Free CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK – Databricks CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Practice Test Question

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• ○ DataFrame.drop()
• ○ DataFrame.coalesce()
• ○ DataFrame.take()
• ○ DataFrame.join()
• ○ DataFrame.filter()

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• ○ storesDF.subset()
• ○ storesDF.select()
• ○ storesDF.selectColumn()
• ○ storesDF.filter()
• ○ storesDF.drop()

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• ○ The argument to the mean() operation should be a Column abject rather than a string column name.
• ○ The argument to the mean() operation should not be quoted.
• ○ The mean() operation is not a standalone function – it’s a method of the Column object.
• ○ The agg() operation is not appropriate here – the withColumn() operation should be used instead.
• ○ The only way to compute a mean of a column is with the mean() method from a DataFrame.

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• ○ The assessPerformance() operation is not properly registered as a UDF.
• ○ The withColumn() operation is not appropriate here – UDFs should be applied by iterating over rows instead.
• ○ UDFs can only be applied vie SQL and not through the DataFrame API.
• ○ The return type of the assessPerformanceUDF() is not specified in the udf() operation.
• ○ The assessPerformance() operation should be used on column customerSatisfaction rather than the assessPerformanceUDF() operation.

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• ○ The createOrReplaceTempView() operation does not make a Dataframe accessible via SQL.
• ○ The sql() operation should be accessed via the spark variable rather than DataFrame storesDF.
• ○ There is the sql() operation in DataFrame storesDF. The operation query() should be used instead.
• ○ This cannot be accomplished using SQL – the DataFrame API should be used instead.
• ○ The createOrReplaceTempView() operation should be accessed via the spark variable rather than DataFrame storesDF.

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• ○ The from_unixtime() operation only accepts two parameters – the TimestampTime() arguments not necessary.
• ○ The from_unixtime() operation only works if column openDate is of type long rather than integer – column openDate must first be converted.
• ○ The second argument to from_unixtime() is not correct – it should be a variant of TimestampType() rather than a string.
• ○ The from_unixtime() operation automatically places the input column in java’s SimpleDateFormat – there is no need for a second or third argument.
• ○ The column openDate must first be converted to a timestamp, and then the Date() function can be used to reformat to java’s SimpleDateFormat.

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• ○ A cross join is not implemented by the DataFrame.join() operations – the standalone CrossJoin() operation should be used instead.
• ○ There is no direct cross join in Spark, but it can be implemented by performing an outer join on all columns of both DataFrames.
• ○ A cross join is not implemented by the DataFrame.join()operation – the DataFrame.crossJoin()operation should be used instead.
• ○ There is no key column specified – the key column "storeId" should be the second argument.
• ○ A cross join is not implemented by the DataFrame.join() operations – the standalone join() operation should be used instead.

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• ○ 4, 1, 2, 3
• ○ 4, 1, 5, 7
• ○ 4, 6, 2, 3
• ○ 4, 1, 5, 3
• ○ 4, 6, 2, 7

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• ○ There is no source parameter to the load() operation – the schema parameter should be used instead.
• ○ There is no load() operation – it should be parquet() instead.
• ○ The spark.read operation should be followed by parentheses to return a DataFrameReader object.
• ○ The filePath argument to the load() operation should be quoted.
• ○ There is no source parameter to the load() operation – it can be removed.

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• ○ DataFrame.distinct()
• ○ DataFrame.dropDuplicates() and DataFrame.distinct()
• ○ DataFrame.dropDuplicates()
• ○ DataFrame.drop_duplicates()
• ○ DataFrame.dropDuplicates(), DataFrame.distinct() and DataFrame.drop_duplicates()

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• ○ storesDF.repartition(4, "sqft")
• ○ storesDF.repartition()
• ○ storesDF.coalesce(4)
• ○ storesDF.repartition(4)
• ○ storesDF.coalesce

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• ○ spark.default.parallelism is not the right Spark configuration parameter – spark.sql.shuffle.partitions should be used instead.
• ○ There is no way to adjust the number of partitions used in wide transformations – it defaults to the number of total CPUs in the cluster.
• ○ Spark configuration parameters cannot be set in runtime.
• ○ Spark configuration parameters are not set with spark.conf.set().
• ○ The second argument should not be the string version of "32" – it should be the integer 32.

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• ○ The customerSatisfaction column cannot be called twice inside the SQL statement.
• ○ Registered UDFs cannot be applied inside of a SQL statement.
• ○ The order of the arguments to spark.udf.register() should be reversed.
• ○ The wrong SQL function is used to compute column result - it should be ASSESS_PERFORMANCE instead of assessPerformance.
• ○ There is no sql() operation - the DataFrame API must be used to apply the UDF assessPerformance().

