Associate-Developer-Apache-Spark Questions PDF [2023] Use Valid New dump to Clear Exam [Q85-Q100]

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Associate-Developer-Apache-Spark Questions PDF [2023] Use Valid New dump to Clear Exam

Passing Databricks Associate-Developer-Apache-Spark Exam Using 2023 Practice Tests

How to become Databricks Associate Developer Apache Spark certified

To create applications that run on the Databricks platform. You’ll learn how to use the Spark shell to interact with the data stored in Databricks, the Databricks DataFrame API, and the Databricks Query Language (DQL). You’ll also gain exposure to a variety of machine learning and data mining algorithms that can be used to solve real-world problems.
The Databricks Associate Developer Apache Spark certification is a great opportunity to get hands-on experience with the most popular big data platform. It’s a two-day certification course that teaches you how to use the latest version of Apache Spark. Databricks Associate Developer Apache Spark exam dumps are the key of success.

The Exam cost of Databricks Associate Developer Apache Spark Exam?

The cost of the Databricks Associate Developer Apache Spark Exam is 200 USD per attempt.

NO.85 Which of the following statements about Spark’s configuration properties is incorrect?

The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property.

The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property.

The default value for spark.sql.autoBroadcastJoinThreshold is 10MB.

The default number of partitions to use when shuffling data for joins or aggregations is 300.

The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.

NO.86 The code block displayed below contains an error. The code block should configure Spark to split data in 20 parts when exchanging data between executors for joins or aggregations. Find the error.
Code block:
spark.conf.set(spark.sql.shuffle.partitions, 20)

The code block uses the wrong command for setting an option.

The code block sets the wrong option.

The code block expresses the option incorrectly.

The code block sets the incorrect number of parts.

The code block is missing a parameter.

NO.87 Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?

itemsDf.write.avro(fileLocation)

itemsDf.write.format(“avro”).mode(“ignore”).save(fileLocation)

itemsDf.write.format(“avro”).mode(“errorifexists”).save(fileLocation)

itemsDf.save.format(“avro”).mode(“ignore”).write(fileLocation)

spark.DataFrameWriter(itemsDf).format(“avro”).write(fileLocation)

NO.88 Which of the following describes tasks?

A task is a command sent from the driver to the executors in response to a transformation.

Tasks transform jobs into DAGs.

A task is a collection of slots.

A task is a collection of rows.

Tasks get assigned to the executors by the driver.

1. withColumn
2. “itemNameBetweenSeparators”
3. split
4. “itemName”
5. 4
(Correct)

1. withColumnRenamed
2. “itemNameBetweenSeparators”
3. split
4. “itemName”
5. 4

1. withColumnRenamed
2. “itemName”
3. split
4. “itemNameBetweenSeparators”
5. 4

1. withColumn
2. “itemNameBetweenSeparators”
3. split
4. “itemName”
5. 5

1. withColumn
2. itemNameBetweenSeparators
3. str_split
4. “itemName”
5. 5

NO.90 The code block displayed below contains an error. The code block is intended to perform an outer join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively.
Find the error.
Code block:
transactionsDf.join(itemsDf, [itemsDf.itemId, transactionsDf.productId], “outer”)

The “outer” argument should be eliminated, since “outer” is the default join type.

The join type needs to be appended to the join() operator, like join().outer() instead of listing it as the last argument inside the join() call.

The term [itemsDf.itemId, transactionsDf.productId] should be replaced by itemsDf.itemId == transactionsDf.productId.

The term [itemsDf.itemId, transactionsDf.productId] should be replaced by itemsDf.col(“itemId”) == transactionsDf.col(“productId”).

The “outer” argument should be eliminated from the call and join should be replaced by joinOuter.

