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

문제1

Which of the following options describes the responsibility of the executors in Spark?

A. The executors accept tasks from the cluster manager, execute those tasks, and return results to the driver.

B. The executors accept jobs from the driver, plan those jobs, and return results to the cluster manager.

C. The executors accept tasks from the driver, execute those tasks, and return results to the cluster manager.

D. The executors accept jobs from the driver, analyze those jobs, and return results to the driver.

E. The executors accept tasks from the driver, execute those tasks, and return results to the driver.

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제2

The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add_2_if_geq_3 as applied to numeric and nullable column predError in DataFrame transactionsDf. Find the error.
Code block:
1.def add_2_if_geq_3(x):
2. if x is None:
3. return x
4. elif x >= 3:
5. return x+2
6. return x
7.
8.add_2_if_geq_3_udf = udf(add_2_if_geq_3)
9.
10.transactionsDf.withColumnRenamed("predErrorAdded", add_2_if_geq_3_udf(col("predError")))

A. UDFs are only available through the SQL API, but not in the Python API as shown in the code block.

B. The operator used to adding the column does not add column predErrorAdded to the DataFrame.

C. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError.

D. The Python function is unable to handle null values, resulting in the code block crashing on execution.

E. The udf() method does not declare a return type.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제3

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)

문제4

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

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

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

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

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

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제5

Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?

A. spark.read.schema(fileSchema).format("parquet").load(filePath)

B. spark.read().schema(fileSchema).format(parquet).load(filePath)

C. spark.read().schema(fileSchema).parquet(filePath)

D. spark.read.schema("fileSchema").format("parquet").load(filePath)

E. spark.read.schema(fileSchema).open(filePath)

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제6

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. transactionsDf.persist()

B. transactionsDf.storage_level('MEMORY_ONLY')

C. from pyspark import StorageLevel
transactionsDf.cache(StorageLevel.MEMORY_ONLY)

D. transactionsDf.clear_persist()

E. transactionsDf.cache()

F. from pyspark import StorageLevel
transactionsDf.persist(StorageLevel.MEMORY_ONLY)

정답: F

설명: (DumpTOP 회원만 볼 수 있음)

문제7

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)

문제8

Which of the following code blocks adds a column predErrorSqrt to DataFrame transactionsDf that is the square root of column predError?

A. transactionsDf.select(sqrt("predError"))

B. transactionsDf.withColumn("predErrorSqrt", sqrt(predError))

C. transactionsDf.withColumn("predErrorSqrt", sqrt(col("predError")))

D. transactionsDf.withColumn("predErrorSqrt", col("predError").sqrt())

E. transactionsDf.select(sqrt(predError))

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제9

Which of the following code blocks returns all unique values of column storeId in DataFrame transactionsDf?

A. transactionsDf["storeId"].distinct()

B. transactionsDf.distinct("storeId")

C. transactionsDf.select("storeId").distinct()
(Correct)

D. transactionsDf.select(col("storeId").distinct())

E. transactionsDf.filter("storeId").distinct()

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제10

Which of the following describes the characteristics of accumulators?

A. If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.

B. All accumulators used in a Spark application are listed in the Spark UI.

C. Accumulators are used to pass around lookup tables across the cluster.

D. Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.

E. Accumulators are immutable.

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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