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

문제1

The code block shown below should read all files with the file ending .png in directory path into Spark.
Choose the answer that correctly fills the blanks in the code block to accomplish this.
spark.__1__.__2__(__3__).option(__4__, "*.png").__5__(path)

A. 1. read
2. format
3. binaryFile
4. pathGlobFilter
5. load

B. 1. read()
2. format
3. "binaryFile"
4. "recursiveFileLookup"
5. load

C. 1. open
2. format
3. "image"
4. "fileType"
5. open

D. 1. open
2. as
3. "binaryFile"
4. "pathGlobFilter"
5. load

E. 1. read
2. format
3. "binaryFile"
4. "pathGlobFilter"
5. load

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제2

Which of the following describes characteristics of the Spark UI?

A. Via the Spark UI, stage execution speed can be modified.

B. Via the Spark UI, workloads can be manually distributed across executors.

C. Some of the tabs in the Spark UI are named Jobs, Stages, Storage, DAGs, Executors, and SQL.

D. There is a place in the Spark UI that shows the property spark.executor.memory.

E. The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제3

Which of the following code blocks creates a new DataFrame with two columns season and wind_speed_ms where column season is of data type string and column wind_speed_ms is of data type double?

A. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})

B. spark.DataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})

C. CharType()), T.StructField("season", T.DoubleType())]))

D. spark.createDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])

E. 1. from pyspark.sql import types as T
2. spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season",

F. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제4

The code block shown below should add column transactionDateForm to DataFrame transactionsDf. The column should express the unix-format timestamps in column transactionDate as string type like Apr 26 (Sunday). Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, from_unixtime(__3__, __4__))

A. 1. withColumnRenamed
2. "transactionDate"
3. "transactionDateForm"
4. "MM d (EEE)"

B. 1. withColumn
2. "transactionDateForm"
3. "MMM d (EEEE)"
4. "transactionDate"

C. 1. select
2. "transactionDate"
3. "transactionDateForm"
4. "MMM d (EEEE)"

D. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MM d (EEE)"

E. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MMM d (EEEE)"

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제5

Which of the following describes a difference between Spark's cluster and client execution modes?

A. In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.

B. In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.

C. In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.

D. In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.

E. In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제6

Which of the following describes a narrow transformation?

A. A narrow transformation is an operation in which data is exchanged across the cluster.

B. A narrow transformation is a process in which data from multiple RDDs is used.

C. A narrow transformation is an operation in which no data is exchanged across the cluster.

D. narrow transformation is an operation in which data is exchanged across partitions.

E. A narrow transformation is a process in which 32-bit float variables are cast to smaller float variables, like 16-bit or 8-bit float variables.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제7

Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:

A. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))

B. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))

C. transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")

D. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))

E. transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제8

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. sample
2. 0.85
3. False
4. select

B. 1. sample
2. False
3. 0.15
4. select

C. 1. fraction
2. False
3. 0.85
4. select

D. 1. fraction
2. 0.15
3. True
4. where

E. 1. sample
2. True
3. 0.15
4. filter

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제9

Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
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.drop(col("value"), col("predError"))

B. transactionsDf.drop(value, predError)

C. transactionsDf.drop([col("predError"), col("value")])

D. transactionsDf.drop(["predError", "value"])

E. transactionsDf.drop("predError", "value")

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제10

The code block shown below should return a copy of DataFrame transactionsDf with an added column cos.
This column should have the values in column value converted to degrees and having the cosine of those converted values taken, rounded to two decimals. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__, round(__3__(__4__(__5__)),2))

A. 1. withColumn
2. "cos"
3. cos
4. degrees
5. transactionsDf.value

B. 1. withColumnRenamed
2. "cos"
3. cos
4. degrees
5. "transactionsDf.value"

C. 1. withColumn
2. col("cos")
3. cos
4. degrees
5. transactionsDf.value

D. 1. withColumn
2. col("cos")
3. cos
4. degrees
5. col("value")
E
. 1. withColumn
2. "cos"
3. degrees
4. cos
5. col("value")

정답: A

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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