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

Which of the following code blocks returns DataFrame transactionsDf sorted in descending order by column predError, showing missing values last?

A. transactionsDf.orderBy("predError").desc_nulls_last()

B. transactionsDf.sort("predError", ascending=False)

C. transactionsDf.orderBy("predError").asc_nulls_last()

D. transactionsDf.desc_nulls_last("predError")

E. transactionsDf.sort(asc_nulls_last("predError"))

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：2

Which of the following statements about lazy evaluation is incorrect?

A. Execution is triggered by transformations.

B. Spark will fail a job only during execution, but not during definition.

C. Lineages allow Spark to coalesce transformations into stages

D. Predicate pushdown is a feature resulting from lazy evaluation.

E. Accumulators do not change the lazy evaluation model of Spark.

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：3

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：4

Which of the following code blocks returns a copy of DataFrame transactionsDf in which column productId has been renamed to productNumber?

A. transactionsDf.withColumn("productId", "productNumber")

B. transactionsDf.withColumnRenamed(col(productId), col(productNumber))

C. transactionsDf.withColumnRenamed("productId", "productNumber")

D. transactionsDf.withColumnRenamed(productId, productNumber)

E. transactionsDf.withColumnRenamed("productNumber", "productId")

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：5

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：6

Which of the following code blocks generally causes a great amount of network traffic?

A. DataFrame.rdd.map()

B. DataFrame.select()

C. DataFrame.collect()

D. DataFrame.count()

E. DataFrame.coalesce()

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：7

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.resample(0.15, False, 3142)

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

D. transactionsDf.sample(0.15)

E. transactionsDf.sample(0.85, 8429)

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：8

Which of the following code blocks returns a copy of DataFrame transactionsDf where the column storeId has been converted to string type?

A. transactionsDf.withColumn("storeId", convert("storeId").as("string"))

B. transactionsDf.withColumn("storeId", col("storeId").cast("string"))

C. transactionsDf.withColumn("storeId", col("storeId", "string"))

D. transactionsDf.withColumn("storeId", convert("storeId", "string"))

E. transactionsDf.withColumn("storeId", col("storeId").convert("string"))

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：9

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

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

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：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 data skew is incorrect?

A. In skewed DataFrames, the largest and the smallest partition consume very different amounts of memory.

B. To mitigate skew, Spark automatically disregards null values in keys when joining.

C. Broadcast joins are a viable way to increase join performance for skewed data over sort-merge joins.

D. Spark will not automatically optimize skew joins by default.

E. Salting can resolve data skew.

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：11

Which of the following code blocks returns a one-column DataFrame for which every row contains an array of all integer numbers from 0 up to and including the number given in column predError of DataFrame transactionsDf, and null if predError is null?
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.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+-------------+---------+-----+-------+---------+----+

A. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = [range(target)]
6. return result
7.
8.count_to_target_udf = udf(count_to_target, ArrayType[IntegerType])
9.
10.transactionsDf.select(count_to_target_udf(col('predError')))

B. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.transactionsDf.select(count_to_target(col('predError')))

C. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
9.
10.transactionsDf.select(count_to_target_udf('predError'))
(Correct)

D. 1.def count_to_target(target):
2. result = list(range(target))
3. return result
4.
5.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
6.
7.df = transactionsDf.select(count_to_target_udf('predError'))

E. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.count_to_target_udf = udf(count_to_target)
9.
10.transactionsDf.select(count_to_target_udf('predError'))

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：12

The code block shown below should return a DataFrame with two columns, itemId and col. In this DataFrame, for each element in column attributes of DataFrame itemDf there should be a separate row in which the column itemId contains the associated itemId from DataFrame itemsDf. The new DataFrame should only contain rows for rows in DataFrame itemsDf in which the column attributes contains the element cozy.
A sample of DataFrame itemsDf is below.
Code block:
itemsDf.__1__(__2__).__3__(__4__, __5__(__6__))

A. 1. filter
2. "array_contains(attributes, 'cozy')"
3. select
4. "itemId"
5. map
6. "attributes"

B. 1. where
2. "array_contains(attributes, 'cozy')"
3. select
4. itemId
5. explode
6. attributes

C. 1. filter
2. "array_contains(attributes, 'cozy')"
3. select
4. "itemId"
5. explode
6. "attributes"

D. 1. filter
2. array_contains("cozy")
3. select
4. "itemId"
5. explode
6. "attributes"

E. 1. filter
2. "array_contains(attributes, cozy)"
3. select
4. "itemId"
5. explode
6. "attributes"

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)

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