Associate-Developer-Apache-Spark Archives

31 Jan, 2023 By admin

Jan-2023 Free Databricks Associate-Developer-Apache-Spark Exam Question Practice Exams [Q17-Q32]

Jan-2023 Free Databricks Associate-Developer-Apache-Spark Exam Question Practice Exams

Ace Associate-Developer-Apache-Spark Certification with 179 Actual Questions

What is the Databricks Associate Developer Apache Spark Exam?

The Databricks Associate Developer Apache Spark Exam is a certification that can be earned by anyone who has successfully completed the Databricks Associate Developer Apache Spark Certification Training. The exam covers all the material that was covered in the training. The exam is designed to test your knowledge of the concepts, skills, and abilities that you learned during the course.

Do you want to become a Data Engineer or a Spark Architect? If so, then the Databricks Associate Developer Apache Spark Exam is a must-pass. The Databricks Associate Developer Apache Spark Exam is designed to help you develop a complete understanding of the technology used by the Databricks platform. You will learn about the basics of Spark, including the Spark programming language, Spark SQL, Spark Streaming, and the Spark ecosystem. Databricks Associate Developer Apache Spark exam dumps are the choice of champions.

The Databricks Associate Developer Apache Spark Exam is a test that aims to assess whether you have the knowledge required to become a certified Apache Spark developer. The Databricks Associate Developer Apache Spark Exam consists of two parts: the first part tests your knowledge of the fundamentals of the Apache Spark framework and the second part tests your ability to apply this knowledge. This post will help you get a head start in preparing for the Databricks Associate Developer Apache Spark Exam. The executors disk division actions documentation frame for the executor syntax variables object return allowed partition for the fit output transformation to induce couple of manager and evaluated expected safely, lazily named nodes broadcast operations for correctly mock driver.

How Databricks Associate Developer Apache Spark Exam can help you?

As the name suggests, it is a special exam that is designed to help the candidates who want to get the job as an Associate Developer in the company, Databricks. The exam is conducted by the company itself and the candidates can register themselves for the exam. The candidates have to prepare for the exam with the help of the given syllabus and the study material. The candidate should have a good knowledge of the concepts related to the big data and the candidates should have a good knowledge of the programming language like Java, Python and R. The candidates can also check the sample papers and the past papers to know about the level of difficulty. Databricks Associate Developer Apache Spark exam dumps will help you prepare for this exam.

Apache Spark is a powerful open source data processing engine that provides a unified platform for data analytics, machine learning, and streaming applications. Spark is used to process massive datasets to find patterns and trends in the data, as well as perform data transformations, analyses, and visualizations. The big data industry is growing rapidly, and companies of all sizes are increasingly adopting Spark to analyze their large datasets. In this article, we will discuss about Databricks Associate Developer Apache Spark Exam and how it can help you to become an expert in the world of Big Data.

NO.17 Which of the following code blocks returns only rows from DataFrame transactionsDf in which values in column productId are unique?

transactionsDf.distinct(“productId”)

transactionsDf.dropDuplicates(subset=[“productId”])

transactionsDf.drop_duplicates(subset=”productId”)

transactionsDf.unique(“productId”)

transactionsDf.dropDuplicates(subset=”productId”)

NO.18 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.19 Which of the following code blocks performs a join in which the small DataFrame transactionsDf is sent to all executors where it is joined with DataFrame itemsDf on columns storeId and itemId, respectively?

itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, “right_outer”)

itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, “broadcast”)

itemsDf.merge(transactionsDf, “itemsDf.itemId == transactionsDf.storeId”, “broadcast”)

itemsDf.join(broadcast(transactionsDf), itemsDf.itemId == transactionsDf.storeId)

itemsDf.join(transactionsDf, broadcast(itemsDf.itemId == transactionsDf.storeId))

NO.20 Which of the following describes slots?

Slots are dynamically created and destroyed in accordance with an executor’s workload.

To optimize I/O performance, Spark stores data on disk in multiple slots.

A Java Virtual Machine (JVM) working as an executor can be considered as a pool of slots for task execution.

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.

NO.21 Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

Slot is another name for executor.

There must be less executors than tasks.

An executor runs on a single core.

There must be more slots than tasks.

Tasks run in parallel via slots.

