Associate-Developer-Apache-Spark test result

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Why you should take Databricks Associate Developer Apache Spark Exam?

If you are a developer who is interested in learning more about Spark and Big Data technologies, then you should definitely consider taking the Databricks Associate Developer Apache Spark Exam. This exam will help you learn how to use the technologies that are being used in the real world. Databricks Associate Developer Apache Spark exam dumps are the best way to prepare for this exam.

Today’s modern businesses need to be agile and nimble to adapt to the fast-paced business environment. With the advent of big data, cloud computing, and the Internet of Things, enterprises now face the challenge of managing, processing, analyzing, and integrating vast amounts of data. These challenges require new skills and a new approach to problem solving. The Apache Spark is a high-performance analytics engine that allows you to analyze and process large datasets in a fraction of the time. The Databricks Associate Developer Apache Spark Exam will help you master the skills required to build data-driven applications using Apache Spark.

QUESTION 87
Which of the following code blocks applies the Python function to_limit on column predError in table transactionsDf, returning a DataFrame with columns transactionId and result?

1.spark.udf.register(“LIMIT_FCN”, to_limit)
2.spark.sql(“SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf”) (Correct)

1.spark.udf.register(“LIMIT_FCN”, to_limit)
2.spark.sql(“SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result”)

1.spark.udf.register(“LIMIT_FCN”, to_limit)
2.spark.sql(“SELECT transactionId, to_limit(predError) AS result FROM transactionsDf”) spark.sql(“SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf”)

1.spark.udf.register(to_limit, “LIMIT_FCN”)
2.spark.sql(“SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf”)

QUESTION 88
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.

All column names need to be wrapped in the col() operator.

Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument
“,” needs to be passed to the split method.

Operator withColumnRenamed needs to be replaced with operator withColumn and the split method needs to be replaced by the splitString method.

Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument “
” needs to be passed to the split method.

The expressions “itemNameElements” and split(“itemName”) need to be swapped.

QUESTION 90
Which of the following code blocks shows the structure of a DataFrame in a tree-like way, containing both column names and types?

1.print(itemsDf.columns)
2.print(itemsDf.types)

itemsDf.printSchema()

spark.schema(itemsDf)

itemsDf.rdd.printSchema()

itemsDf.print.schema()

QUESTION 91
Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?

transactionsDf.summary()

transactionsDf.agg(“count”, “mean”, “stddev”, “25%”, “50%”, “75%”, “min”)

transactionsDf.summary(“count”, “mean”, “stddev”, “25%”, “50%”, “75%”, “max”).show()

transactionsDf.agg(“count”, “mean”, “stddev”, “25%”, “50%”, “75%”, “min”).show()

transactionsDf.summary().show()

QUESTION 92
The code block shown below should return only the average prediction error (column predError) of a random subset, without replacement, of approximately 15% of rows in DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(avg(‘predError’))

1. sample
2. True
3. 0.15
4. filter

1. sample
2. False
3. 0.15
4. select

1. sample
2. 0.85
3. False
4. select

1. fraction
2. 0.15
3. True
4. where

1. fraction
2. False
3. 0.85
4. select

QUESTION 93
Which of the following describes the difference between client and cluster execution modes?

In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.

In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.

In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.

In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.

In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.

QUESTION 94
Which of the following code blocks reads in the two-partition parquet file stored at filePath, making sure all columns are included exactly once even though each partition has a different schema?
Schema of first partition:
1.root
2. |– transactionId: integer (nullable = true)
3. |– predError: integer (nullable = true)
4. |– value: integer (nullable = true)
5. |– storeId: integer (nullable = true)
6. |– productId: integer (nullable = true)
7. |– f: integer (nullable = true)
Schema of second partition:
1.root
2. |– transactionId: integer (nullable = true)
3. |– predError: integer (nullable = true)
4. |– value: integer (nullable = true)
5. |– storeId: integer (nullable = true)
6. |– rollId: integer (nullable = true)
7. |– f: integer (nullable = true)
8. |– tax_id: integer (nullable = false)

spark.read.parquet(filePath, mergeSchema=’y’)

spark.read.option(“mergeSchema”, “true”).parquet(filePath)

spark.read.parquet(filePath)