NO.91 Which of the following code blocks returns a copy of DataFrame itemsDf where the column supplier has been renamed to manufacturer?

itemsDf.withColumn([“supplier”, “manufacturer”])

itemsDf.withColumn(“supplier”).alias(“manufacturer”)

itemsDf.withColumnRenamed(“supplier”, “manufacturer”)

itemsDf.withColumnRenamed(col(“manufacturer”), col(“supplier”))

itemsDf.withColumnsRenamed(“supplier”, “manufacturer”)

Explanation
itemsDf.withColumnRenamed(“supplier”, “manufacturer”)
Correct! This uses the relatively trivial DataFrame method withColumnRenamed for renaming column supplier to column manufacturer.
Note that the question asks for “a copy of DataFrame itemsDf”. This may be confusing if you are not familiar with Spark yet. RDDs (Resilient Distributed Datasets) are the foundation of Spark DataFrames and are immutable. As such, DataFrames are immutable, too. Any command that changes anything in the DataFrame therefore necessarily returns a copy, or a new version, of it that has the changes applied.
itemsDf.withColumnsRenamed(“supplier”, “manufacturer”)
Incorrect. Spark’s DataFrame API does not have a withColumnsRenamed() method.
itemsDf.withColumnRenamed(col(“manufacturer”), col(“supplier”))
No. Watch out – although the col() method works for many methods of the DataFrame API, withColumnRenamed is not one of them. As outlined in the documentation linked below, withColumnRenamed expects strings.
itemsDf.withColumn([“supplier”, “manufacturer”])
Wrong. While DataFrame.withColumn() exists in Spark, it has a different purpose than renaming columns.
withColumn is typically used to add columns to DataFrames, taking the name of the new column as a first, and a Column as a second argument. Learn more via the documentation that is linked below.
itemsDf.withColumn(“supplier”).alias(“manufacturer”)
No. While DataFrame.withColumn() exists, it requires 2 arguments. Furthermore, the alias() method on DataFrames would not help the cause of renaming a column much. DataFrame.alias() can be useful in addressing the input of join statements. However, this is far outside of the scope of this question. If you are curious nevertheless, check out the link below.
More info: pyspark.sql.DataFrame.withColumnRenamed – PySpark 3.1.1 documentation, pyspark.sql.DataFrame.withColumn – PySpark 3.1.1 documentation, and pyspark.sql.DataFrame.alias – PySpark 3.1.2 documentation (https://bit.ly/3aSB5tm , https://bit.ly/2Tv4rbE , https://bit.ly/2RbhBd2) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/spark_practice_tests_code/#1/31.html ,
https://bit.ly/sparkpracticeexams_import_instructions)

NO.92 Which of the following options describes the responsibility of the executors in Spark?

The executors accept jobs from the driver, analyze those jobs, and return results to the driver.

The executors accept tasks from the driver, execute those tasks, and return results to the cluster manager.

The executors accept tasks from the driver, execute those tasks, and return results to the driver.

The executors accept tasks from the cluster manager, execute those tasks, and return results to the driver.

The executors accept jobs from the driver, plan those jobs, and return results to the cluster manager.

NO.93 Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?

spark.read.schema(fileSchema).format(“parquet”).load(filePath)

spark.read.schema(“fileSchema”).format(“parquet”).load(filePath)

spark.read().schema(fileSchema).parquet(filePath)

spark.read().schema(fileSchema).format(parquet).load(filePath)

spark.read.schema(fileSchema).open(filePath)

NO.94 The code block shown below should return a copy of DataFrame transactionsDf with an added column cos.
This column should have the values in column value converted to degrees and having the cosine of those converted values taken, rounded to two decimals. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__, round(__3__(__4__(__5__)),2))

1. withColumn
2. col(“cos”)
3. cos
4. degrees
5. transactionsDf.value

1. withColumnRenamed
2. “cos”
3. cos
4. degrees
5. “transactionsDf.value”

1. withColumn
2. “cos”
3. cos
4. degrees
5. transactionsDf.value

1. withColumn
2. col(“cos”)
3. cos
4. degrees
5. col(“value”)
E
. 1. withColumn
2. “cos”
3. degrees
4. cos
5. col(“value”)

