최신 Associate-Developer-Apache-Spark 무료덤프 - Databricks Certified Associate Developer for Apache Spark 3.0

문제1

Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a DataFrame?

A. spark.read().format('parquet').open("/FileStore/imports.parquet")

B. spark.mode("parquet").read("/FileStore/imports.parquet")

C. spark.read().parquet("/FileStore/imports.parquet")

D. spark.read.path("/FileStore/imports.parquet", source="parquet")

E. spark.read.parquet("/FileStore/imports.parquet")

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제2

Which of the following statements about data skew is incorrect?

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

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

C. Salting can resolve data skew.

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

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

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제3

Which of the following statements about Spark's configuration properties is incorrect?

A. The default value for spark.sql.autoBroadcastJoinThreshold is 10MB.

B. The default number of partitions to use when shuffling data for joins or aggregations is 300.

C. The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property.

D. The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property.

E. The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제4

Which of the following code blocks reads in the parquet file stored at location filePath, given that all columns in the parquet file contain only whole numbers and are stored in the most appropriate format for this kind of data?

A. 1.spark.read.schema([
2. StructField("transactionId", IntegerType(), True),
3. StructField("predError", IntegerType(), True)
4. ]).load(filePath, format="parquet")

B. 1.spark.read.schema(
2. StructType([
3. StructField("transactionId", StringType(), True),
4. StructField("predError", IntegerType(), True)]
5. )).parquet(filePath)

C. 1.spark.read.schema(
2. StructType([
3. StructField("transactionId", IntegerType(), True),
4. StructField("predError", IntegerType(), True)]
5. )).format("parquet").load(filePath)

D. 1.spark.read.schema([
2. StructField("transactionId", NumberType(), True),
3. StructField("predError", IntegerType(), True)
4. ]).load(filePath)

E. 1.spark.read.schema(
2. StructType(
3. StructField("transactionId", IntegerType(), True),
4. StructField("predError", IntegerType(), True)
5. )).load(filePath)

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제5

Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?

A. itemsDf.store()

B. itemsDf.cache()

C. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)

D. itemsDf.write.option('destination', 'memory').save()

E. itemsDf.persist(StorageLevel.MEMORY_ONLY)

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제6

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

B. Spark is not adding a column.

C. Spark is not using the UDF method correctly.

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

E. UDFs do not exist in PySpark.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제7

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. 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'))

B. 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')))

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

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제8

The code block shown below should set the number of partitions that Spark uses when shuffling data for joins or aggregations to 100. Choose the answer that correctly fills the blanks in the code block to accomplish this.
spark.sql.shuffle.partitions
__1__.__2__.__3__(__4__, 100)

A. 1. pyspark
2. config
3. set
4. spark.shuffle.partitions

B. 1. spark
2. conf
3. get
4. "spark.sql.shuffle.partitions"

C. 1. pyspark
2. config
3. set
4. "spark.sql.shuffle.partitions"

D. 1. spark
2. conf
3. set
4. "spark.sql.aggregate.partitions"

E. 1. spark
2. conf
3. set
4. "spark.sql.shuffle.partitions"

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제9

Which of the following describes the difference between client and cluster execution modes?

A. In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.

B. In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.

C. In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.

D. In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.

E. In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제10

The code block shown below should show information about the data type that column storeId of DataFrame transactionsDf contains. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__).__3__

A. 1. select
2. "storeId"
3. printSchema()

B. 1. limit
2. "storeId"
3. printSchema()

C. 1. limit
2. 1
3. columns

D. 1. select
2. "storeId"
3. print_schema()

E. 1. select
2. storeId
3. dtypes

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

최신 Associate-Developer-Apache-Spark 무료덤프 - Databricks Certified Associate Developer for Apache Spark 3.0

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