Associate-Developer-Apache-Sparkの無料問題集（179題）、Associate-Developer-Apache-Spark認定試験参考書

Databricks Associate-Developer-Apache-Spark 問題集

試験コード：Associate-Developer-Apache-Spark

試験名称：Databricks Certified Associate Developer for Apache Spark 3.0 Exam

最近更新時間：2024-12-16

問題と解答：全179問

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無料問題集Associate-Developer-Apache-Spark 資格取得

質問 1：
Which of the following code blocks performs a join in which the small DataFrame transactionsDf is sent to all executors where it is joined with DataFrame itemsDf on columns storeId and itemId, respectively?
A. itemsDf.join(transactionsDf, broadcast(itemsDf.itemId == transactionsDf.storeId))
B. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "broadcast")
C. itemsDf.join(broadcast(transactionsDf), itemsDf.itemId == transactionsDf.storeId)
D. itemsDf.merge(transactionsDf, "itemsDf.itemId == transactionsDf.storeId", "broadcast")
E. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "right_outer")
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
Which of the following describes Spark's Adaptive Query Execution?
A. Adaptive Query Execution features are dynamically switching join strategies and dynamically optimizing skew joins.
B. Adaptive Query Execution is enabled in Spark by default.
C. Adaptive Query Execution reoptimizes queries at execution points.
D. Adaptive Query Execution features include dynamically coalescing shuffle partitions, dynamically injecting scan filters, and dynamically optimizing skew joins.
E. Adaptive Query Execution applies to all kinds of queries.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
Which of the following DataFrame methods is classified as a transformation?
A. DataFrame.count()
B. DataFrame.first()
C. DataFrame.select()
D. DataFrame.show()
E. DataFrame.foreach()
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Which of the following describes a difference between Spark's cluster and client execution modes?
A. In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.
B. In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.
C. In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.
D. In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.
E. In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 6：
The code block displayed below contains multiple errors. The code block should remove column transactionDate from DataFrame transactionsDf and add a column transactionTimestamp in which dates that are expressed as strings in column transactionDate of DataFrame transactionsDf are converted into unix timestamps. Find the errors.
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+----------------+
2.|transactionId|predError|value|storeId|productId| f| transactionDate|
3.+-------------+---------+-----+-------+---------+----+----------------+
4.| 1| 3| 4| 25| 1|null|2020-04-26 15:35|
5.| 2| 6| 7| 2| 2|null|2020-04-13 22:01|
6.| 3| 3| null| 25| 3|null|2020-04-02 10:53|
7.+-------------+---------+-----+-------+---------+----+----------------+ Code block:
1.transactionsDf = transactionsDf.drop("transactionDate")
2.transactionsDf["transactionTimestamp"] = unix_timestamp("transactionDate", "yyyy-MM-dd")
A. Column transactionDate should be dropped after transactionTimestamp has been written. The withColumn operator should be used instead of the existing column assignment. Column transactionDate should be wrapped in a col() operator.
B. Column transactionDate should be wrapped in a col() operator.
C. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment. Operator to_unixtime() should be used instead of unix_timestamp().
D. The string indicating the date format should be adjusted. The withColumnReplaced operator should be used instead of the drop and assign pattern in the code block to replace column transactionDate with the new column transactionTimestamp.
E. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment.
正解：E
解説: (Topexam メンバーにのみ表示されます)

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Databricks Certified Associate Developer for Apache Spark 3.0 認定 Associate-Developer-Apache-Spark 試験問題:

1. Which of the following code blocks returns a DataFrame where columns predError and productId are removed from DataFrame transactionsDf?
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.dropColumns("predError", "productId", "associateId")
B) transactionsDf.drop(col("predError", "productId"))
C) transactionsDf.drop("predError", "productId", "associateId")
D) transactionsDf.drop(["predError", "productId", "associateId"])
E) transactionsDf.withColumnRemoved("predError", "productId")

2. The code block displayed below contains an error. The code block should save DataFrame transactionsDf at path path as a parquet file, appending to any existing parquet file. Find the error.
Code block:

A) The mode option should be omitted so that the command uses the default mode.
B) The code block is missing a reference to the DataFrameWriter.
C) The code block is missing a bucketBy command that takes care of partitions.
D) save() is evaluated lazily and needs to be followed by an action.
E) transactionsDf.format("parquet").option("mode", "append").save(path)
F) Given that the DataFrame should be saved as parquet file, path is being passed to the wrong method.

3. Which of the following code blocks returns a DataFrame that has all columns of DataFrame transactionsDf and an additional column predErrorSquared which is the squared value of column predError in DataFrame transactionsDf?

A) transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))
B) transactionsDf.withColumnRenamed("predErrorSquared", pow(predError, 2))
C) transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))
D) transactionsDf.withColumn("predErrorSquared", "predError"**2)
E) transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))

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

5. 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. select
2. "transactionId", "supplier"
3. join
4. [transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId]
B) 1. join
2. transactionsDf.productId==itemsDf.itemId, how="inner"
3. select
4. "transactionId", "supplier"
C) 1. join
2. transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId
3. filter
4. "transactionId", "supplier"
D) 1. join
2. [transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId]
3. select
4. "transactionId", "supplier"
E) 1. filter
2. "transactionId", "supplier"
3. join
4. "transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId"