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The exam is based on Apache Spark 3.0, which is the latest version of the open-source big data processing framework. It consists of 60 multiple-choice questions that assess a developer's understanding of Apache Spark and their ability to use Databricks to build and deploy Spark applications. The exam is designed to be challenging but fair, and it tests both conceptual knowledge and practical skills.

To prepare for the certification exam, candidates can take advantage of a variety of resources, including training courses, practice exams, and study guides. Databricks offers a range of training courses designed specifically for the certification exam, as well as a practice exam to help candidates assess their readiness. With the right preparation, candidates can earn their Databricks Certified Associate Developer for Apache Spark 3.0 credential and demonstrate their expertise in one of the most widely used big data processing frameworks.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?

A. itemsDf.rdd.storeCopy()B. cache(itemsDf)C. itemsDf.cache(eager=True)D. itemsDf.cache().count()E. itemsDf.cache().filter()

Answer: D

Explanation:
Explanation
Caching means storing a copy of a partition on an executor, so it can be accessed quicker by subsequent operations, instead of having to be recalculated. cache() is a lazily-evaluated method of the DataFrame. Since count() is an action (while filter() is not), it triggers the caching process.
More info: pyspark.sql.DataFrame.cache - PySpark 3.1.2 documentation, Learning Spark, 2nd Edition, Chapter 7 Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 94
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. cast
4. StringType()B. 1. select
2. col("storeId")
3. as
4. StringTypeC. 1. select
2. storeId
3. cast
4. StringType()D. 1. cast
2. "storeId"
3. as
4. StringType()E. 1. select
2. col("storeId")
3. cast
4. StringType

Answer: A

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 # 95
Which of the following describes tasks?

A. Tasks get assigned to the executors by the driver.B. A task is a command sent from the driver to the executors in response to a transformation.C. Tasks transform jobs into DAGs.D. A task is a collection of rows.E. A task is a collection of slots.

Answer: A

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 # 96
The code block shown below should add a column itemNameBetweenSeparators to DataFrame itemsDf. The column should contain arrays of maximum 4 strings. The arrays should be composed of the values in column itemsDf which are separated at - or whitespace characters. Choose the answer that correctly fills the blanks in the code block to accomplish this.
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.__1__(__2__, __3__(__4__, "[\s\-]", __5__))

A. 1. withColumn
2. "itemNameBetweenSeparators"
3. split
4. "itemName"
5. 5B. 1. withColumn
2. itemNameBetweenSeparators
3. str_split
4. "itemName"
5. 5C. 1. withColumn
2. "itemNameBetweenSeparators"
3. split
4. "itemName"
5. 4
(Correct)D. 1. withColumnRenamed
2. "itemName"
3. split
4. "itemNameBetweenSeparators"
5. 4E. 1. withColumnRenamed
2. "itemNameBetweenSeparators"
3. split
4. "itemName"
5. 4

Answer: C

Explanation:
Explanation
This question deals with the parameters of Spark's split operator for strings.
To solve this question, you first need to understand the difference between DataFrame.withColumn() and DataFrame.withColumnRenamed(). The correct option here is DataFrame.withColumn() since, according to the question, we want to add a column and not rename an existing column. This leaves you with only 3 answers to consider.
The second gap should be filled with the name of the new column to be added to the DataFrame. One of the remaining answers states the column name as itemNameBetweenSeparators, while the other two state it as "itemNameBetweenSeparators". The correct option here is
"itemNameBetweenSeparators", since the other option would let Python try to interpret itemNameBetweenSeparators as the name of a variable, which we have not defined. This leaves you with 2 answers to consider.
The decision boils down to how to fill gap 5. Either with 4 or with 5. The question asks for arrays of maximum four strings. The code in gap 5 relates to the limit parameter of Spark's split operator (see documentation linked below). The documentation states that "the resulting array's length will not be more than limit", meaning that we should pick the answer option with 4 as the code in the fifth gap here.
On a side note: One answer option includes a function str_split. This function does not exist in pySpark.
More info: pyspark.sql.functions.split - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

NEW QUESTION # 97
Which of the following code blocks reads all CSV files in directory filePath into a single DataFrame, with column names defined in the CSV file headers?
Content of directory filePath:
1._SUCCESS
2._committed_2754546451699747124
3._started_2754546451699747124
4.part-00000-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-298-1-c000.csv.gz
5.part-00001-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-299-1-c000.csv.gz
6.part-00002-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-300-1-c000.csv.gz
7.part-00003-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-301-1-c000.csv.gz spark.option("header",True).csv(filePath)

A. spark.read.format("csv").option("header",True).load(filePath)B. spark.read.load(filePath)C. spark.read.format("csv").option("header",True).option("compression","zip").load(filePath)D. spark.read().option("header",True).load(filePath)

Answer: A

Explanation:
Explanation
The files in directory filePath are partitions of a DataFrame that have been exported using gzip compression.
Spark automatically recognizes this situation and imports the CSV files as separate partitions into a single DataFrame. It is, however, necessary to specify that Spark should load the file headers in the CSV with the header option, which is set to False by default.

NEW QUESTION # 98
......

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