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

A. transactionsDf.count("productId")

B. transactionsDf.groupBy("productId").agg(col("value").count())

C. transactionsDf.groupBy("productId").select(count("value"))

D. transactionsDf.groupBy("productId").count()

E. transactionsDf.count("productId").distinct()

A. The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value as strings.
(Correct)

B. The column names in the select operator should not be strings and wrapped in the col operator, so they should be expressed like select(~col(predError), ~col(productId), ~col(value)).

C. The select operator should be replaced with the deselect operator.

D. The select operator should be replaced by the drop operator.

E. The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value wrapped in the col operator so they should be expressed like drop(col(predError), col(productId), col(value)).

A. If set in the Spark configuration, Spark scales the Spark driver horizontally to improve parallel processing performance.

B. The Spark driver's responsibility includes scheduling queries for execution on worker nodes.

C. In a non-interactive Spark application, the Spark driver automatically creates the SparkSession object.

D. The Spark driver processes partitions in an optimized, distributed fashion.

E. The Spark driver requests the transformation of operations into DAG computations from the worker nodes.

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.

A. transactionsDf.drop("predError & value")

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

C. transactionsDf.drop(predError, value)

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

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

A. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, to_limit(predError) AS result FROM transactionsDf") spark.sql("SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf")

B. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf") (Correct)

C. 1.spark.udf.register(to_limit, "LIMIT_FCN")
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf")

D. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result")

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()

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.

A. The method used on column predError is incorrect.

B. Instead of a list, the values need to be passed as single arguments to the in operator.

C. Numbers 3 and 6 need to be passed as string variables.

D. Instead of filter, the select method should be used.

E. The number of rows cannot be determined with the count() operator.

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)

A. Caching can be undone using the DataFrame.unpersist() operator.

B. In client mode, DataFrames cached with the MEMORY_ONLY_2 level will not be stored in the edge node's memory.

C. DISK_ONLY will not use the worker node's memory.

D. MEMORY_AND_DISK replicates cached DataFrames both on memory and disk.

E. The cache operator on DataFrames is evaluated like a transformation.