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：2025-07-02

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 statements about DAGs is correct?
A. In contrast to transformations, DAGs are never lazily executed.
B. DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.
C. DAGs can be decomposed into tasks that are executed in parallel.
D. DAG stands for "Directing Acyclic Graph".
E. Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.
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 is a characteristic of the cluster manager?
A. The cluster manager transforms jobs into DAGs.
B. The cluster manager does not exist in standalone mode.
C. Each cluster manager works on a single partition of data.
D. In client mode, the cluster manager runs on the edge node.
E. The cluster manager receives input from the driver through the SparkContext.
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 3：
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.schema(json_schema).json(filePath)
1.schema = StructType.fromJson(json.loads(json_schema))
2.spark.read.json(filePath, schema=schema)
B. spark.read.json(filePath, schema=schema_of_json(json_schema))
C. spark.read.json(filePath, schema=spark.read.json(json_schema))
D. spark.read.json(filePath, schema=json_schema)
正解：A
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 should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
A. The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
B. The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.
C. The copy() operator should be appended to the code block to ensure a copy is returned.
D. The arguments to the withColumn method need to be reordered.
E. Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 5：
The code block displayed below contains an error. The code block should read the csv file located at path data/transactions.csv into DataFrame transactionsDf, using the first row as column header and casting the columns in the most appropriate type. Find the error.
First 3 rows of transactions.csv:
1.transactionId;storeId;productId;name
2.1;23;12;green grass
3.2;35;31;yellow sun
4.3;23;12;green grass
Code block:
transactionsDf = spark.read.load("data/transactions.csv", sep=";", format="csv", header=True)
A. The resulting DataFrame will not have the appropriate schema.
B. Spark is unable to understand the file type.
C. The transaction is evaluated lazily, so no file will be read.
D. The code block is unable to capture all columns.
E. The DataFrameReader is not accessed correctly.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Which of the following statements about data skew is incorrect?
A. To mitigate skew, Spark automatically disregards null values in keys when joining.
B. In skewed DataFrames, the largest and the smallest partition consume very different amounts of memory.
C. Salting can resolve data skew.
D. Spark will not automatically optimize skew joins by default.
E. Broadcast joins are a viable way to increase join performance for skewed data over sort-merge joins.
正解：A
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. The code block shown below should convert up to 5 rows in DataFrame transactionsDf that have the value 25 in column storeId into a Python list. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__).__3__(__4__)

A) 1. filter
2. "storeId"==25
3. collect
4. 5
B) 1. select
2. storeId==25
3. head
4. 5
C) 1. filter
2. col("storeId")==25
3. collect
4. 5
D) 1. filter
2. col("storeId")==25
3. take
4. 5
E) 1. filter
2. col("storeId")==25
3. toLocalIterator
4. 5

2. The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:

A) Spark will only apply the limit to threshold joins and not to other joins.
B) The passed limit has the wrong variable type.
C) The correct option to write configurations is through spark.config and not spark.conf.
D) spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 20)
E) Spark will only broadcast DataFrames that are much smaller than the default value.
F) The command is evaluated lazily and needs to be followed by an action.

3. The code block shown below should return only the average prediction error (column predError) of a random subset, without replacement, of approximately 15% of rows in DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(avg('predError'))

A) 1. fraction
2. 0.15
3. True
4. where
B) 1. sample
2. False
3. 0.15
4. select
C) 1. fraction
2. False
3. 0.85
4. select
D) 1. sample
2. 0.85
3. False
4. select
E) 1. sample
2. True
3. 0.15
4. filter

4. 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.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
9.
10.transactionsDf.select(count_to_target_udf('predError'))
(Correct)
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.transactionsDf.select(count_to_target(col('predError')))
D) 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'))
E) 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'))

5. Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

A) from pyspark import StorageLevel
transactionsDf.persist(StorageLevel.MEMORY_ONLY)
B) transactionsDf.clear_persist()
C) transactionsDf.persist()
D) transactionsDf.cache()
E) transactionsDf.storage_level('MEMORY_ONLY')
F) from pyspark import StorageLevel
transactionsDf.cache(StorageLevel.MEMORY_ONLY)