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Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps

NEW QUESTION 54
Which of the following describes a difference between Spark's cluster and client execution modes?

• A. In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.
• B. In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.
• C. In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.
• D. In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.
• E. In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.

Answer: B

Explanation:
Explanation
In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.
Correct. The idea of Spark's client mode is that workloads can be executed from an edge node, also known as gateway machine, from outside the cluster. The most common way to execute Spark however is in cluster mode, where the driver resides on a worker node.
In practice, in client mode, there are tight constraints about the data transfer speed relative to the data transfer speed between worker nodes in the cluster. Also, any job in that is executed in client mode will fail if the edge node fails. For these reasons, client mode is usually not used in a production environment.
In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client execution mode.
No. In both execution modes, the cluster manager may reside on a worker node, but it does not reside on an edge node in client mode.
In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.
This is incorrect. Only the driver runs on gateway nodes (also known as "edge nodes") in client mode, but not the executor processes.
In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.
No, in client mode, the Spark driver is not co-located with the driver. The whole point of client mode is that the driver is outside the cluster and not associated with the resource that manages the cluster (the machine that runs the cluster manager).
In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.
No, it is exactly the opposite: There are no gateway machines in cluster mode, but in client mode, they host the driver.

NEW QUESTION 55
Which of the following is a problem with using accumulators?

• A. Only unnamed accumulators can be inspected in the Spark UI.
• B. 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.
• C. Only numeric values can be used in accumulators.
• D. Accumulators do not obey lazy evaluation.
• E. Accumulator values can only be read by the driver, but not by executors.

Answer: E

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 56
The code block shown below should return a one-column DataFrame where the column storeId is converted to string type. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__.__3__(__4__))

• A. 1. select
  2. col("storeId")
  3. as
  4. StringType
• B. 1. cast
  2. "storeId"
  3. as
  4. StringType()
• C. 1. select
  2. col("storeId")
  3. cast
  4. StringType()
• D. 1. select
  2. storeId
  3. cast
  4. StringType()
• E. 1. select
  2. col("storeId")
  3. cast
  4. StringType

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.select(col("storeId").cast(StringType()))
Solving this question involves understanding that, when using types from the pyspark.sql.types such as StringType, these types need to be instantiated when using them in Spark, or, in simple words, they need to be followed by parentheses like so: StringType(). You could also use .cast("string") instead, but that option is not given here.
More info: pyspark.sql.Column.cast - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION 57
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