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Certification Topics of Databricks Associate Developer Apache Spark Exam?

• Then, Spark Architecture: Applied understanding (11%)
• To begin with, Spark Architecture: Conceptual understanding (17%)
• Lastly, Spark DataFrame API Applications (72%)

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
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. spark
  2. conf
  3. get
  4. "spark.sql.shuffle.partitions"
• B. 1. spark
  2. conf
  3. set
  4. "spark.sql.aggregate.partitions"
• C. 1. pyspark
  2. config
  3. set
  4. "spark.sql.shuffle.partitions"
• D. 1. spark
  2. conf
  3. set
  4. "spark.sql.shuffle.partitions"
• E. 1. pyspark
  2. config
  3. set
  4. spark.shuffle.partitions

Answer: D 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 # 131
Which of the following describes a difference between Spark's cluster and client execution modes?

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

Answer: D 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 # 132
Which of the following describes characteristics of the Spark UI?

• A. Via the Spark UI, workloads can be manually distributed across executors.
• B. Via the Spark UI, stage execution speed can be modified.
• C. The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.
• 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 # 133
The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:

• A. Spark will only broadcast DataFrames that are much smaller than the default value.
• B. spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 20)
• C. The correct option to write configurations is through spark.config and not spark.conf.
• D. The command is evaluated lazily and needs to be followed by an action.
• E. Spark will only apply the limit to threshold joins and not to other joins.
• F. The passed limit has the wrong variable type.

Answer: A Explanation:
Explanation
This is question is hard. Let's assess the different answers one-by-one.
Spark will only broadcast DataFrames that are much smaller than the default value.
This is correct. The default value is 10 MB (10485760 bytes). Since the configuration for spark.sql.autoBroadcastJoinThreshold expects a number in bytes (and not megabytes), the code block sets the limits to merely 20 bytes, instead of the requested 20 * 1024 * 1024 (= 20971520) bytes.
The command is evaluated lazily and needs to be followed by an action.
No, this command is evaluated right away!
Spark will only apply the limit to threshold joins and not to other joins.
There are no "threshold joins", so this option does not make any sense.
The correct option to write configurations is through spark.config and not spark.conf.
No, it is indeed spark.conf!
The passed limit has the wrong variable type.
The configuration expects the number of bytes, a number, as an input. So, the 20 provided in the code block is fine.
NEW QUESTION # 134
Which of the following code blocks returns a DataFrame that matches the multi-column DataFrame itemsDf, except that integer column itemId has been converted into a string column?

• A. itemsDf.withColumn("itemId", convert("itemId", "string"))
• B. itemsDf.select(cast("itemId", "string"))
• C. itemsDf.withColumn("itemId", col("itemId").cast("string"))
  (Correct)
• D. spark.cast(itemsDf, "itemId", "string")
• E. itemsDf.withColumn("itemId", col("itemId").convert("string"))

Answer: C Explanation:
Explanation
itemsDf.withColumn("itemId", col("itemId").cast("string"))
Correct. You can convert the data type of a column using the cast method of the Column class. Also note that you will have to use the withColumn method on itemsDf for replacing the existing itemId column with the new version that contains strings.
itemsDf.withColumn("itemId", col("itemId").convert("string"))
Incorrect. The Column object that col("itemId") returns does not have a convert method.
itemsDf.withColumn("itemId", convert("itemId", "string"))
Wrong. Spark's spark.sql.functions module does not have a convert method. The question is trying to mislead you by using the word "converted". Type conversion is also called "type casting". This may help you remember to look for a cast method instead of a convert method (see correct answer).
itemsDf.select(astype("itemId", "string"))
False. While astype is a method of Column (and an alias of Column.cast), it is not a method of pyspark.sql.functions (what the code block implies). In addition, the question asks to return a full DataFrame that matches the multi-column DataFrame itemsDf. Selecting just one column from itemsDf as in the code block would just return a single-column DataFrame.
spark.cast(itemsDf, "itemId", "string")
No, the Spark session (called by spark) does not have a cast method. You can find a list of all methods available for the Spark session linked in the documentation below.
More info:
- pyspark.sql.Column.cast - PySpark 3.1.2 documentation
- pyspark.sql.Column.astype - PySpark 3.1.2 documentation
- pyspark.sql.SparkSession - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION # 135
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