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The exam consists of multiple-choice questions and is conducted online. The duration of the exam is two hours, and the passing score is 70%. Once individuals pass the exam, they will receive a certification from Databricks that verifies their knowledge and skills in using Spark. This certification can be used to demonstrate proficiency to potential employers and clients and can help individuals advance their careers in the field of data analytics and processing.

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Databricks Associate Developer Apache Spark Exam

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The Databricks Associate-Developer-Apache-Spark certification exam is based on Apache Spark 3.0, the latest version of the open-source distributed computing system. The exam covers a wide range of topics related to Apache Spark, including Spark architecture, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX. The exam is designed to test the candidate's knowledge and ability to develop and deploy Apache Spark applications using Databricks.

Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
In which order should the code blocks shown below be run in order to assign articlesDf a DataFrame that lists all items in column attributes ordered by the number of times these items occur, from most to least often?
Sample of DataFrame articlesDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+

A. 4, 5
B. 5, 2
C. 2, 5, 4
D. 1. articlesDf = articlesDf.groupby("col")
2. articlesDf = articlesDf.select(explode(col("attributes")))
3. articlesDf = articlesDf.orderBy("count").select("col")
4. articlesDf = articlesDf.sort("count",ascending=False).select("col")
5. articlesDf = articlesDf.groupby("col").count()
E. 2, 5, 3
F. 2, 3, 4

Answer: F

NEW QUESTION # 85
Which of the following describes slots?

A. To optimize I/O performance, Spark stores data on disk in multiple slots.
B. A Java Virtual Machine (JVM) working as an executor can be considered as a pool of slots for task execution.
C. Slots are dynamically created and destroyed in accordance with an executor's workload.
D. A slot is always limited to a single core.
Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.

Answer: B

Explanation:
Explanation
Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.
Wrong, executors communicate with the driver directly.
Slots are dynamically created and destroyed in accordance with an executor's workload.
No, Spark does not actively create and destroy slots in accordance with the workload. Per executor, slots are made available in accordance with how many cores per executor (property spark.executor.cores) and how many CPUs per task (property spark.task.cpus) the Spark configuration calls for.
A slot is always limited to a single core.
No, a slot can span multiple cores. If a task would require multiple cores, it would have to be executed through a slot that spans multiple cores.
In Spark documentation, "core" is often used interchangeably with "thread", although "thread" is the more accurate word. A single physical core may be able to make multiple threads available. So, it is better to say that a slot can span multiple threads.
To optimize I/O performance, Spark stores data on disk in multiple slots.
No - Spark stores data on disk in multiple partitions, not slots.
More info: Spark Architecture | Distributed Systems Architecture

NEW QUESTION # 86
The code block displayed below contains an error. The code block below is intended to add a column itemNameElements to DataFrame itemsDf that includes an array of all words in column itemName. Find the error.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-------------------+
2.|itemId|itemName |supplier |
3.+------+----------------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |YetiX |
6.|3 |Outdoors Backpack |Sports Company Inc.|
7.+------+----------------------------------+-------------------+
Code block:
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))

A. Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument
"," needs to be passed to the split method.
B. The expressions "itemNameElements" and split("itemName") need to be swapped.
C. Operator withColumnRenamed needs to be replaced with operator withColumn and the split method needs to be replaced by the splitString method.
D. All column names need to be wrapped in the col() operator.
E. Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument "
" needs to be passed to the split method.

Answer: E

Explanation:
Explanation
Correct code block:
itemsDf.withColumn("itemNameElements", split("itemName"," "))
Output of code block:
+------+----------------------------------+-------------------+------------------------------------------+
|itemId|itemName |supplier |itemNameElements |
+------+----------------------------------+-------------------+------------------------------------------+
|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in, the, Snow]|
|2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress] |
|3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |
+------+----------------------------------+-------------------+------------------------------------------+ The key to solving this question is that the split method definitely needs a second argument here (also look at the link to the documentation below). Given the values in column itemName in DataFrame itemsDf, this should be a space character " ". This is the character we need to split the words in the column.
More info: pyspark.sql.functions.split - PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 87
The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that correctly fills the blanks in the code block to accomplish this.

A. transactionsDf.filter(transactionsDf.storeId==25)
B. transactionsDf.select(transactionsDf.storeId!=25)
C. transactionsDf.drop(transactionsDf.storeId==25)
D. transactionsDf.remove(transactionsDf.storeId==25)
E. transactionsDf.where(transactionsDf.storeId!=25)

Answer: E

Explanation:
Explanation
transactionsDf.where(transactionsDf.storeId!=25)
Correct. DataFrame.where() is an alias for the DataFrame.filter() method. Using this method, it is straightforward to filter out rows that do not have value 25 in column storeId.
transactionsDf.select(transactionsDf.storeId!=25)
Wrong. The select operator allows you to build DataFrames column-wise, but when using it as shown, it does not filter out rows.
transactionsDf.filter(transactionsDf.storeId==25)
Incorrect. Although the filter expression works for filtering rows, the == in the filtering condition is inappropriate. It should be != instead.
transactionsDf.drop(transactionsDf.storeId==25)
No. DataFrame.drop() is used to remove specific columns, but not rows, from the DataFrame.
transactionsDf.remove(transactionsDf.storeId==25)
False. There is no DataFrame.remove() operator in PySpark.
More info: pyspark.sql.DataFrame.where - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

NEW QUESTION # 88
The code block shown below should return a column that indicates through boolean variables whether rows in DataFrame transactionsDf have values greater or equal to 20 and smaller or equal to
30 in column storeId and have the value 2 in column productId. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__((__2__.__3__) __4__ (__5__))

A. 1. select
2. col("storeId")
3. between(20, 30)
4. and
5. col("productId")==2
B. 1. where
2. col("storeId")
3. geq(20).leq(30)
4. &
5. col("productId")==2
C. 1. select
2. col("storeId")
3. between(20, 30)
4. &&
5. col("productId")=2
D. 1. select
2. "storeId"
3. between(20, 30)
4. &&
5. col("productId")==2
E. 1. select
2. col("storeId")
3. between(20, 30)
4. &
5. col("productId")==2

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.select((col("storeId").between(20, 30)) & (col("productId")==2)) Although this question may make you think that it asks for a filter or where statement, it does not. It asks explicity to return a column with booleans - this should point you to the select statement.
Another trick here is the rarely used between() method. It exists and resolves to ((storeId >= 20) AND (storeId
<= 30)) in SQL. geq() and leq() do not exist.
Another riddle here is how to chain the two conditions. The only valid answer here is &. Operators like && or and are not valid. Other boolean operators that would be valid in Spark are | and.
Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 89
......

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