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NEW QUESTION 32
Which of the following options describes the responsibility of the executors in Spark?

• A. The executors accept tasks from the driver, execute those tasks, and return results to the driver.
• B. The executors accept jobs from the driver, analyze those jobs, and return results to the driver.
• C. The executors accept tasks from the cluster manager, execute those tasks, and return results to the driver.
• D. The executors accept tasks from the driver, execute those tasks, and return results to the cluster manager.
• E. The executors accept jobs from the driver, plan those jobs, and return results to the cluster manager.

Answer: A

Explanation:
Explanation
More info: Running Spark: an overview of Spark's runtime architecture - Manning (https://bit.ly/2RPmJn9)

NEW QUESTION 33
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. broadcast
  3. transactionsDf
  4. "transactionId"
  5. "left_semi"
• C. 1. transactionsDf
  2. join
  3. broadcast(itemsDf)
  4. "transactionId"
  5. "left_semi"
• D. 1. transactionsDf
  2. join
  3. itemsDf
  4. transactionsDf.transactionId==itemsDf.transactionId
  5. "anti"
• E. 1. itemsDf
  2. join
  3. broadcast(transactionsDf)
  4. "transactionId"
  5. "left_semi"

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This question is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join, broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard it. Another answer option wraps the broadcast() operator around transactionsDf - the bigger of the two DataFrames. This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One answer option, however, resolves to itemsDf.broadcast([...]). The DataFrame class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([...]) in the first 2 gaps, so you will have to figure out the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as asked for by the question. So, the correct answer is the one that uses the left_semi join.

NEW QUESTION 34
Which of the elements in the labeled panels represent the operation performed for broadcast variables?
Larger image

• A. 2, 5
• B. 0
• C. 1, 3, 4
• D. 1, 2
• E. 2, 3

Answer: E

Explanation:
Explanation
2,3
Correct! Both panels 2 and 3 represent the operation performed for broadcast variables. While a broadcast operation may look like panel 3, with the driver being the bottleneck, it most probably looks like panel 2.
This is because the torrent protocol sits behind Spark's broadcast implementation. In the torrent protocol, each executor will try to fetch missing broadcast variables from the driver or other nodes, preventing the driver from being the bottleneck.
1,2
Wrong. While panel 2 may represent broadcasting, panel 1 shows bi-directional communication which does not occur in broadcast operations.
3
No. While broadcasting may materialize like shown in panel 3, its use of the torrent protocol also enables communciation as shown in panel 2 (see first explanation).
1,3,4
No. While panel 2 shows broadcasting, panel 1 shows bi-directional communication - not a characteristic of broadcasting. Panel 4 shows uni-directional communication, but in the wrong direction.
Panel 4 resembles more an accumulator variable than a broadcast variable.
2,5
Incorrect. While panel 2 shows broadcasting, panel 5 includes bi-directional communication - not a characteristic of broadcasting.
More info: Broadcast Join with Spark - henning.kropponline.de

NEW QUESTION 35
Which of the following is a problem with using accumulators?

• A. Accumulator values can only be read by the driver, but not by executors.
• B. Only numeric values can be used in accumulators.
• C. Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
• D. Accumulators do not obey lazy evaluation.
• E. Only unnamed accumulators can be inspected in the Spark UI.

Answer: A

Explanation:
Explanation
Accumulator values can only be read by the driver, but not by executors.
Correct. So, for example, you cannot use an accumulator variable for coordinating workloads between executors. The typical, canonical, use case of an accumulator value is to report data, for example for debugging purposes, back to the driver. For example, if you wanted to count values that match a specific condition in a UDF for debugging purposes, an accumulator provides a good way to do that.
Only numeric values can be used in accumulators.
No. While pySpark's Accumulator only supports numeric values (think int and float), you can define accumulators for custom types via the AccumulatorParam interface (documentation linked below).
Accumulators do not obey lazy evaluation.
Incorrect - accumulators do obey lazy evaluation. This has implications in practice: When an accumulator is encapsulated in a transformation, that accumulator will not be modified until a subsequent action is run.
Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
Wrong. A concern with accumulators is in fact that under certain conditions they can run for each task more than once. For example, if a hardware failure occurs during a task after an accumulator variable has been increased but before a task has finished and Spark launches the task on a different worker in response to the failure, already executed accumulator variable increases will be repeated.
Only unnamed accumulators can be inspected in the Spark UI.
No. Currently, in PySpark, no accumulators can be inspected in the Spark UI. In the Scala interface of Spark, only named accumulators can be inspected in the Spark UI.
More info: Aggregating Results with Spark Accumulators | Sparkour, RDD Programming Guide - Spark 3.1.2 Documentation, pyspark.Accumulator - PySpark 3.1.2 documentation, and pyspark.AccumulatorParam - PySpark 3.1.2 documentation

NEW QUESTION 36
Which of the following are valid execution modes?

• A. Server, Standalone, Client
• B. Standalone, Client, Cluster
• C. Cluster, Server, Local
• D. Client, Cluster, Local
• E. Kubernetes, Local, Client

Answer: D

Explanation:
Explanation
This is a tricky question to get right, since it is easy to confuse execution modes and deployment modes. Even in literature, both terms are sometimes used interchangeably.
There are only 3 valid execution modes in Spark: Client, cluster, and local execution modes. Execution modes do not refer to specific frameworks, but to where infrastructure is located with respect to each other.
In client mode, the driver sits on a machine outside the cluster. In cluster mode, the driver sits on a machine inside the cluster. Finally, in local mode, all Spark infrastructure is started in a single JVM (Java Virtual Machine) in a single computer which then also includes the driver.
Deployment modes often refer to ways that Spark can be deployed in cluster mode and how it uses specific frameworks outside Spark. Valid deployment modes are standalone, Apache YARN, Apache Mesos and Kubernetes.
Client, Cluster, Local
Correct, all of these are the valid execution modes in Spark.
Standalone, Client, Cluster
No, standalone is not a valid execution mode. It is a valid deployment mode, though.
Kubernetes, Local, Client
No, Kubernetes is a deployment mode, but not an execution mode.
Cluster, Server, Local
No, Server is not an execution mode.
Server, Standalone, Client
No, standalone and server are not execution modes.
More info: Apache Spark Internals - Learning Journal

NEW QUESTION 37
......

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