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Databricks Associate-Developer-Apache-Spark Exam Overview:

Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:

FAQs for Databricks Certified Associate Developer for Apache Spark 3.0 Candidates

Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:

Question 1

Which of the following code blocks returns a DataFrame showing the mean value of column "value" of DataFrame transactionsDf, grouped by its column storeId?

A. transactionsDf.groupBy("storeId").agg(avg("value"))
B. transactionsDf.groupBy("storeId").avg(col("value"))
C. transactionsDf.groupBy("storeId").agg(average("value"))
D. transactionsDf.groupBy("value").average()
E. transactionsDf.groupBy(col(storeId).avg())

Question 2

Which of the following code blocks returns a DataFrame that has all columns of DataFrame transactionsDf and an additional column predErrorSquared which is the squared value of column predError in DataFrame transactionsDf?

A. transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))
B. transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))
C. transactionsDf.withColumn("predErrorSquared", "predError"**2)
D. transactionsDf.withColumnRenamed("predErrorSquared", pow(predError, 2))
E. transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))

Question 3

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

Question 4

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")

A. explode() is not a method of DataFrame. explode() should be used inside the select() method instead.
B. Since itemId is the index, it does not need to be an argument to the select() method.
C. The alias() method needs to be called after the select() method.
D. The split() method should be used inside the select() method instead of the explode() method.
E. The explode() method expects a Column object rather than a string.

Question 5

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?

A. itemsDf.save.format("avro").mode("ignore").write(fileLocation)
B. itemsDf.write.format("avro").mode("errorifexists").save(fileLocation)
C. itemsDf.write.avro(fileLocation)
D. itemsDf.write.format("avro").mode("ignore").save(fileLocation)
E. spark.DataFrameWriter(itemsDf).format("avro").write(fileLocation)

Solutions: