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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q107-Q112):NEW QUESTION # 107
Which of the following describes tasks?
Answer: B
Explanation:
Explanation
Tasks get assigned to the executors by the driver.
Correct! Or, in other words: Executors take the tasks that they were assigned to by the driver, run them over partitions, and report the their outcomes back to the driver.
Tasks transform jobs into DAGs.
No, this statement disrespects the order of elements in the Spark hierarchy. The Spark driver transforms jobs into DAGs. Each job consists of one or more stages. Each stage contains one or more tasks.
A task is a collection of rows.
Wrong. A partition is a collection of rows. Tasks have little to do with a collection of rows. If anything, a task processes a specific partition.
A task is a command sent from the driver to the executors in response to a transformation.
Incorrect. The Spark driver does not send anything to the executors in response to a transformation, since transformations are evaluated lazily. So, the Spark driver would send tasks to executors only in response to actions.
A task is a collection of slots.
No. Executors have one or more slots to process tasks and each slot can be assigned a task.
NEW QUESTION # 108
Which of the following describes Spark actions?
Answer: C
Explanation:
Explanation
The driver receives data upon request by actions.
Correct! Actions trigger the distributed execution of tasks on executors which, upon task completion, transfer result data back to the driver.
Actions are Spark's way of exchanging data between executors.
No. In Spark, data is exchanged between executors via shuffles.
Writing data to disk is the primary purpose of actions.
No. The primary purpose of actions is to access data that is stored in Spark's RDDs and return the data, often in aggregated form, back to the driver.
Actions are Spark's way of modifying RDDs.
Incorrect. Firstly, RDDs are immutable - they cannot be modified. Secondly, Spark generates new RDDs via transformations and not actions.
Stage boundaries are commonly established by actions.
Wrong. A stage boundary is commonly established by a shuffle, for example caused by a wide transformation.
NEW QUESTION # 109
The code block displayed below contains an error. The code block should read the csv file located at path data/transactions.csv into DataFrame transactionsDf, using the first row as column header and casting the columns in the most appropriate type. Find the error.
First 3 rows of transactions.csv:
1.transactionId;storeId;productId;name
2.1;23;12;green grass
3.2;35;31;yellow sun
4.3;23;12;green grass
Code block:
transactionsDf = spark.read.load("data/transactions.csv", sep=";", format="csv", header=True)
Answer: B
Explanation:
Explanation
Correct code block:
transactionsDf = spark.read.load("data/transactions.csv", sep=";", format="csv", header=True, inferSchema=True) By default, Spark does not infer the schema of the CSV (since this usually takes some time). So, you need to add the inferSchema=True option to the code block.
More info: pyspark.sql.DataFrameReader.csv - PySpark 3.1.2 documentation
NEW QUESTION # 110
Which of the following describes a difference between Spark's cluster and client execution modes?
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 # 111
The code block shown below should return a single-column DataFrame with a column named consonant_ct that, for each row, shows the number of consonants in column itemName of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.select(__1__(__2__(__3__(__4__), "a|e|i|o|u|\s", "")).__5__("consonant_ct"))
2. regexp_replace
3. lower
4. "itemName"
5. aliasB. 1. size
2. regexp_extract
3. lower
4. col("itemName")
5. aliasC. 1. lower
2. regexp_replace
3. length
4. "itemName"
5. aliasD. 1. length
2. regexp_extract
3. upper
4. col("itemName")
5. asE. 1. length
2. regexp_replace
3. lower
4. col("itemName")
5. alias
Answer: E
Explanation:
Explanation
Correct code block:
itemsDf.select(length(regexp_replace(lower(col("itemName")), "a|e|i|o|u|\s", "")).alias("consonant_ct")) Returned DataFrame:
+------------+
|consonant_ct|
+------------+
| 19|
| 16|
| 10|
+------------+
This question tries to make you think about the string functions Spark provides and in which order they should be applied. Arguably the most difficult part, the regular expression "a|e|i|o|u|
\s", is not a numbered blank. However, if you are not familiar with the string functions, it may be a good idea to review those before the exam.
The size operator and the length operator can easily be confused. size works on arrays, while length works on strings. Luckily, this is something you can read up about in the documentation.
The code block works by first converting all uppercase letters in column itemName into lowercase (the lower() part). Then, it replaces all vowels by "nothing" - an empty character "" (the regexp_replace() part). Now, only lowercase characters without spaces are included in the DataFrame. Then, per row, the length operator counts these remaining characters. Note that column itemName in itemsDf does not include any numbers or other characters, so we do not need to make any provisions for these. Finally, by using the alias() operator, we rename the resulting column to consonant_ct.
More info:
- lower: pyspark.sql.functions.lower - PySpark 3.1.2 documentation
- regexp_replace: pyspark.sql.functions.regexp_replace - PySpark 3.1.2 documentation
- length: pyspark.sql.functions.length - PySpark 3.1.2 documentation
- alias: pyspark.sql.Column.alias - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
NEW QUESTION # 112
......
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