Pass Databricks Associate-Developer-Apache-Spark exam Dumps 100 Pass Guarantee With Latest Demo [Q15-Q38]

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Pass Databricks Associate-Developer-Apache-Spark exam Dumps 100 Pass Guarantee With Latest Demo

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Q15. The code block shown below should return a DataFrame with all columns of DataFrame transactionsDf, but only maximum 2 rows in which column productId has at least the value 2. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__).__3__

1. where
2. “productId” > 2
3. max(2)

1. where
2. transactionsDf[productId] >= 2
3. limit(2)

1. filter
2. productId > 2
3. max(2)

1. filter
2. col(“productId”) >= 2
3. limit(2)

1. where
2. productId >= 2
3. limit(2)

Q16. Which of the following is a characteristic of the cluster manager?

Each cluster manager works on a single partition of data.

The cluster manager receives input from the driver through the SparkContext.

The cluster manager does not exist in standalone mode.

The cluster manager transforms jobs into DAGs.

In client mode, the cluster manager runs on the edge node.

Q17. The code block displayed below contains an error. The code block should return a new DataFrame that only contains rows from DataFrame transactionsDf in which the value in column predError is at least 5. Find the error.
Code block:
transactionsDf.where(“col(predError) >= 5”)

The argument to the where method should be “predError >= 5”.

Instead of where(), filter() should be used.

The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where(“col(predError) >= 5”).

The argument to the where method cannot be a string.

Instead of >=, the SQL operator GEQ should be used.

Q18. Which of the following code blocks performs an inner join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively, excluding columns value and storeId from DataFrame transactionsDf and column attributes from DataFrame itemsDf?

transactionsDf.drop(‘value’, ‘storeId’).join(itemsDf.select(‘attributes’), transactionsDf.productId==itemsDf.itemId)

1.transactionsDf.createOrReplaceTempView(‘transactionsDf’)
2.itemsDf.createOrReplaceTempView(‘itemsDf’)
3.
4.spark.sql(“SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId”).drop(“attributes”)

transactionsDf.drop(“value”, “storeId”).join(itemsDf.drop(“attributes”),
“transactionsDf.productId==itemsDf.itemId”)

1.transactionsDf
2. .drop(col(‘value’), col(‘storeId’))
3. .join(itemsDf.drop(col(‘attributes’)), col(‘productId’)==col(‘itemId’))

1.transactionsDf.createOrReplaceTempView(‘transactionsDf’)
2.itemsDf.createOrReplaceTempView(‘itemsDf’)
3.
4.statement = “””
5.SELECT * FROM transactionsDf
6.INNER JOIN itemsDf
7.ON transactionsDf.productId==itemsDf.itemId
8.”””
9.spark.sql(statement).drop(“value”, “storeId”, “attributes”)

Explanation
This question offers you a wide variety of answers for a seemingly simple question. However, this variety reflects the variety of ways that one can express a join in PySpark. You need to understand some SQL syntax to get to the correct answer here.
transactionsDf.createOrReplaceTempView(‘transactionsDf’)
itemsDf.createOrReplaceTempView(‘itemsDf’)
statement = “””
SELECT * FROM transactionsDf
INNER JOIN itemsDf
ON transactionsDf.productId==itemsDf.itemId
“””
spark.sql(statement).drop(“value”, “storeId”, “attributes”)
Correct – this answer uses SQL correctly to perform the inner join and afterwards drops the unwanted columns. This is totally fine. If you are unfamiliar with the triple-quote “”” in Python: This allows you to express strings as multiple lines.
transactionsDf \
drop(col(‘value’), col(‘storeId’)) \
join(itemsDf.drop(col(‘attributes’)), col(‘productId’)==col(‘itemId’))
No, this answer option is a trap, since DataFrame.drop() does not accept a list of Column objects. You could use transactionsDf.drop(‘value’, ‘storeId’) instead.
transactionsDf.drop(“value”, “storeId”).join(itemsDf.drop(“attributes”),
“transactionsDf.productId==itemsDf.itemId”)
Incorrect – Spark does not evaluate “transactionsDf.productId==itemsDf.itemId” as a valid join expression.
This would work if it would not be a string.
transactionsDf.drop(‘value’, ‘storeId’).join(itemsDf.select(‘attributes’), transactionsDf.productId==itemsDf.itemId) Wrong, this statement incorrectly uses itemsDf.select instead of itemsDf.drop.
transactionsDf.createOrReplaceTempView(‘transactionsDf’)
itemsDf.createOrReplaceTempView(‘itemsDf’)
spark.sql(“SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId”).drop(“attributes”) No, here the SQL expression syntax is incorrect. Simply specifying -columnName does not drop a column.
More info: pyspark.sql.DataFrame.join – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

