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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")

A. The copy() operator should be appended to the code block to ensure a copy is returned.
B. Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.
C. The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.
D. The arguments to the withColumn method need to be reordered.
E. The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.withColumnRenamed("transactionId", "transactionNumber")
Note that in Spark, a copy is returned by default. So, there is no need to append copy() to the code block.
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 61
Which of the following code blocks returns a DataFrame that is an inner join of DataFrame itemsDf and DataFrame transactionsDf, on columns itemId and productId, respectively and in which every itemId just appears once?

A. itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.productId).dropDuplicates(["itemId"])
B. itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.productId, how="inner").distinct(["itemId"])
C. itemsDf.join(transactionsDf, "itemsDf.itemId==transactionsDf.productId", how="inner").dropDuplicates(["itemId"])
D. itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.productId).dropDuplicates("itemId")
E. itemsDf.join(transactionsDf, "itemsDf.itemId==transactionsDf.productId").distinct("itemId")

Answer: A

Explanation:
Explanation
Filtering out distinct rows based on columns is achieved with the dropDuplicates method, not the distinct method which does not take any arguments.
The second argument of the join() method only accepts strings if they are column names. The SQL-like statement "itemsDf.itemId==transactionsDf.productId" is therefore invalid.
In addition, it is not necessary to specify how="inner", since the default join type for the join command is already inner.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 62
Which of the following code blocks reduces a DataFrame from 12 to 6 partitions and performs a full shuffle?

A. DataFrame.coalesce(6, shuffle=True)
B. DataFrame.repartition(12)
C. DataFrame.coalesce(6)
D. DataFrame.coalesce(6).shuffle()
E. DataFrame.repartition(6)

Answer: E

Explanation:
Explanation
DataFrame.repartition(6)
Correct. repartition() always triggers a full shuffle (different from coalesce()).
DataFrame.repartition(12)
No, this would just leave the DataFrame with 12 partitions and not 6.
DataFrame.coalesce(6)
coalesce does not perform a full shuffle of the data. Whenever you see "full shuffle", you know that you are not dealing with coalesce(). While coalesce() can perform a partial shuffle when required, it will try to minimize shuffle operations, so the amount of data that is sent between executors.
Here, 12 partitions can easily be repartitioned to be 6 partitions simply by stitching every two partitions into one.
DataFrame.coalesce(6, shuffle=True) and DataFrame.coalesce(6).shuffle() These statements are not valid Spark API syntax.
More info: Spark Repartition & Coalesce - Explained and Repartition vs Coalesce in Apache Spark - Rock the JVM Blog

NEW QUESTION # 63
Which of the following describes characteristics of the Spark UI?

A. Via the Spark UI, workloads can be manually distributed across executors.
B. The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.
C. Via the Spark UI, stage execution speed can be modified.
D. There is a place in the Spark UI that shows the property spark.executor.memory.
E. Some of the tabs in the Spark UI are named Jobs, Stages, Storage, DAGs, Executors, and SQL.

Answer: D

Explanation:
Explanation
There is a place in the Spark UI that shows the property spark.executor.memory.
Correct, you can see Spark properties such as spark.executor.memory in the Environment tab.
Some of the tabs in the Spark UI are named Jobs, Stages, Storage, DAGs, Executors, and SQL.
Wrong - Jobs, Stages, Storage, Executors, and SQL are all tabs in the Spark UI. DAGs can be inspected in the
"Jobs" tab in the job details or in the Stages or SQL tab, but are not a separate tab.
Via the Spark UI, workloads can be manually distributed across distributors.
No, the Spark UI is meant for inspecting the inner workings of Spark which ultimately helps understand, debug, and optimize Spark transactions.
Via the Spark UI, stage execution speed can be modified.
No, see above.
The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.
No, there is no Scheduler tab.

NEW QUESTION # 64
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_split")A.

A. Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
B. The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.
C. partitionOn("storeId") should be called before the write operation.
D. Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
E. The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").

Answer: A

Explanation:
Explanation
Correct code block:
transactionsDf.write.format("parquet").partitionBy("storeId").save("/FileStore/transactions_split") More info: partition by - Reading files which are written using PartitionBy or BucketBy in Spark - Stack Overflow Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 65
......

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