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• ○ joinExprs = col(“a.column1”) === col(“b.column1”) and col(“a.column2”) === col(“b.column2”)
• ○ usingColumns = Seq(col(“column1”), col(“column2”))
• ○ All of these options can be used to perform an inner join with two key columns.
• ○ joinExprs = storesDF(“column1”) === employeesDF(“column1”) and storesDF(“column2”) === employeesDF (“column2”)
• ○ usingColumns = Seq(“column1”, “column2”)

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• ○ The MEMORY_ONLY storage level will store as much data as possible in memory and will store any data that does on fit in memory on disk and read it as it's called. The MEMORY_AND_DISK storage level will store as much data as possible in memory and will recompute any data that does not fit in memory as it’s called.
• ○ The MEMORY_ONLY storage level will store as much data as possible in memory on two cluster nodes and will recompute any data that does not fit in memory as it’s called. The MEMORY_AND_DISK storage level will store as much data as possible in memory on two cluster nodes and will store any data that does on fit in memory on disk and read it as it's called.
• ○ The MEMORY_ONLY storage level will store as much data as possible in memory on two cluster nodes and will store any data that does on fit in memory on disk and read it as it's called. The MEMORY_AND_DISK storage level will store as much data as possible in memory on two cluster nodes and will recompute any data that does not fit in memory as it's called.
• ○ The MEMORY_ONLY storage level will store as much data as possible in memory and will recompute any data that does not fit in memory as it's called. The MEMORY_AND_DISK storage level will store as much data as possible in memory and will store any data that does on fit in memory on disk and read it as it's called.
• ○ The MEMORY_ONLY storage level will store as much data as possible in memory and will recompute any data that does not fit in memory as it’s called. The MEMORY_AND_DISK storage level will store half of the data in memory and store half of the memory on disk. This provides quick preview and better logical plan design.

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• ○ 1. withColumnRenamed 2."state" 3."division" 4.withColumnRenamed 5."managerFullName" 6."managerName"
• ○ 1.withColumnRenamed 2.division 3.col("state") 4. withColumnRenamed 5."managerName" 6.col("managerFullName")
• ○ 1. WithColumnRenamed 2. "division" 3."state" 4. withColumnRenamed 5. "managerName" 6."managerFullName"
• ○ 1. withColumn 2. "division" 3. "state" 4.withcolumn 5."managerName" 6."managerFullName
• ○ 1. withColumn 2. "division" 3. "state" 4. withColumn 5."managerName" 6."managerFullName"

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• ○ 1. newColumn 2. "productCategories" 3. col 4. split 5. "productCategories"
• ○ 1. withColumn 2. "productCategory" 3. split 4. col 5. "productCategories"
• ○ 1. withColumn 2. "productCategory" 3. explode 4. col 5. "productCategories"
• ○ 1. newColumn 2. "productCategory" 3. explode 4. col 5. "productCategories"
• ○ 1. withColumn 2. "productCategories" 3. explode 4. col 5. "productCategories"

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• ○ 1. storesDF 2. schema 3. Nothing
• ○ 1. storesDF 2. str 3. schema
• ○ 1. storesDF 2. printSchema 3. True
• ○ 1. storesDF 2. printSchema 3. Nothing
• ○ 1. storesDF 2. printSchema 3. "all"

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• ○ 1.spark 2. read() 3. parquet 4. filePath
• ○ 1. spark 2. read() 3. load 4. filePath
• ○ 1. spark 2. read 3. load 4. filePath, source = "parquet"
• ○ 1. storesDF 2. read() 3. load 4. filePath
• ○ 1. spark 2. read 3. load 4. filePath

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• ○ The split() operation does not accomplish the requested task in the way that it is used. It should be used provided an alias.
• ○ The split() operation does not accomplish the requested task. The broadcast() operation should be used instead.
• ○ The split() operation does not accomplish the requested task in the way that it is used. It should be used as a column object method instead.
• ○ The split() operation does not accomplish the requested task. The explode() operation should be used instead.
• ○ The split() operation does not accomplish the requested task. The array_distinct() operation should be used instead.

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• ○ The Spark driver runs in its own node without any executors.
• ○ Each executor is a running processing engine inside of a worker node.
• ○ There may be more executors than total nodes or more total nodes than executors.
• ○ There is always more than one node.
• ○ There is a single node that contains the Spark driver and the executors.

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• ○ df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
• ○ df.withcolumn("discount", df.purchase_amount * 0.1).select ("discount")
• ○ df.withcolumn("purchase_date", current_date()).where("total_purchase > 50")
• ○ df.groupBy("user_id").agg (sum ("purchase_amount").alias("total_purchase")).repartition(10)

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• ○ Spark DataFrames, Structured Streaming, and Graphx
• ○ Spark SQL, Pandas API on Spark, and Structured Streaming
• ○ Spark Streaming, GraphX, and Pandas API on Spark
• ○ Spark DataFrames, Spark SQL, and MLIib