Q19. 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.

Q20. Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?

tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’),
‘outer’)

transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})

transactionsDf.select(‘value’, ‘productId’).distinct()

transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()

transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})

Q21. 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.

Q22. Which is the highest level in Spark’s execution hierarchy?

Task

Executor

Slot

Job

Stage

Q23. Which of the following is a viable way to improve Spark’s performance when dealing with large amounts of data, given that there is only a single application running on the cluster?

Increase values for the properties spark.default.parallelism and spark.sql.shuffle.partitions

Decrease values for the properties spark.default.parallelism and spark.sql.partitions

Increase values for the properties spark.sql.parallelism and spark.sql.partitions

Increase values for the properties spark.sql.parallelism and spark.sql.shuffle.partitions

Increase values for the properties spark.dynamicAllocation.maxExecutors, spark.default.parallelism, and spark.sql.shuffle.partitions

Q24. 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.

Q25. 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.

Q26. Which of the following statements about data skew is incorrect?

Spark will not automatically optimize skew joins by default.

Broadcast joins are a viable way to increase join performance for skewed data over sort-merge joins.

In skewed DataFrames, the largest and the smallest partition consume very different amounts of memory.

To mitigate skew, Spark automatically disregards null values in keys when joining.

Salting can resolve data skew.

Explanation
To mitigate skew, Spark automatically disregards null values in keys when joining.
This statement is incorrect, and thus the correct answer to the question. Joining keys that contain null values is of particular concern with regard to data skew.
In real-world applications, a table may contain a great number of records that do not have a value assigned to the column used as a join key. During the join, the data is at risk of being heavily skewed. This is because all records with a null-value join key are then evaluated as a single large partition, standing in stark contrast to the potentially diverse key values (and therefore small partitions) of the non-null-key records.
Spark specifically does not handle this automatically. However, there are several strategies to mitigate this problem like discarding null values temporarily, only to merge them back later (see last link below).
In skewed DataFrames, the largest and the smallest partition consume very different amounts of memory.
This statement is correct. In fact, having very different partition sizes is the very definition of skew. Skew can degrade Spark performance because the largest partition occupies a single executor for a long time. This blocks a Spark job and is an inefficient use of resources, since other executors that processed smaller partitions need to idle until the large partition is processed.
Salting can resolve data skew.
This statement is correct. The purpose of salting is to provide Spark with an opportunity to repartition data into partitions of similar size, based on a salted partitioning key.
A salted partitioning key typically is a column that consists of uniformly distributed random numbers. The number of unique entries in the partitioning key column should match the number of your desired number of partitions. After repartitioning by the salted key, all partitions should have roughly the same size.
Spark does not automatically optimize skew joins by default.
This statement is correct. Automatic skew join optimization is a feature of Adaptive Query Execution (AQE).
By default, AQE is disabled in Spark. To enable it, Spark’s spark.sql.adaptive.enabled configuration option needs to be set to true instead of leaving it at the default false.
To automatically optimize skew joins, Spark’s spark.sql.adaptive.skewJoin.enabled options also needs to be set to true, which it is by default.
When skew join optimization is enabled, Spark recognizes skew joins and optimizes them by splitting the bigger partitions into smaller partitions which leads to performance increases.
Broadcast joins are a viable way to increase join performance for skewed data over sort-merge joins.
This statement is correct. Broadcast joins can indeed help increase join performance for skewed data, under some conditions. One of the DataFrames to be joined needs to be small enough to fit into each executor’s memory, along a partition from the other DataFrame. If this is the case, a broadcast join increases join performance over a sort-merge join.
The reason is that a sort-merge join with skewed data involves excessive shuffling. During shuffling, data is sent around the cluster, ultimately slowing down the Spark application. For skewed data, the amount of data, and thus the slowdown, is particularly big.
Broadcast joins, however, help reduce shuffling data. The smaller table is directly stored on all executors, eliminating a great amount of network traffic, ultimately increasing join performance relative to the sort-merge join.
It is worth noting that for optimizing skew join behavior it may make sense to manually adjust Spark’s spark.sql.autoBroadcastJoinThreshold configuration property if the smaller DataFrame is bigger than the 10 MB set by default.
More info:
– Performance Tuning – Spark 3.0.0 Documentation
– Data Skew and Garbage Collection to Improve Spark Performance
– Section 1.2 – Joins on Skewed Data * GitBook