NO.22 Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has
10 partitions?

transactionsDf.repartition(transactionsDf.getNumPartitions()+2)

transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)

transactionsDf.coalesce(10)

transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)

transactionsDf.repartition(transactionsDf._partitions+2)

NO.23 Which of the following code blocks returns a one-column DataFrame for which every row contains an array of all integer numbers from 0 up to and including the number given in column predError of DataFrame transactionsDf, and null if predError is null?
Sample of DataFrame transactionsDf:
1.+————-+———+—–+——-+———+—-+
2.|transactionId|predError|value|storeId|productId| f|
3.+————-+———+—–+——-+———+—-+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+————-+———+—–+——-+———+—-+

1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = [range(target)]
6. return result
7.
8.count_to_target_udf = udf(count_to_target, ArrayType[IntegerType])
9.
10.transactionsDf.select(count_to_target_udf(col(‘predError’)))

1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.transactionsDf.select(count_to_target(col(‘predError’)))

1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
9.
10.transactionsDf.select(count_to_target_udf(‘predError’))
(Correct)

1.def count_to_target(target):
2. result = list(range(target))
3. return result
4.
5.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
6.
7.df = transactionsDf.select(count_to_target_udf(‘predError’))

1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.count_to_target_udf = udf(count_to_target)
9.
10.transactionsDf.select(count_to_target_udf(‘predError’))

NO.24 The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:

spark.conf.set(“spark.sql.autoBroadcastJoinThreshold”, 20)

Spark will only broadcast DataFrames that are much smaller than the default value.

The correct option to write configurations is through spark.config and not spark.conf.

Spark will only apply the limit to threshold joins and not to other joins.

The passed limit has the wrong variable type.

The command is evaluated lazily and needs to be followed by an action.

NO.25 The code block displayed below contains an error. The code block should write DataFrame transactionsDf as a parquet file to location filePath after partitioning it on column storeId. Find the error.
Code block:
transactionsDf.write.partitionOn(“storeId”).parquet(filePath)

The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block.

The partitionOn method should be called before the write method.

The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath.

Column storeId should be wrapped in a col() operator.

No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.

NO.26 Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?

itemsDf.persist(StorageLevel.MEMORY_ONLY)

itemsDf.cache(StorageLevel.MEMORY_AND_DISK)

itemsDf.store()

itemsDf.cache()

itemsDf.write.option(‘destination’, ‘memory’).save()

NO.27 Which of the following is the deepest level in Spark’s execution hierarchy?

Job

Task

Executor

Slot

Stage

NO.28 Which of the following are valid execution modes?

Kubernetes, Local, Client

Client, Cluster, Local

Server, Standalone, Client

Cluster, Server, Local

Standalone, Client, Cluster

NO.29 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.

NO.30 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__))

1. select
2. col(“storeId”)
3. cast
4. StringType

1. select
2. col(“storeId”)
3. as
4. StringType

1. cast
2. “storeId”
3. as
4. StringType()

1. select
2. col(“storeId”)
3. cast
4. StringType()

1. select
2. storeId
3. cast
4. StringType()

NO.31 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)

1. read()
2. format
3. “binaryFile”
4. “recursiveFileLookup”
5. load

1. read
2. format
3. “binaryFile”
4. “pathGlobFilter”
5. load

1. read
2. format
3. binaryFile
4. pathGlobFilter
5. load

1. open
2. format
3. “image”
4. “fileType”
5. open

1. open
2. as
3. “binaryFile”
4. “pathGlobFilter”
5. load

NO.32 Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?

itemsDf.cache().count()

itemsDf.cache(eager=True)

cache(itemsDf)

itemsDf.cache().filter()

itemsDf.rdd.storeCopy()

Associate-Developer-Apache-Spark Questions PDF [2023] Use Valid New dump to Clear Exam: https://www.premiumvcedump.com/Databricks/valid-Associate-Developer-Apache-Spark-premium-vce-exam-dumps.html

Premium VCE Dumps

Premium VCE Dumps

Jan-2023 Free Databricks Associate-Developer-Apache-Spark Exam Question Practice Exams [Q17-Q32]

What is the Databricks Associate Developer Apache Spark Exam?

How Databricks Associate Developer Apache Spark Exam can help you?

Premium VCE Dumps

Premium VCE Dumps

Category Archives: Associate-Developer-Apache-Spark

Jan-2023 Free Databricks Associate-Developer-Apache-Spark Exam Question Practice Exams [Q17-Q32]

What is the Databricks Associate Developer Apache Spark Exam?

How Databricks Associate Developer Apache Spark Exam can help you?