SPARK-DEV practice questions 51–60

1. Question 51 · 219 in bank

   Which of the following code blocks returns a new DataFrame that is the result of a cross join between DataFrame storesDF and DataFrame employeesDF?

   • AstoresDF.crossJoin(employeesDF)
   • BstoresDF.join(employeesDF, "storeId", "cross")
   • CcrossJoin(storesDF, employeesDF)
   • Djoin(storesDF, employeesDF, "cross")
   • EstoresDF.join(employeesDF, "cross")

   Show answer

   Answer: A

   Next question

2. Question 52 · 219 in bank

   Which of the following code blocks returns a new DataFrame that is the result of a position-wise union between DataFrame storesDF and DataFrame acquiredStoresDF?

   • AstoresDF.unionByName(acquiredStoresDF)
   • BunionAll(storesDF, acquiredStoresDF)
   • Cunion(storesDF, acquiredStoresDF)
   • Dconcat(storesDF, acquiredStoresDF)
   • EstoresDF.union(acquiredStoresDF)

   Show answer

   Answer: E

   Next question

3. Question 53 · 219 in bank

   In what order should the below lines of code be run in order to read a parquet at the file path filePath into a DataFrame? Lines of code: 1. storesDF 2. .load(filePath, source = "parquet") 3. .read \ 4. spark \ 5. .read() \ 6. .parquet(filePath)

   • A1, 5, 2
   • B4, 5, 2
   • C4, 3, 6
   • D4, 5, 6
   • E4, 3, 2

   Show answer

   Answer: C

   Next question

4. Question 54 · 219 in bank

   The code block shown below should read a CSV at the file path filePath into a DataFrame with the specified schema schema. Choose the response that correctly fills in the numbered blanks within the code block to complete this task. Code block: __1__.__2__.__3__(__4__).format("csv").__5__(__6__)

   • A1. spark 2. read() 3. schema 4. schema 5. json 6. filePath
   • B1. spark 2. read() 3. schema 4. schema 5. load 6. filePath
   • C1. spark 2. read 3. format 4. "json" 5. load 6. filePath
   • D1. spark 2. read() 3. json 4. filePath 5. format 6. schema
   • E1. spark 2. read 3. schema 4. schema 5. load 6. filePath

   Show answer

   Answer: E

   Next question

5. Question 55 · 219 in bank

   Which of the following describes slots?

   • ASlots are the most coarse level of execution in the Spark execution hierarchy.
   • BSlots are resource threads that can be used for parallelization within a Spark application.
   • CSlots are resources that are used to run multiple Spark applications at once on a single cluster.
   • DSlots are the most granular level of execution in the Spark execution hierarchy.
   • ESlots are unique segments of data from a DataFrame that are split up by row.

   Show answer

   Answer: B

   Next question

6. Question 56 · 219 in bank

   A Spark application has a 128 GB DataFrame A and a 1 GB DataFrame B. If a broadcast join were to be performed on these two DataFrames, which of the following describes which DataFrame should be broadcasted and why?

   • AEither DataFrame can be broadcasted. Their results will be identical in result and efficiency.
   • BDataFrame B should be broadcasted because it is smaller and will eliminate the need for the shuffling of itself.
   • CDataFrame A should be broadcasted because it is larger and will eliminate the need for the shuffling of DataFrame B.
   • DDataFrame B should be broadcasted because it is smaller and will eliminate the need for the shuffling of DataFrame A.
   • EDataFrame A should be broadcasted because it is smaller and will eliminate the need for the shuffling of itself.

   Show answer

   Answer: D

   Explanation

   The correct answer is: B. DataFrame B should be broadcasted because it is smaller and will eliminate the need for the shuffling of itself. Explanation: In Spark, a broadcast join is a specific type of join where one DataFrame is sent to every node in the cluster to avoid the costly network shuffle that can occur with large datasets in regular joins. Generally, the smaller DataFrame should be broadcasted to optimize performance. This is because broadcasting a smaller DataFrame requires less network bandwidth and memory usage across the cluster. Broadcasting DataFrame B (the smaller DataFrame at 1 GB) means that each node will have a local copy of DataFrame B, allowing them to perform the join operation locally with their respective partitions of DataFrame A without needing to shuffle DataFrame B across the network. This approach significantly reduces the amount of data that needs to be shuffled (since only DataFrame A is partitioned across the nodes), thereby improving the performance of the join operation.

   Source: Adapted from community discussion — atulrao

   Next question

7. Question 57 · 219 in bank

   Which of the following operations is least likely to result in a shuffle?

   • ADataFrame.join()
   • BDataFrame.fliter()
   • CDataFrame.orderBy()
   • DDataFrame.distinct()
   • EDataFrame.intersect()

   Show answer

   Answer: B

   Next question

8. Question 58 · 219 in bank

   Which of the following cluster configurations is least likely to experience delays due to garbage collection of a large DataFrame?

   

   Note: each configuration has roughly the same compute power using 100GB of RAM and 200 cores.

   • AScenario #4
   • BScenario #1
   • CScenario #5
   • DMore information is needed to determine an answer.
   • EScenario #6

   Show answer

   Answer: E

   Next question

9. Question 59 · 219 in bank

   The code block shown below should return a new DataFrame where column productСategories only has one word per row, resulting in a DataFrame with many more rows than DataFrame storesDF. Choose the response that correctly fills in the numbered blanks within the code block to complete this task. A sample of storesDF is displayed below:

   

   Code block: storesDF.__1__(__2__, __3__(__4__(__5__)))

   • A1. newColumn 2. "productCategories" 3. col 4. split 5. "productCategories"
   • B1. withColumn 2. "productCategory" 3. split 4. col 5. "productCategories"
   • C1. withColumn 2. "productCategory" 3. explode 4. col 5. "productCategories"
   • D1. newColumn 2. "productCategory" 3. explode 4. col 5. "productCategories"
   • E1. withColumn 2. "productCategories" 3. explode 4. col 5. "productCategories"

   Show answer

   Answer: C

   Next question

10. Question 60 · 219 in bank

    Which of the following code blocks returns a new DataFrame with column storeReview where the pattern "End" has been removed from the end of column storeReview in DataFrame storesDF? A sample DataFrame storesDF is below:

    

    • AstoresDF.withColumn("storeReview", col("storeReview").regexp_replace(" End$", ""))
    • BstoresDF.withColumn("storeReview", regexp_replace(col("storeReview"), " End$", ""))
    • CstoresDF.withColumn("storeReview”, regexp_replace(col("storeReview"), " End$"))
    • DstoresDF.withColumn("storeReview", regexp_replace("storeReview", " End$", ""))
    • EstoresDF.withColumn("storeReview", regexp_extract(col("storeReview"), " End$", ""))

    Show answer

    Answer: B

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

SPARK-DEV practice, question 51. 219 questions available.