1. filter
2. col(“supplier”).isin(“Sports”)
3. “itemName”
4. explode(col(“attributes”))

1. where
2. col(“supplier”).contains(“Sports”)
3. “itemName”
4. “attributes”

1. where
2. col(supplier).contains(“Sports”)
3. explode(attributes)
4. itemName

1. where
2. “Sports”.isin(col(“Supplier”))
3. “itemName”
4. array_explode(“attributes”)

1. filter
2. col(“supplier”).contains(“Sports”)
3. “itemName”
4. explode(“attributes”)

Explanation
Output of correct code block:
+———————————-+——+
|itemName |col |
+———————————-+——+
|Thick Coat for Walking in the Snow|blue |
|Thick Coat for Walking in the Snow|winter|
|Thick Coat for Walking in the Snow|cozy |
|Outdoors Backpack |green |
|Outdoors Backpack |summer|
|Outdoors Backpack |travel|
+———————————-+——+
The key to solving this question is knowing about Spark’s explode operator. Using this operator, you can extract values from arrays into single rows. The following guidance steps through the answers systematically from the first to the last gap. Note that there are many ways to solving the gap questions and filtering out wrong answers, you do not always have to start filtering out from the first gap, but can also exclude some answers based on obvious problems you see with them.
The answers to the first gap present you with two options: filter and where. These two are actually synonyms in PySpark, so using either of those is fine. The answer options to this gap therefore do not help us in selecting the right answer.
The second gap is more interesting. One answer option includes “Sports”.isin(col(“Supplier”)). This construct does not work, since Python’s string does not have an isin method. Another option contains col(supplier). Here, Python will try to interpret supplier as a variable. We have not set this variable, so this is not a viable answer. Then, you are left with answers options that include col (“supplier”).contains(“Sports”) and col(“supplier”).isin(“Sports”). The question states that we are looking for suppliers whose name includes Sports, so we have to go for the contains operator here.
We would use the isin operator if we wanted to filter out for supplier names that match any entries in a list of supplier names.
Finally, we are left with two answers that fill the third gap both with “itemName” and the fourth gap either with explode(“attributes”) or “attributes”. While both are correct Spark syntax, only explode (“attributes”) will help us achieve our goal. Specifically, the question asks for one attribute from column attributes per row – this is what the explode() operator does.
One answer option also includes array_explode() which is not a valid operator in PySpark.
More info: pyspark.sql.functions.explode – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

Q28. Which of the following describes Spark’s standalone deployment mode?

Standalone mode uses a single JVM to run Spark driver and executor processes.

Standalone mode means that the cluster does not contain the driver.

Standalone mode is how Spark runs on YARN and Mesos clusters.

Standalone mode uses only a single executor per worker per application.

Standalone mode is a viable solution for clusters that run multiple frameworks, not only Spark.

Q29. The code block displayed below contains an error. The code block should arrange the rows of DataFrame transactionsDf using information from two columns in an ordered fashion, arranging first by column value, showing smaller numbers at the top and greater numbers at the bottom, and then by column predError, for which all values should be arranged in the inverse way of the order of items in column value. Find the error.
Code block:
transactionsDf.orderBy(‘value’, asc_nulls_first(col(‘predError’)))

Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement.

