E. Stages consist of one or more jobs.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

Which of the following code blocks immediately removes the previously cached DataFrame transactionsDf from memory and disk?

A. transactionsDf.clearCache()

B. del transactionsDf

C. transactionsDf.persist()

D. array_remove(transactionsDf, "*")

E. transactionsDf.unpersist()
(Correct)

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.+-------------+---------+-----+-------+---------+----+

A. transactionsDf.drop(["predError", "value"])

B. transactionsDf.drop("predError", "value")

C. transactionsDf.drop(value, predError)

D. transactionsDf.drop(col("value"), col("predError"))

E. transactionsDf.drop([col("predError"), col("value")])

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that correctly fills the blanks in the code block to accomplish this.

A. transactionsDf.remove(transactionsDf.storeId==25)

B. transactionsDf.drop(transactionsDf.storeId==25)

C. transactionsDf.filter(transactionsDf.storeId==25)

D. transactionsDf.select(transactionsDf.storeId!=25)

E. transactionsDf.where(transactionsDf.storeId!=25)

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

The code block shown below should return a copy of DataFrame transactionsDf without columns value and productId and with an additional column associateId that has the value 5. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(__5__, 'value')

A. 1. withColumn
2. 'associateId'
3. lit(5)
4. drop
5. 'productId'

B. 1. withColumnRenamed
2. 'associateId'
3. 5
4. drop
5. 'productId'

C. 1. withColumn
2. col(associateId)
3. lit(5)
4. drop
5. col(productId)

D. 1. withColumn
2. 'associateId'
3. 5
4. remove
5. 'productId'

E. 1. withNewColumn
2. associateId
3. lit(5)
4. drop
5. productId

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

Which of the following statements about garbage collection in Spark is incorrect?

A. Garbage collection information can be accessed in the Spark UI's stage detail view.

B. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.

C. Serialized caching is a strategy to increase the performance of garbage collection.

D. Manually persisting RDDs in Spark prevents them from being garbage collected.

E. Optimizing garbage collection performance in Spark may limit caching ability.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

Which of the following describes a valid concern about partitioning?

A. The coalesce() method should be used to increase the number of partitions.

B. Short partition processing times are indicative of low skew.

C. No data is exchanged between executors when coalesce() is run.

D. Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.

E. A shuffle operation returns 200 partitions if not explicitly set.

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

Which of the following describes Spark's Adaptive Query Execution?

A. Adaptive Query Execution applies to all kinds of queries.

B. Adaptive Query Execution features are dynamically switching join strategies and dynamically optimizing skew joins.

C. Adaptive Query Execution is enabled in Spark by default.

D. Adaptive Query Execution features include dynamically coalescing shuffle partitions, dynamically injecting scan filters, and dynamically optimizing skew joins.

E. Adaptive Query Execution reoptimizes queries at execution points.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

Which of the following statements about broadcast variables is correct?

A. Broadcast variables are immutable.

B. Broadcast variables are commonly used for tables that do not fit into memory.

C. Broadcast variables are local to the worker node and not shared across the cluster.

D. Broadcast variables are serialized with every single task.

E. Broadcast variables are occasionally dynamically updated on a per-task basis.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

Associate-Developer-Apache-Spark試験無料問題集「Databricks Certified Associate Developer for Apache Spark 3.0 認定」