1.nx = 0
2.for file in dbutils.fs.ls(filePath):
3. if not file.name.endswith(“.parquet”):
4. continue
5. df_temp = spark.read.parquet(file.path)
6. if nx == 0:
7. df = df_temp
8. else:
9. df = df.union(df_temp)
10. nx = nx+1
11.df

QUESTION 95
The code block shown below should store DataFrame transactionsDf on two different executors, utilizing the executors’ memory as much as possible, but not writing anything to disk. Choose the answer that correctly fills the blanks in the code block to accomplish this.
1.from pyspark import StorageLevel
2.transactionsDf.__1__(StorageLevel.__2__).__3__

1. cache
2. MEMORY_ONLY_2
3. count()

1. persist
2. DISK_ONLY_2
3. count()

1. persist
2. MEMORY_ONLY_2
3. select()

1. cache
2. DISK_ONLY_2
3. count()

1. persist
2. MEMORY_ONLY_2
3. count()

QUESTION 96
The code block shown below should return a two-column DataFrame with columns transactionId and supplier, with combined information from DataFrames itemsDf and transactionsDf. The code block should merge rows in which column productId of DataFrame transactionsDf matches the value of column itemId in DataFrame itemsDf, but only where column storeId of DataFrame transactionsDf does not match column itemId of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(itemsDf, __2__).__3__(__4__)

1. join
2. transactionsDf.productId==itemsDf.itemId, how=”inner”
3. select
4. “transactionId”, “supplier”

1. select
2. “transactionId”, “supplier”
3. join
4. [transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId]

1. join
2. [transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId]
3. select
4. “transactionId”, “supplier”

1. filter
2. “transactionId”, “supplier”
3. join
4. “transactionsDf.storeId!=itemsDf.itemId, transactionsDf.productId==itemsDf.itemId”

1. join
2. transactionsDf.productId==itemsDf.itemId, transactionsDf.storeId!=itemsDf.itemId
3. filter
4. “transactionId”, “supplier”

QUESTION 97
Which of the following statements about Spark’s execution hierarchy is correct?

In Spark’s execution hierarchy, a job may reach over multiple stage boundaries.

In Spark’s execution hierarchy, manifests are one layer above jobs.

In Spark’s execution hierarchy, a stage comprises multiple jobs.

In Spark’s execution hierarchy, executors are the smallest unit.

In Spark’s execution hierarchy, tasks are one layer above slots.

QUESTION 98
Which of the following statements about RDDs is incorrect?

An RDD consists of a single partition.

The high-level DataFrame API is built on top of the low-level RDD API.

RDDs are immutable.

RDD stands for Resilient Distributed Dataset.

RDDs are great for precisely instructing Spark on how to do a query.

QUESTION 99
Which of the following statements about garbage collection in Spark is incorrect?

Garbage collection information can be accessed in the Spark UI’s stage detail view.

Optimizing garbage collection performance in Spark may limit caching ability.

Manually persisting RDDs in Spark prevents them from being garbage collected.

In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.

Serialized caching is a strategy to increase the performance of garbage collection.

