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

문제1

The code block shown below should return a DataFrame with only columns from DataFrame transactionsDf for which there is a corresponding transactionId in DataFrame itemsDf. DataFrame itemsDf is very small and much smaller than DataFrame transactionsDf. The query should be executed in an optimized way. Choose the answer that correctly fills the blanks in the code block to accomplish this.
__1__.__2__(__3__, __4__, __5__)

A. 1. transactionsDf
2. join
3. broadcast(itemsDf)
4. transactionsDf.transactionId==itemsDf.transactionId
5. "outer"

B. 1. itemsDf
2. join
3. broadcast(transactionsDf)
4. "transactionId"
5. "left_semi"

C. 1. itemsDf
2. broadcast
3. transactionsDf
4. "transactionId"
5. "left_semi"

D. 1. transactionsDf
2. join
3. itemsDf
4. transactionsDf.transactionId==itemsDf.transactionId
5. "anti"

E. 1. transactionsDf
2. join
3. broadcast(itemsDf)
4. "transactionId"
5. "left_semi"

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제2

The code block displayed below contains an error. The code block should return DataFrame transactionsDf, but with the column storeId renamed to storeNumber. Find the error.
Code block:
transactionsDf.withColumn("storeNumber", "storeId")

A. Instead of withColumn, the withColumnRenamed method should be used and argument "storeId" should be the first and argument "storeNumber" should be the second argument to that method.

B. Arguments "storeNumber" and "storeId" each need to be wrapped in a col() operator.

C. Argument "storeId" should be the first and argument "storeNumber" should be the second argument to the withColumn method.

D. The withColumn operator should be replaced with the copyDataFrame operator.

E. Instead of withColumn, the withColumnRenamed method should be used.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제3

The code block displayed below contains an error. The code block below is intended to add a column itemNameElements to DataFrame itemsDf that includes an array of all words in column itemName. Find the error.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-------------------+
2.|itemId|itemName |supplier |
3.+------+----------------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |YetiX |
6.|3 |Outdoors Backpack |Sports Company Inc.|
7.+------+----------------------------------+-------------------+
Code block:
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))

A. Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument "
" needs to be passed to the split method.

B. The expressions "itemNameElements" and split("itemName") need to be swapped.

C. Operator withColumnRenamed needs to be replaced with operator withColumn and the split method needs to be replaced by the splitString method.

D. Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument
"," needs to be passed to the split method.

E. All column names need to be wrapped in the col() operator.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제4

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)

문제5

Which of the following code blocks creates a new 6-column DataFrame by appending the rows of the
6-column DataFrame yesterdayTransactionsDf to the rows of the 6-column DataFrame todayTransactionsDf, ignoring that both DataFrames have different column names?

A. todayTransactionsDf.unionByName(yesterdayTransactionsDf, allowMissingColumns=True)

B. union(todayTransactionsDf, yesterdayTransactionsDf)

C. todayTransactionsDf.union(yesterdayTransactionsDf)

D. todayTransactionsDf.unionByName(yesterdayTransactionsDf)

E. todayTransactionsDf.concat(yesterdayTransactionsDf)

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제6

Which of the following statements about garbage collection in Spark is incorrect?

A. Optimizing garbage collection performance in Spark may limit caching ability.

B. Garbage collection information can be accessed in the Spark UI's stage detail view.

C. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.

D. Serialized caching is a strategy to increase the performance of garbage collection.

E. Manually persisting RDDs in Spark prevents them from being garbage collected.

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제7

Which of the following code blocks generally causes a great amount of network traffic?

A. DataFrame.count()

B. DataFrame.collect()

C. DataFrame.rdd.map()

D. DataFrame.coalesce()

E. DataFrame.select()

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제8

The code block displayed below contains an error. The code block is intended to join DataFrame itemsDf with the larger DataFrame transactionsDf on column itemId. Find the error.
Code block:
transactionsDf.join(itemsDf, "itemId", how="broadcast")

A. The larger DataFrame transactionsDf is being broadcasted, rather than the smaller DataFrame itemsDf.

B. broadcast is not a valid join type.

C. The syntax is wrong, how= should be removed from the code block.

D. The join method should be replaced by the broadcast method.

E. Spark will only perform the broadcast operation if this behavior has been enabled on the Spark cluster.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제9

Which of the following code blocks returns DataFrame transactionsDf sorted in descending order by column predError, showing missing values last?

A. transactionsDf.orderBy("predError").asc_nulls_last()

B. transactionsDf.sort(asc_nulls_last("predError"))

C. transactionsDf.orderBy("predError").desc_nulls_last()

D. transactionsDf.desc_nulls_last("predError")

E. transactionsDf.sort("predError", ascending=False)

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제10

Which of the following code blocks performs a join in which the small DataFrame transactionsDf is sent to all executors where it is joined with DataFrame itemsDf on columns storeId and itemId, respectively?

A. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "right_outer")

B. itemsDf.join(broadcast(transactionsDf), itemsDf.itemId == transactionsDf.storeId)

C. itemsDf.join(transactionsDf, broadcast(itemsDf.itemId == transactionsDf.storeId))

D. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "broadcast")

E. itemsDf.merge(transactionsDf, "itemsDf.itemId == transactionsDf.storeId", "broadcast")

정답: B

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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