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Databricks Certified Associate Developer for Apache Spark 3.0 certification is a valuable credential for developers who want to demonstrate their expertise in Apache Spark and Databricks. It validates a developer's skills and knowledge in building and deploying Spark applications using Databricks and can help them advance their career in the big data industry.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q147-Q152):

NEW QUESTION # 147
In which order should the code blocks shown below be run in order to return the number of records that are not empty in column value in the DataFrame resulting from an inner join of DataFrame transactionsDf and itemsDf on columns productId and itemId, respectively?
1. .filter(~isnull(col('value')))
2. .count()
3. transactionsDf.join(itemsDf, col("transactionsDf.productId")==col("itemsDf.itemId"))
4. transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId, how='inner')
5. .filter(col('value').isnotnull())
6. .sum(col('value'))

A. 3, 5, 2B. 3, 1, 6C. 4, 1, 2D. 4, 6E. 3, 1, 2

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId,
how='inner').filter(~isnull(col('value'))).count()
Expressions col("transactionsDf.productId") and col("itemsDf.itemId") are invalid. col() does not accept the name of a DataFrame, only column names.
Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 148
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. set
4. "spark.sql.shuffle.partitions"B. 1. pyspark
2. config
3. set
4. spark.shuffle.partitionsC. 1. spark
2. conf
3. set
4. "spark.sql.aggregate.partitions"D. 1. spark
2. conf
3. get
4. "spark.sql.shuffle.partitions"E. 1. pyspark
2. config
3. set
4. "spark.sql.shuffle.partitions"

Answer: A

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 # 149
Which of the following statements about executors is correct?

A. Executors are launched by the driver.B. Executors stop upon application completion by default.C. An executor can serve multiple applications.D. Executors store data in memory only.E. Each node hosts a single executor.

Answer: B

Explanation:
Explanation
Executors stop upon application completion by default.
Correct. Executors only persist during the lifetime of an application.
A notable exception to that is when Dynamic Resource Allocation is enabled (which it is not by default). With Dynamic Resource Allocation enabled, executors are terminated when they are idle, independent of whether the application has been completed or not.
An executor can serve multiple applications.
Wrong. An executor is always specific to the application. It is terminated when the application completes (exception see above).
Each node hosts a single executor.
No. Each node can host one or more executors.
Executors store data in memory only.
No. Executors can store data in memory or on disk.
Executors are launched by the driver.
Incorrect. Executors are launched by the cluster manager on behalf of the driver.
More info: Job Scheduling - Spark 3.1.2 Documentation, How Applications are Executed on a Spark Cluster | Anatomy of a Spark Application | InformIT, and Spark Jargon for Starters. This blog is to clear some of the... | by Mageswaran D | Medium

NEW QUESTION # 150
Which of the following code blocks returns a DataFrame that has all columns of DataFrame transactionsDf and an additional column predErrorSquared which is the squared value of column predError in DataFrame transactionsDf?

A. transactionsDf.withColumnRenamed("predErrorSquared", pow(predError, 2))B. transactionsDf.withColumn("predErrorSquared", "predError"**2)C. transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))D. transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))E. transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))

Answer: E

Explanation:
Explanation
While only one of these code blocks works, the DataFrame API is pretty flexible when it comes to accepting columns into the pow() method. The following code blocks would also work:
transactionsDf.withColumn("predErrorSquared", pow("predError", 2))
transactionsDf.withColumn("predErrorSquared", pow("predError", lit(2))) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/spark_practice_tests_code/#1/26.html ,
https://bit.ly/sparkpracticeexams_import_instructions)

NEW QUESTION # 151
The code block shown below should read all files with the file ending .png in directory path into Spark.
Choose the answer that correctly fills the blanks in the code block to accomplish this.
spark.__1__.__2__(__3__).option(__4__, "*.png").__5__(path)

A. 1. read
2. format
3. binaryFile
4. pathGlobFilter
5. loadB. 1. open
2. as
3. "binaryFile"
4. "pathGlobFilter"
5. loadC. 1. read()
2. format
3. "binaryFile"
4. "recursiveFileLookup"
5. loadD. 1. open
2. format
3. "image"
4. "fileType"
5. openE. 1. read
2. format
3. "binaryFile"
4. "pathGlobFilter"
5. load

Answer: E

Explanation:
Explanation
Correct code block:
spark.read.format("binaryFile").option("recursiveFileLookup", "*.png").load(path) Spark can deal with binary files, like images. Using the binaryFile format specification in the SparkSession's read API is the way to read in those files. Remember that, to access the read API, you need to start the command with spark.read. The pathGlobFilter option is a great way to filter files by name (and ending). Finally, the path can be specified using the load operator - the open operator shown in one of the answers does not exist.

NEW QUESTION # 152
......

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