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Databricks Certified Associate Developer for Apache Spark 3.0 - Associate-Developer-Apache-Spark Exam Questions

QUESTION NO: 1
Which of the following code blocks returns a DataFrame with a single column in which all items in column attributes of DataFrame itemsDf are listed that contain the letter i?
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+

A. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(attributes_exploded.contains("i")) B. itemsDf.select(explode("attributes")).filter("attributes_exploded".contains("i")) C. itemsDf.explode(attributes).alias("attributes_exploded").filter(col("attributes_exploded").contains("i")) D. itemsDf.select(col("attributes").explode().alias("attributes_exploded")).filter(col("attributes_exploded").co E. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(col("attributes_exploded").contain

Correct Answer: E

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QUESTION NO: 2
The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.__1__(__2__).select(__3__, __4__)

A. 1. where
2. col("supplier").contains("Sports")
3. "itemName"
4. "attributes" B. 1. where
2. "Sports".isin(col("Supplier"))
3. "itemName"
4. array_explode("attributes") C. 1. filter
2. col("supplier").contains("Sports")
3. "itemName"
4. explode("attributes") D. 1. filter
2. col("supplier").isin("Sports")
3. "itemName"
4. explode(col("attributes")) E. 1. where
2. col(supplier).contains("Sports")
3. explode(attributes)
4. itemName

Correct Answer: C

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QUESTION NO: 3
Which of the following code blocks creates a new one-column, two-row DataFrame dfDates with column date of type timestamp?

A. 1.dfDates = spark.createDataFrame(["23/01/2022 11:28:12","24/01/2022 10:58:34"], ["date"])
2.dfDates = dfDates.withColumnRenamed("date", to_datetime("date", "yyyy-MM-dd HH:mm:ss")) B. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"]) C. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
2.dfDates = dfDates.withColumnRenamed("date", to_timestamp("date", "yyyy-MM-dd HH:mm:ss")) D. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
2.dfDates = dfDates.withColumn("date", to_timestamp("date", "dd/MM/yyyy HH:mm:ss")) E. 1.dfDates = spark.createDataFrame(["23/01/2022 11:28:12","24/01/2022 10:58:34"], ["date"])
2.dfDates = dfDates.withColumn("date", to_timestamp("dd/MM/yyyy HH:mm:ss", "date"))

Correct Answer: D

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QUESTION NO: 4
Which of the following statements about Spark's configuration properties is incorrect?

A. The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property. B. The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property. C. The default value for spark.sql.autoBroadcastJoinThreshold is 10MB. D. The default number of partitions to use when shuffling data for joins or aggregations is 300. E. The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.

Correct Answer: D

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QUESTION NO: 5
Which of the following statements about broadcast variables is correct?

A. Broadcast variables are serialized with every single task. B. Broadcast variables are occasionally dynamically updated on a per-task basis. C. Broadcast variables are commonly used for tables that do not fit into memory. D. Broadcast variables are immutable. E. Broadcast variables are local to the worker node and not shared across the cluster.

Correct Answer: D

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QUESTION NO: 6
Which of the following statements about garbage collection in Spark is incorrect?

A. Manually persisting RDDs in Spark prevents them from being garbage collected. B. Garbage collection information can be accessed in the Spark UI's stage detail view. C. Serialized caching is a strategy to increase the performance of garbage collection. D. Optimizing garbage collection performance in Spark may limit caching ability. E. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.

Correct Answer: A

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QUESTION NO: 7
Which of the following code blocks creates a new 6-column DataFrame by appending the rows of the
6-column DataFrame yesterdayTransactionsDf to the rows of the 6-column DataFrame todayTransactionsDf, ignoring that both DataFrames have different column names?

A. union(todayTransactionsDf, yesterdayTransactionsDf) B. todayTransactionsDf.unionByName(yesterdayTransactionsDf) C. todayTransactionsDf.concat(yesterdayTransactionsDf) D. todayTransactionsDf.union(yesterdayTransactionsDf) E. todayTransactionsDf.unionByName(yesterdayTransactionsDf, allowMissingColumns=True)

Correct Answer: D

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QUESTION NO: 8
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?

A. spark.read.schema(fileSchema).format("parquet").load(filePath) B. spark.read.schema("fileSchema").format("parquet").load(filePath) C. spark.read().schema(fileSchema).format(parquet).load(filePath) D. spark.read.schema(fileSchema).open(filePath) E. spark.read().schema(fileSchema).parquet(filePath)

Correct Answer: A

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QUESTION NO: 9
Which is the highest level in Spark's execution hierarchy?

A. Task B. Executor C. Job D. Slot E. Stage

Correct Answer: C