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NEW QUESTION 26
The code block displayed below contains an error. The code block should configure Spark to split data in 20 parts when exchanging data between executors for joins or aggregations. Find the error.
Code block:
spark.conf.set(spark.sql.shuffle.partitions, 20)
- A. The code block is missing a parameter.
- B. The code block uses the wrong command for setting an option.
- C. The code block sets the wrong option.
- D. The code block expresses the option incorrectly.
- E. The code block sets the incorrect number of parts.
Answer: D
Explanation:
Explanation
Correct code block:
spark.conf.set("spark.sql.shuffle.partitions", 20)
The code block expresses the option incorrectly.
Correct! The option should be expressed as a string.
The code block sets the wrong option.
No, spark.sql.shuffle.partitions is the correct option for the use case in the question.
The code block sets the incorrect number of parts.
Wrong, the code block correctly states 20 parts.
The code block uses the wrong command for setting an option.
No, in PySpark spark.conf.set() is the correct command for setting an option.
The code block is missing a parameter.
Incorrect, spark.conf.set() takes two parameters.
More info: Configuration - Spark 3.1.2 Documentation
NEW QUESTION 27
Which of the following code blocks performs an inner join between DataFrame itemsDf and DataFrame transactionsDf, using columns itemId and transactionId as join keys, respectively?
- A. itemsDf.join(transactionsDf, col(itemsDf.itemId) == col(transactionsDf.transactionId))
- B. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.transactionId, "inner")
- C. itemsDf.join(transactionsDf, itemId == transactionId)
- D. itemsDf.join(transactionsDf, "inner", itemsDf.itemId == transactionsDf.transactionId)
- E. itemsDf.join(transactionsDf, "itemsDf.itemId == transactionsDf.transactionId", "inner")
Answer: B
Explanation:
Explanation
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 28
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?
- A. spark.read.schema(fileSchema).open(filePath)
- B. spark.read.schema(fileSchema).format("parquet").load(filePath)
- C. spark.read().schema(fileSchema).format(parquet).load(filePath)
- D. spark.read.schema("fileSchema").format("parquet").load(filePath)
- E. spark.read().schema(fileSchema).parquet(filePath)
Answer: B
Explanation:
Explanation
Pay attention here to which variables are quoted. fileSchema is a variable and thus should not be in quotes.
parquet is not a variable and therefore should be in quotes.
SparkSession.read (here referenced as spark.read) returns a DataFrameReader which all subsequent calls reference - the DataFrameReader is not callable, so you should not use parentheses here.
Finally, there is no open method in PySpark. The method name is load.
Static notebook | Dynamic notebook: See test 1
NEW QUESTION 29
Which of the following describes the difference between client and cluster execution modes?
- A. In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
- B. In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
- C. In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.
- D. In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.
- E. In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
Answer: C
Explanation:
Explanation
In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.
This is wrong, since execution modes do not specify whether workloads are run in the cloud or on-premise.
In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
Wrong, since in both cases executors run on worker nodes.
In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
Wrong - in cluster mode, the driver runs on a worker node. In client mode, the driver runs on the client machine.
In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
No. In both modes, the cluster manager is typically on a separate node - not on the same host as the driver. It only runs on the same host as the driver in local execution mode.
More info: Learning Spark, 2nd Edition, Chapter 1, and Spark: The Definitive Guide, Chapter 15. ()
NEW QUESTION 30
Which of the following code blocks returns about 150 randomly selected rows from the 1000-row DataFrame transactionsDf, assuming that any row can appear more than once in the returned DataFrame?
- A. transactionsDf.sample(0.15)
- B. transactionsDf.resample(0.15, False, 3142)
- C. transactionsDf.sample(0.85, 8429)
- D. transactionsDf.sample(True, 0.15, 8261)
- E. transactionsDf.sample(0.15, False, 3142)
Answer: D
Explanation:
Explanation
Answering this question correctly depends on whether you understand the arguments to the DataFrame.sample() method (link to the documentation below). The arguments are as follows:
DataFrame.sample(withReplacement=None, fraction=None, seed=None).
The first argument withReplacement specified whether a row can be drawn from the DataFrame multiple times. By default, this option is disabled in Spark. But we have to enable it here, since the question asks for a row being able to appear more than once. So, we need to pass True for this argument.
About replacement: "Replacement" is easiest explained with the example of removing random items from a box. When you remove those "with replacement" it means that after you have taken an item out of the box, you put it back inside. So, essentially, if you would randomly take 10 items out of a box with 100 items, there is a chance you take the same item twice or more times. "Without replacement" means that you would not put the item back into the box after removing it. So, every time you remove an item from the box, there is one less item in the box and you can never take the same item twice.
The second argument to the withReplacement method is fraction. This referes to the fraction of items that should be returned. In the question we are asked for 150 out of 1000 items - a fraction of 0.15.
The last argument is a random seed. A random seed makes a randomized processed repeatable. This means that if you would re-run the same sample() operation with the same random seed, you would get the same rows returned from the sample() command. There is no behavior around the random seed specified in the question. The varying random seeds are only there to confuse you!
More info: pyspark.sql.DataFrame.sample - PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1
NEW QUESTION 31
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