Explanation
Manually persisting RDDs in Spark prevents them from being garbage collected.
This statement is incorrect, and thus the correct answer to the question. Spark’s garbage collector will remove even persisted objects, albeit in an “LRU” fashion. LRU stands for least recently used.
So, during a garbage collection run, the objects that were used the longest time ago will be garbage collected first.
See the linked StackOverflow post below for more information.
Serialized caching is a strategy to increase the performance of garbage collection.
This statement is correct. The more Java objects Spark needs to collect during garbage collection, the longer it takes. Storing a collection of many Java objects, such as a DataFrame with a complex schema, through serialization as a single byte array thus increases performance. This means that garbage collection takes less time on a serialized DataFrame than an unserialized DataFrame.
Optimizing garbage collection performance in Spark may limit caching ability.
This statement is correct. A full garbage collection run slows down a Spark application. When taking about
“tuning” garbage collection, we mean reducing the amount or duration of these slowdowns.
A full garbage collection run is triggered when the Old generation of the Java heap space is almost full. (If you are unfamiliar with this concept, check out the link to the Garbage Collection Tuning docs below.) Thus, one measure to avoid triggering a garbage collection run is to prevent the Old generation share of the heap space to be almost full.
To achieve this, one may decrease its size. Objects with sizes greater than the Old generation space will then be discarded instead of cached (stored) in the space and helping it to be “almost full”.
This will decrease the number of full garbage collection runs, increasing overall performance.
Inevitably, however, objects will need to be recomputed when they are needed. So, this mechanism only works when a Spark application needs to reuse cached data as little as possible.
Garbage collection information can be accessed in the Spark UI’s stage detail view.
This statement is correct. The task table in the Spark UI’s stage detail view has a “GC Time” column, indicating the garbage collection time needed per task.
In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
This statement is correct. The G1 garbage collector, also known as garbage first garbage collector, is an alternative to the default Parallel garbage collector.
While the default Parallel garbage collector divides the heap into a few static regions, the G1 garbage collector divides the heap into many small regions that are created dynamically. The G1 garbage collector has certain advantages over the Parallel garbage collector which improve performance particularly for Spark workloads that require high throughput and low latency.
The G1 garbage collector is not enabled by default, and you need to explicitly pass an argument to Spark to enable it. For more information about the two garbage collectors, check out the Databricks article linked below.

QUESTION 100
The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column storeId as key for partitioning. Find the error.
Code block:
transactionsDf.write.format(“parquet”).partitionOn(“storeId”).save(“/FileStore/transactions_split”)A.

The format(“parquet”) expression is inappropriate to use here, “parquet” should be passed as first argument to the save() operator and “/FileStore/transactions_split” as the second argument.

Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.

Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.

partitionOn(“storeId”) should be called before the write operation.

The format(“parquet”) expression should be removed and instead, the information should be added to the write expression like so: write(“parquet”).

QUESTION 101
Which of the following code blocks creates a new DataFrame with 3 columns, productId, highest, and lowest, that shows the biggest and smallest values of column value per value in column productId from DataFrame transactionsDf?
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.+————-+———+—–+——-+———+—-+

transactionsDf.max(‘value’).min(‘value’)

transactionsDf.agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))

transactionsDf.groupby(col(productId)).agg(max(col(value)).alias(“highest”), min(col(value)).alias(“lowest”))

transactionsDf.groupby(‘productId’).agg(max(‘value’).alias(‘highest’), min(‘value’).alias(‘lowest’))

transactionsDf.groupby(“productId”).agg({“highest”: max(“value”), “lowest”: min(“value”)})

QUESTION 102
Which of the following is the deepest level in Spark’s execution hierarchy?

Job

Task

Executor

Slot

Stage

QUESTION 103
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()

QUESTION 104
Which of the following statements about DAGs is correct?

DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.

DAG stands for “Directing Acyclic Graph”.

Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.

In contrast to transformations, DAGs are never lazily executed.

DAGs can be decomposed into tasks that are executed in parallel.

QUESTION 105
Which of the following code blocks returns DataFrame transactionsDf sorted in descending order by column predError, showing missing values last?

transactionsDf.sort(asc_nulls_last(“predError”))

transactionsDf.orderBy(“predError”).desc_nulls_last()

transactionsDf.sort(“predError”, ascending=False)

transactionsDf.desc_nulls_last(“predError”)

transactionsDf.orderBy(“predError”).asc_nulls_last()

QUESTION 106
Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?

transactionsDf.select(“storeId”).dropDuplicates().count()

transactionsDf.select(count(“storeId”)).dropDuplicates()

transactionsDf.select(distinct(“storeId”)).count()

transactionsDf.dropDuplicates().agg(count(“storeId”))

transactionsDf.distinct().select(“storeId”).count()

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