NO.95 The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matching column names and inserting null values where column names do not appear in both DataFrames. Find the error.
Sample of DataFrame transactionsDfMonday:
1.+————-+———+—–+——-+———+—-+
2.|transactionId|predError|value|storeId|productId| f|
3.+————-+———+—–+——-+———+—-+
4.| 5| null| null| null| 2|null|
5.| 6| 3| 2| 25| 2|null|
6.+————-+———+—–+——-+———+—-+
Sample of DataFrame transactionsDfTuesday:
1.+——-+————-+———+—–+
2.|storeId|transactionId|productId|value|
3.+——-+————-+———+—–+
4.| 25| 1| 1| 4|
5.| 2| 2| 2| 7|
6.| 3| 4| 2| null|
7.| null| 5| 2| null|
8.+——-+————-+———+—–+
Code block:
sc.union([transactionsDfMonday, transactionsDfTuesday])

The DataFrames’ RDDs need to be passed into the sc.union method instead of the DataFrame variable names.

Instead of union, the concat method should be used, making sure to not use its default arguments.

Instead of the Spark context, transactionDfMonday should be called with the join method instead of the union method, making sure to use its default arguments.

Instead of the Spark context, transactionDfMonday should be called with the union method.

Instead of the Spark context, transactionDfMonday should be called with the unionByName method instead of the union method, making sure to not use its default arguments.

NO.96 Which of the following statements about the differences between actions and transformations is correct?

Actions are evaluated lazily, while transformations are not evaluated lazily.

Actions generate RDDs, while transformations do not.

Actions do not send results to the driver, while transformations do.

Actions can be queued for delayed execution, while transformations can only be processed immediately.

Actions can trigger Adaptive Query Execution, while transformation cannot.

NO.97 Which of the following describes a difference between Spark’s cluster and client execution modes?

In cluster mode, the cluster manager resides on a worker node, while it resides 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.

In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.

In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.

In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.

itemsDf.filter(col(supplier).not_contains(‘X’)).select(supplier).distinct()

itemsDf.select(~col(‘supplier’).contains(‘X’)).distinct()

itemsDf.filter(not(col(‘supplier’).contains(‘X’))).select(‘supplier’).unique()

itemsDf.filter(~col(‘supplier’).contains(‘X’)).select(‘supplier’).distinct()

itemsDf.filter(!col(‘supplier’).contains(‘X’)).select(col(‘supplier’)).unique()

NO.99 Which of the following code blocks prints out in how many rows the expression Inc. appears in the string-type column supplier of DataFrame itemsDf?

1.counter = 0
2.
3.for index, row in itemsDf.iterrows():
4. if ‘Inc.’ in row[‘supplier’]:
5. counter = counter + 1
6.
7.print(counter)

1.counter = 0
2.
3.def count(x):
4. if ‘Inc.’ in x[‘supplier’]:
5. counter = counter + 1
6.
7.itemsDf.foreach(count)
8.print(counter)

print(itemsDf.foreach(lambda x: ‘Inc.’ in x))

print(itemsDf.foreach(lambda x: ‘Inc.’ in x).sum())

1.accum=sc.accumulator(0)
2.
3.def check_if_inc_in_supplier(row):
4. if ‘Inc.’ in row[‘supplier’]:
5. accum.add(1)
6.
7.itemsDf.foreach(check_if_inc_in_supplier)
8.print(accum.value)

NO.100 The code block displayed below contains an error. The code block should create DataFrame itemsAttributesDf which has columns itemId and attribute and lists every attribute from the attributes column in DataFrame itemsDf next to the itemId of the respective row in itemsDf. Find the error.
A sample of DataFrame itemsDf is below.

Code block:
itemsAttributesDf = itemsDf.explode(“attributes”).alias(“attribute”).select(“attribute”, “itemId”)

Since itemId is the index, it does not need to be an argument to the select() method.

The alias() method needs to be called after the select() method.

The explode() method expects a Column object rather than a string.

explode() is not a method of DataFrame. explode() should be used inside the select() method instead.

The split() method should be used inside the select() method instead of the explode() method.

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Associate-Developer-Apache-Spark Questions PDF [2023] Use Valid New dump to Clear Exam [Q85-Q100]

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New Way To Prepare For Your Databricks Associate Developer Apache Spark Exam

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How to become Databricks Associate Developer Apache Spark certified

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