ASSOCIATE-DEVELOPER-APACHE-SPARK LATEST TEST PREP & RELIABLE ASSOCIATE-DEVELOPER-APACHE-SPARK DUMPS QUESTIONS

Associate-Developer-Apache-Spark Latest Test Prep & Reliable Associate-Developer-Apache-Spark Dumps Questions

Associate-Developer-Apache-Spark Latest Test Prep & Reliable Associate-Developer-Apache-Spark Dumps Questions

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The cost of the Databricks Associate Developer Apache Spark Exam is 200 USD per attempt.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q80-Q85):

NEW QUESTION # 80
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__))

  • A. 1. select
    2. col("storeId")
    3. cast
    4. StringType
  • B. 1. cast
    2. "storeId"
    3. as
    4. StringType()
  • C. 1. select
    2. storeId
    3. cast
    4. StringType()
  • D. 1. select
    2. col("storeId")
    3. cast
    4. StringType()
  • E. 1. select
    2. col("storeId")
    3. as
    4. StringType

Answer: D

Explanation:
Explanation
Correct code block:
transactionsDf.select(col("storeId").cast(StringType()))
Solving this question involves understanding that, when using types from the pyspark.sql.types such as StringType, these types need to be instantiated when using them in Spark, or, in simple words, they need to be followed by parentheses like so: StringType(). You could also use .cast("string") instead, but that option is not given here.
More info: pyspark.sql.Column.cast - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2


NEW QUESTION # 81
The code block displayed below contains an error. The code block should return DataFrame transactionsDf, but with the column storeId renamed to storeNumber. Find the error.
Code block:
transactionsDf.withColumn("storeNumber", "storeId")

  • A. Arguments "storeNumber" and "storeId" each need to be wrapped in a col() operator.
  • B. The withColumn operator should be replaced with the copyDataFrame operator.
  • C. Argument "storeId" should be the first and argument "storeNumber" should be the second argument to the withColumn method.
  • D. Instead of withColumn, the withColumnRenamed method should be used.
  • E. Instead of withColumn, the withColumnRenamed method should be used and argument "storeId" should be the first and argument "storeNumber" should be the second argument to that method.

Answer: E

Explanation:
Explanation
Correct code block:
transactionsDf.withColumnRenamed("storeId", "storeNumber")
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1


NEW QUESTION # 82
Which of the following code blocks returns a single row from DataFrame transactionsDf?
Full 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.+-------------+---------+-----+-------+---------+----+

  • A. transactionsDf.where(col("value").isNull()).select("productId", "storeId").distinct()
  • B. transactionsDf.filter((col("storeId")!=25) | (col("productId")==2))
  • C. transactionsDf.select("productId", "storeId").where("storeId == 2 OR storeId != 25")
  • D. transactionsDf.filter(col("storeId")==25).select("predError","storeId").distinct()
  • E. transactionsDf.where(col("storeId").between(3,25))

Answer: D

Explanation:
Explanation
Output of correct code block:
+---------+-------+
|predError|storeId|
+---------+-------+
| 3| 25|
+---------+-------+
This question is difficult because it requires you to understand different kinds of commands and operators. All answers are valid Spark syntax, but just one expression returns a single-row DataFrame.
For reference, here is what the incorrect answers return:
transactionsDf.filter((col("storeId")!=25) | (col("productId")==2)) returns
+-------------+---------+-----+-------+---------+----+
|transactionId|predError|value|storeId|productId| f|
+-------------+---------+-----+-------+---------+----+
| 2| 6| 7| 2| 2|null|
| 4| null| null| 3| 2|null|
| 5| null| null| null| 2|null|
| 6| 3| 2| 25| 2|null|
+-------------+---------+-----+-------+---------+----+
transactionsDf.where(col("storeId").between(3,25)) returns
+-------------+---------+-----+-------+---------+----+
|transactionId|predError|value|storeId|productId| f|
+-------------+---------+-----+-------+---------+----+
| 1| 3| 4| 25| 1|null|
| 3| 3| null| 25| 3|null|
| 4| null| null| 3| 2|null|
| 6| 3| 2| 25| 2|null|
+-------------+---------+-----+-------+---------+----+
transactionsDf.where(col("value").isNull()).select("productId", "storeId").distinct() returns
+---------+-------+
|productId|storeId|
+---------+-------+
| 3| 25|
| 2| 3|
| 2| null|
+---------+-------+
transactionsDf.select("productId", "storeId").where("storeId == 2 OR storeId != 25") returns
+---------+-------+
|productId|storeId|
+---------+-------+
| 2| 2|
| 2| 3|
+---------+-------+
Static notebook | Dynamic notebook: See test 2


NEW QUESTION # 83
Which of the following code blocks sorts DataFrame transactionsDf both by column storeId in ascending and by column productId in descending order, in this priority?

  • A. transactionsDf.sort(col(storeId)).desc(col(productId))
  • B. transactionsDf.sort("storeId").sort(desc("productId"))
  • C. transactionsDf.sort("storeId", asc("productId"))
  • D. transactionsDf.order_by(col(storeId), desc(col(productId)))
  • E. transactionsDf.sort("storeId", desc("productId"))

Answer: E

Explanation:
Explanation
In this question it is important to realize that you are asked to sort transactionDf by two columns. This means that the sorting of the second column depends on the sorting of the first column.
So, any option that sorts the entire DataFrame (through chaining sort statements) will not work. The two columns need to be channeled through the same call to sort().
Also, order_by is not a valid DataFrame API method.
More info: pyspark.sql.DataFrame.sort - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2


NEW QUESTION # 84
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__)

  • A. 1. itemsDf
    2. join
    3. broadcast(transactionsDf)
    4. "transactionId"
    5. "left_semi"
  • B. 1. itemsDf
    2. broadcast
    3. transactionsDf
    4. "transactionId"
    5. "left_semi"
  • C. 1. transactionsDf
    2. join
    3. itemsDf
    4. transactionsDf.transactionId==itemsDf.transactionId
    5. "anti"
  • D. 1. transactionsDf
    2. join
    3. broadcast(itemsDf)
    4. "transactionId"
    5. "left_semi"
  • E. 1. transactionsDf
    2. join
    3. broadcast(itemsDf)
    4. transactionsDf.transactionId==itemsDf.transactionId
    5. "outer"

Answer: D

Explanation:
Explanation
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This question is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join, broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard it. Another answer option wraps the broadcast() operator around transactionsDf - the bigger of the two DataFrames. This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One answer option, however, resolves to itemsDf.broadcast([...]). The DataFrame class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([...]) in the first 2 gaps, so you will have to figure out the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as asked for by the question. So, the correct answer is the one that uses the left_semi join.


NEW QUESTION # 85
......

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