Column value should be wrapped by the col() operator.

Column predError should be sorted in a descending way, putting nulls last.

Column predError should be sorted by desc_nulls_first() instead.

Instead of orderBy, sort should be used.

itemsDf.select(explode(“attributes”).alias(“attributes_exploded”)).filter(attributes_exploded.contains(“i”))

itemsDf.explode(attributes).alias(“attributes_exploded”).filter(col(“attributes_exploded”).contains(“i”))

itemsDf.select(explode(“attributes”)).filter(“attributes_exploded”.contains(“i”))

itemsDf.select(explode(“attributes”).alias(“attributes_exploded”)).filter(col(“attributes_exploded”).contain

itemsDf.select(col(“attributes”).explode().alias(“attributes_exploded”)).filter(col(“attributes_exploded”).co

Q31. The code block shown below should return a DataFrame with only columns from DataFrame transactionsDf for which there is a corresponding transactionId in DataFrame itemsDf. DataFrame itemsDf is very small and much smaller than DataFrame transactionsDf. The query should be executed in an optimized way. Choose the answer that correctly fills the blanks in the code block to accomplish this.
__1__.__2__(__3__, __4__, __5__)

1. transactionsDf
2. join
3. broadcast(itemsDf)
4. transactionsDf.transactionId==itemsDf.transactionId
5. “outer”

1. transactionsDf
2. join
3. itemsDf
4. transactionsDf.transactionId==itemsDf.transactionId
5. “anti”

1. transactionsDf
2. join
3. broadcast(itemsDf)
4. “transactionId”
5. “left_semi”

1. itemsDf
2. broadcast
3. transactionsDf
4. “transactionId”
5. “left_semi”

1. itemsDf
2. join
3. broadcast(transactionsDf)
4. “transactionId”
5. “left_semi”

Q32. 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()

Q33. Which of the following describes the characteristics of accumulators?

Accumulators are used to pass around lookup tables across the cluster.

All accumulators used in a Spark application are listed in the Spark UI.

Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.

Accumulators are immutable.

If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.

Q34. 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)

1. spark
2. conf
3. set
4. “spark.sql.shuffle.partitions”

1. pyspark
2. config
3. set
4. spark.shuffle.partitions

1. spark
2. conf
3. get
4. “spark.sql.shuffle.partitions”

1. pyspark
2. config
3. set
4. “spark.sql.shuffle.partitions”

1. spark
2. conf
3. set
4. “spark.sql.aggregate.partitions”

Q35. Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?

spark.read.json(filePath)

spark.read.path(filePath, source=”json”)

spark.read().path(filePath)

spark.read().json(filePath)

spark.read.path(filePath)

Q36. Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:

transactionsDf.withColumn(“transactionDateFormatted”, from_unixtime(“transactionDate”, format=”dd/MM/yyyy”))

transactionsDf.withColumnRenamed(“transactionDate”, “transactionDateFormatted”, from_unixtime(“transactionDateFormatted”, format=”MM/dd/yyyy”))

transactionsDf.apply(from_unixtime(format=”MM/dd/yyyy”)).asColumn(“transactionDateFormatted”)

transactionsDf.withColumn(“transactionDateFormatted”, from_unixtime(“transactionDate”, format=”MM/dd/yyyy”))

transactionsDf.withColumn(“transactionDateFormatted”, from_unixtime(“transactionDate”))

Q37. Which of the following statements about Spark’s DataFrames is incorrect?

Spark’s DataFrames are immutable.

Spark’s DataFrames are equal to Python’s DataFrames.

Data in DataFrames is organized into named columns.

RDDs are at the core of DataFrames.

The data in DataFrames may be split into multiple chunks.

Q38. Which of the following code blocks generally causes a great amount of network traffic?

DataFrame.select()

DataFrame.coalesce()

DataFrame.collect()

DataFrame.rdd.map()

DataFrame.count()

Exam Format and Content

Language: This exam is only available in the Python or Scala language.
Exam Length: 60 questions
Exam Duration: 120 minutes
Exam Format: Multiple choice questions
Passing score: 70%

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