[2026] Databricks Certified Associate Developer for Apache Spark 3.0 - Associate-Developer-Apache-Spark 무료 시험 문제

문제1

Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?

A. transactionsDf.select('value', 'productId').distinct()

B. transactionsDf.select(col('value'), col('productId')).agg({'*': 'count'})

C. transactionsDf.agg({'value': 'collect_set', 'productId': 'collect_set'})

D. tranactionsDf.select('value').join(transactionsDf.select('productId'), col('value')==col('productId'),
'outer')

E. transactionsDf.select('value').union(transactionsDf.select('productId')).distinct()

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제2

Which of the following code blocks saves DataFrame transactionsDf in location /FileStore/transactions.csv as a CSV file and throws an error if a file already exists in the location?

A. transactionsDf.write.format("csv").mode("error").path("/FileStore/transactions.csv")

B. transactionsDf.write("csv").mode("error").save("/FileStore/transactions.csv")

C. transactionsDf.write.format("csv").mode("error").save("/FileStore/transactions.csv")

D. transactionsDf.write.save("/FileStore/transactions.csv")

E. transactionsDf.write.format("csv").mode("ignore").path("/FileStore/transactions.csv")

정답: C

설명: (Fast2test 회원만 볼 수 있음)

문제3

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.withColumnRenamed("predErrorSquared", pow(predError, 2))

B. transactionsDf.withColumn("predErrorSquared", "predError"**2)

C. transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))

D. transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))

E. transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))

정답: D

설명: (Fast2test 회원만 볼 수 있음)

문제4

Which of the following statements about RDDs is incorrect?

A. RDD stands for Resilient Distributed Dataset.

B. The high-level DataFrame API is built on top of the low-level RDD API.

C. RDDs are great for precisely instructing Spark on how to do a query.

D. RDDs are immutable.

E. An RDD consists of a single partition.

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제5

The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.__1__(__2__).select(__3__, __4__)

A. 1. filter
2. col("supplier").isin("Sports")
3. "itemName"
4. explode(col("attributes"))

B. 1. where
2. col("supplier").contains("Sports")
3. "itemName"
4. "attributes"

C. 1. where
2. "Sports".isin(col("Supplier"))
3. "itemName"
4. array_explode("attributes")

D. 1. filter
2. col("supplier").contains("Sports")
3. "itemName"
4. explode("attributes")

E. 1. where
2. col(supplier).contains("Sports")
3. explode(attributes)
4. itemName

정답: D

설명: (Fast2test 회원만 볼 수 있음)

문제6

Which of the following describes characteristics of the Dataset API?

A. The Dataset API does not provide compile-time type safety.

B. The Dataset API does not support unstructured data.

C. In Python, the Dataset API's schema is constructed via type hints.

D. In Python, the Dataset API mainly resembles Pandas' DataFrame API.

E. The Dataset API is available in Scala, but it is not available in Python.

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제7

Which is the highest level in Spark's execution hierarchy?

A. Job

B. Stage

C. Task

D. Executor

E. Slot

정답: A

문제8

The code block displayed below contains an error. The code block should use Python method find_most_freq_letter to find the letter present most in column itemName of DataFrame itemsDf and return it in a new column most_frequent_letter. Find the error.
Code block:
1. find_most_freq_letter_udf = udf(find_most_freq_letter)
2. itemsDf.withColumn("most_frequent_letter", find_most_freq_letter("itemName"))

A. Spark is not using the UDF method correctly.

B. The UDF method is not registered correctly, since the return type is missing.

C. The "itemName" expression should be wrapped in col().

D. UDFs do not exist in PySpark.

E. Spark is not adding a column.

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제9

Which of the following code blocks returns a DataFrame that matches the multi-column DataFrame itemsDf, except that integer column itemId has been converted into a string column?

A. itemsDf.withColumn("itemId", convert("itemId", "string"))

B. itemsDf.select(cast("itemId", "string"))

C. spark.cast(itemsDf, "itemId", "string")

D. itemsDf.withColumn("itemId", col("itemId").cast("string"))
(Correct)

E. itemsDf.withColumn("itemId", col("itemId").convert("string"))

정답: D

설명: (Fast2test 회원만 볼 수 있음)

문제10

Which of the following code blocks creates a new one-column, two-row DataFrame dfDates with column date of type timestamp?

A. 1.dfDates = spark.createDataFrame(["23/01/2022 11:28:12","24/01/2022 10:58:34"], ["date"])
2.dfDates = dfDates.withColumnRenamed("date", to_datetime("date", "yyyy-MM-dd HH:mm:ss"))

B. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])

C. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
2.dfDates = dfDates.withColumn("date", to_timestamp("date", "dd/MM/yyyy HH:mm:ss"))

D. 1.dfDates = spark.createDataFrame(["23/01/2022 11:28:12","24/01/2022 10:58:34"], ["date"])
2.dfDates = dfDates.withColumn("date", to_timestamp("dd/MM/yyyy HH:mm:ss", "date"))

E. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
2.dfDates = dfDates.withColumnRenamed("date", to_timestamp("date", "yyyy-MM-dd HH:mm:ss"))

정답: C

설명: (Fast2test 회원만 볼 수 있음)

문제11

Which of the following code blocks sorts DataFrame transactionsDf both by column storeId in ascending and by column productId in descending order, in this priority?

A. transactionsDf.sort("storeId").sort(desc("productId"))

B. transactionsDf.order_by(col(storeId), desc(col(productId)))

C. transactionsDf.sort("storeId", asc("productId"))

D. transactionsDf.sort(col(storeId)).desc(col(productId))

E. transactionsDf.sort("storeId", desc("productId"))

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제12

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. withColumnRenamed
2. 'associateId'
3. 5
4. drop
5. 'productId'

B. 1. withColumn
2. col(associateId)
3. lit(5)
4. drop
5. col(productId)

C. 1. withNewColumn
2. associateId
3. lit(5)
4. drop
5. productId

D. 1. withColumn
2. 'associateId'
3. 5
4. remove
5. 'productId'

E. 1. withColumn
2. 'associateId'
3. lit(5)
4. drop
5. 'productId'

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제13

Which of the following is a problem with using accumulators?

A. Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.

B. Accumulator values can only be read by the driver, but not by executors.

C. Only numeric values can be used in accumulators.

D. Accumulators do not obey lazy evaluation.

E. Only unnamed accumulators can be inspected in the Spark UI.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제14

The code block displayed below contains an error. The code block should return a new DataFrame that only contains rows from DataFrame transactionsDf in which the value in column predError is at least 5. Find the error.
Code block:
transactionsDf.where("col(predError) >= 5")

A. Instead of >=, the SQL operator GEQ should be used.

B. The argument to the where method should be "predError >= 5".

C. The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").

D. Instead of where(), filter() should be used.

E. The argument to the where method cannot be a string.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제15

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.

정답: D

설명: (Fast2test 회원만 볼 수 있음)

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