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NEW QUESTION 34
Which of the following code blocks returns a DataFrame that has all columns of DataFrame transactionsDf and an additional column predErrorSquared which is the squared value of column predError in DataFrame transactionsDf?
- A. transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))
- B. transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))
- C. transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))
- D. transactionsDf.withColumnRenamed("predErrorSquared", pow(predError, 2))
- E. transactionsDf.withColumn("predErrorSquared", "predError"**2)
Answer: C
Explanation:
Explanation
While only one of these code blocks works, the DataFrame API is pretty flexible when it comes to accepting columns into the pow() method. The following code blocks would also work:
transactionsDf.withColumn("predErrorSquared", pow("predError", 2))
transactionsDf.withColumn("predErrorSquared", pow("predError", lit(2))) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/spark_practice_tests_code/#1/26.html ,
https://bit.ly/sparkpracticeexams_import_instructions)
NEW QUESTION 35
Which of the following describes Spark's standalone deployment mode?
- A. Standalone mode is a viable solution for clusters that run multiple frameworks, not only Spark.
- B. Standalone mode is how Spark runs on YARN and Mesos clusters.
- C. Standalone mode uses a single JVM to run Spark driver and executor processes.
- D. Standalone mode uses only a single executor per worker per application.
- E. Standalone mode means that the cluster does not contain the driver.
Answer: D
Explanation:
Explanation
Standalone mode uses only a single executor per worker per application.
This is correct and a limitation of Spark's standalone mode.
Standalone mode is a viable solution for clusters that run multiple frameworks.
Incorrect. A limitation of standalone mode is that Apache Spark must be the only framework running on the cluster. If you would want to run multiple frameworks on the same cluster in parallel, for example Apache Spark and Apache Flink, you would consider the YARN deployment mode.
Standalone mode uses a single JVM to run Spark driver and executor processes.
No, this is what local mode does.
Standalone mode is how Spark runs on YARN and Mesos clusters.
No. YARN and Mesos modes are two deployment modes that are different from standalone mode. These modes allow Spark to run alongside other frameworks on a cluster. When Spark is run in standalone mode, only the Spark framework can run on the cluster.
Standalone mode means that the cluster does not contain the driver.
Incorrect, the cluster does not contain the driver in client mode, but in standalone mode the driver runs on a node in the cluster.
More info: Learning Spark, 2nd Edition, Chapter 1
NEW QUESTION 36
The code block shown below should set the number of partitions that Spark uses when shuffling data for joins or aggregations to 100. Choose the answer that correctly fills the blanks in the code block to accomplish this.
spark.sql.shuffle.partitions
__1__.__2__.__3__(__4__, 100)
- A. 1. pyspark
2. config
3. set
4. "spark.sql.shuffle.partitions" - B. 1. spark
2. conf
3. set
4. "spark.sql.shuffle.partitions" - C. 1. spark
2. conf
3. set
4. "spark.sql.aggregate.partitions" - D. 1. spark
2. conf
3. get
4. "spark.sql.shuffle.partitions" - E. 1. pyspark
2. config
3. set
4. spark.shuffle.partitions
Answer: B
Explanation:
Explanation
Correct code block:
spark.conf.set("spark.sql.shuffle.partitions", 100)
The conf interface is part of the SparkSession, so you need to call it through spark and not pyspark. To configure spark, you need to use the set method, not the get method. get reads a property, but does not write it. The correct property to achieve what is outlined in the question is spark.sql.aggregate.partitions, which needs to be passed to set as a string. Properties spark.shuffle.partitions and spark.sql.aggregate.partitions do not exist in Spark.
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 37
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