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

質問 1

Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?

（A）Use a wide transformation to reduce the number of partitions.
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.

（B）Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.

（C）Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.

（D）Use a narrow transformation to reduce the number of partitions.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:

（A）transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))

（B）transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))

（C）transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))

（D）transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

（E）transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

In which order should the code blocks shown below be run in order to create a table of all values in column attributes next to the respective values in column supplier in DataFrame itemsDf?
1. itemsDf.createOrReplaceView("itemsDf")
2. spark.sql("FROM itemsDf SELECT 'supplier', explode('Attributes')")
3. spark.sql("FROM itemsDf SELECT supplier, explode(attributes)")
4. itemsDf.createOrReplaceTempView("itemsDf")

（A）4, 2

（B）1, 2

（C）1, 3

（D）4, 3

（E）2

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

Which of the following describes characteristics of the Spark driver?

（A）The Spark driver requests the transformation of operations into DAG computations from the worker nodes.

（B）If set in the Spark configuration, Spark scales the Spark driver horizontally to improve parallel processing performance.

（C）The Spark driver processes partitions in an optimized, distributed fashion.

（D）The Spark driver's responsibility includes scheduling queries for execution on worker nodes.

（E）In a non-interactive Spark application, the Spark driver automatically creates the SparkSession object.

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

Which of the following code blocks returns a single row from DataFrame transactionsDf?
Full 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.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+-------------+---------+-----+-------+---------+----+

（A）transactionsDf.filter(col("storeId")==25).select("predError","storeId").distinct()

（B）transactionsDf.where(col("storeId").between(3,25))

（C）transactionsDf.select("productId", "storeId").where("storeId == 2 OR storeId != 25")

（D）transactionsDf.where(col("value").isNull()).select("productId", "storeId").distinct()

（E）transactionsDf.filter((col("storeId")!=25) | (col("productId")==2))

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

Which of the following code blocks returns about 150 randomly selected rows from the 1000-row DataFrame transactionsDf, assuming that any row can appear more than once in the returned DataFrame?

（A）transactionsDf.sample(0.15, False, 3142)

（B）transactionsDf.sample(0.85, 8429)

（C）transactionsDf.sample(0.15)

（D）transactionsDf.sample(True, 0.15, 8261)

（E）transactionsDf.resample(0.15, False, 3142)

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

Which of the following statements about the differences between actions and transformations is correct?

（A）Actions generate RDDs, while transformations do not.

（B）Actions do not send results to the driver, while transformations do.

（C）Actions can be queued for delayed execution, while transformations can only be processed immediately.

（D）Actions are evaluated lazily, while transformations are not evaluated lazily.

（E）Actions can trigger Adaptive Query Execution, while transformation cannot.

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

The code block shown below should return a two-column DataFrame with columns transactionId and supplier, with combined information from DataFrames itemsDf and transactionsDf. The code block should merge rows in which column productId of DataFrame transactionsDf matches the value of column itemId in DataFrame itemsDf, but only where column storeId of DataFrame transactionsDf does not match column itemId of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(itemsDf, __2__).__3__(__4__)

（A）1. filter
2. "transactionId", "supplier"
3. join
4. "transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId"

（B）1. join
2. transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId
3. filter
4. "transactionId", "supplier"

（C）1. join
2. transactionsDf.productId==itemsDf.itemId, how="inner"
3. select
4. "transactionId", "supplier"

（D）1. select
2. "transactionId", "supplier"
3. join
4. [transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId]

（E）1. join
2. [transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId]
3. select
4. "transactionId", "supplier"

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

Which of the following describes a valid concern about partitioning?

（A）Short partition processing times are indicative of low skew.

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

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

（D）A shuffle operation returns 200 partitions if not explicitly set.

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

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

Which of the following is the deepest level in Spark's execution hierarchy?

（A）Executor

（B）Slot

（C）Task

（D）Stage

（E）Job

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

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

弊社を連絡する

関連リンク

トップ試験