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 DataFrame with only columns from DataFrame transactionsDf for which there is a corresponding transactionId in DataFrame itemsDf. DataFrame itemsDf is very small and much smaller than DataFrame transactionsDf. The query should be executed in an optimized way. Choose the answer that correctly fills the blanks in the code block to accomplish this.
__1__.__2__(__3__, __4__, __5__)

A. 1. transactionsDf
2. join
3. itemsDf
4. transactionsDf.transactionId==itemsDf.transactionId
5. "anti"

B. 1. itemsDf
2. join
3. broadcast(transactionsDf)
4. "transactionId"
5. "left_semi"

C. 1. transactionsDf
2. join
3. broadcast(itemsDf)
4. transactionsDf.transactionId==itemsDf.transactionId
5. "outer"

D. 1. itemsDf
2. broadcast
3. transactionsDf
4. "transactionId"
5. "left_semi"

E. 1. transactionsDf
2. join
3. broadcast(itemsDf)
4. "transactionId"
5. "left_semi"

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：2

The code block shown below should return a DataFrame with all columns of DataFrame transactionsDf, but only maximum 2 rows in which column productId has at least the value 2. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__).__3__

A. 1. filter
2. col("productId") >= 2
3. limit(2)

B. 1. where
2. transactionsDf[productId] >= 2
3. limit(2)

C. 1. where
2. "productId" > 2
3. max(2)

D. 1. where
2. productId >= 2
3. limit(2)

E. 1. filter
2. productId > 2
3. max(2)

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

A. itemsDf.select(explode("attributes")).filter("attributes_exploded".contains("i"))

B. itemsDf.select(col("attributes").explode().alias("attributes_exploded")).filter(col("attributes_exploded").co

C. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(col("attributes_exploded").contain

D. itemsDf.explode(attributes).alias("attributes_exploded").filter(col("attributes_exploded").contains("i"))

E. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(attributes_exploded.contains("i"))

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：4

Which of the following code blocks applies the Python function to_limit on column predError in table transactionsDf, returning a DataFrame with columns transactionId and result?

A. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, to_limit(predError) AS result FROM transactionsDf") spark.sql("SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf")

B. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf") (Correct)

C. 1.spark.udf.register(to_limit, "LIMIT_FCN")
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf")

D. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result")

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

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)

出題：6

Which of the following code blocks reads all CSV files in directory filePath into a single DataFrame, with column names defined in the CSV file headers?
Content of directory filePath:
1._SUCCESS
2._committed_2754546451699747124
3._started_2754546451699747124
4.part-00000-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-298-1-c000.csv.gz
5.part-00001-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-299-1-c000.csv.gz
6.part-00002-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-300-1-c000.csv.gz
7.part-00003-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-301-1-c000.csv.gz spark.option("header",True).csv(filePath)

A. spark.read().option("header",True).load(filePath)

B. spark.read.format("csv").option("header",True).option("compression","zip").load(filePath)

C. spark.read.format("csv").option("header",True).load(filePath)

D. spark.read.load(filePath)

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 statements about DAGs is correct?

A. DAG stands for "Directing Acyclic Graph".

B. DAGs can be decomposed into tasks that are executed in parallel.

C. In contrast to transformations, DAGs are never lazily executed.

D. Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.

E. DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.

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：8

Which of the following code blocks reads in the JSON file stored at filePath, enforcing the schema expressed in JSON format in variable json_schema, shown in the code block below?
Code block:
1.json_schema = """
2.{"type": "struct",
3. "fields": [
4. {
5. "name": "itemId",
6. "type": "integer",
7. "nullable": true,
8. "metadata": {}
9. },
10. {
11. "name": "supplier",
12. "type": "string",
13. "nullable": true,
14. "metadata": {}
15. }
16. ]
17.}
18."""

A. spark.read.json(filePath, schema=schema_of_json(json_schema))

B. spark.read.json(filePath, schema=spark.read.json(json_schema))

C. spark.read.json(filePath, schema=json_schema)

D. spark.read.schema(json_schema).json(filePath)
1.schema = StructType.fromJson(json.loads(json_schema))
2.spark.read.json(filePath, schema=schema)

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：9

Which of the following describes properties of a shuffle?

A. Shuffles belong to a class known as "full transformations".

B. Operations involving shuffles are never evaluated lazily.

C. Shuffles involve only single partitions.

D. In a shuffle, Spark writes data to disk.

E. A shuffle is one of many actions in Spark.

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：10

Which of the following describes the role of the cluster manager?

A. The cluster manager allocates resources to the DataFrame manager.

B. The cluster manager schedules tasks on the cluster in client mode.

C. The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode.

D. The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode.

E. The cluster manager schedules tasks on the cluster in local mode.

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」