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The Exam cost of Databricks Associate Developer Apache Spark Exam?

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

Certification Topics of Databricks Associate Developer Apache Spark Exam?

• Lastly, Spark DataFrame API Applications (72%)
• Then, Spark Architecture: Applied understanding (11%)
• To begin with, Spark Architecture: Conceptual understanding (17%)

Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which of the following code blocks returns a single-column DataFrame showing the number of words in column supplier of DataFrame itemsDf?
Sample of DataFrame itemsDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+

• A. itemsDf.select(size(split("supplier", " ")))
• B. spark.select(size(split(col(supplier), " ")))
• C. itemsDf.select(word_count("supplier"))
• D. itemsDf.split("supplier", " ").size()
• E. itemsDf.split("supplier", " ").count()

Answer: A Explanation:
Explanation
Output of correct code block:
+----------------------------+
|size(split(supplier, , -1))|
+----------------------------+
| 3|
| 1|
| 3|
+----------------------------+
This question shows a typical use case for the split command: Splitting a string into words. An additional difficulty is that you are asked to count the words. Although it is tempting to use the count method here, the size method (as in: size of an array) is actually the correct one to use. Familiarize yourself with the split and the size methods using the linked documentation below.
More info:
Split method: pyspark.sql.functions.split - PySpark 3.1.2 documentation Size method: pyspark.sql.functions.size - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2
NEW QUESTION # 31
The code block shown below should return a copy of DataFrame transactionsDf with an added column cos.
This column should have the values in column value converted to degrees and having the cosine of those converted values taken, rounded to two decimals. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.1(2, round(3(4(5)),2))

• A. 1. withColumn
  2. col("cos")
  3. cos
  4. degrees
  5. transactionsDf.value
• B. 1. withColumn
  2. "cos"
  3. cos
  4. degrees
  5. transactionsDf.value
• C. 1. withColumn
  2. col("cos")
  3. cos
  4. degrees
  5. col("value")
  E
  . 1. withColumn
  2. "cos"
  3. degrees
  4. cos
  5. col("value")
• D. 1. withColumnRenamed
  2. "cos"
  3. cos
  4. degrees
  5. "transactionsDf.value"

Answer: B Explanation:
Explanation
Correct code block:
transactionsDf.withColumn("cos", round(cos(degrees(transactionsDf.value)),2)) This question is especially confusing because col, "cos" are so similar. Similar-looking answer options can also appear in the exam and, just like in this question, you need to pay attention to the details to identify what the correct answer option is.
The first answer option to throw out is the one that starts with withColumnRenamed: The question NO:
speaks specifically of adding a column. The withColumnRenamed operator only renames an existing column, however, so you cannot use it here.
Next, you will have to decide what should be in gap 2, the first argument of transactionsDf.withColumn().
Looking at the documentation (linked below), you can find out that the first argument of withColumn actually needs to be a string with the name of the column to be added. So, any answer that includes col("cos") as the option for gap 2 can be disregarded.
This leaves you with two possible answers. The real difference between these two answers is where the cos and degree methods are, either in gaps 3 and 4, or vice-versa. From the question you can find out that the new column should have "the values in column value converted to degrees and having the cosine of those converted values taken". This prescribes you a clear order of operations: First, you convert values from column value to degrees and then you take the cosine of those values. So, the inner parenthesis (gap 4) should contain the degree method and then, logically, gap 3 holds the cos method. This leaves you with just one possible correct answer.
More info: pyspark.sql.DataFrame.withColumn - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
NEW QUESTION # 32
Which of the following code blocks returns a single-column DataFrame of all entries in Python list throughputRates which contains only float-type values ?

• A. spark.createDataFrame((throughputRates), FloatType)
• B. spark.createDataFrame(throughputRates)
• C. spark.DataFrame(throughputRates, FloatType)
• D. spark.createDataFrame(throughputRates, FloatType())
• E. spark.createDataFrame(throughputRates, FloatType)

Answer: D Explanation:
Explanation
spark.createDataFrame(throughputRates, FloatType())
Correct! spark.createDataFrame is the correct operator to use here and the type FloatType() which is passed in for the command's schema argument is correctly instantiated using the parentheses.
Remember that it is essential in PySpark to instantiate types when passing them to SparkSession.createDataFrame. And, in Databricks, spark returns a SparkSession object.
spark.createDataFrame((throughputRates), FloatType)
No. While packing throughputRates in parentheses does not do anything to the execution of this command, not instantiating the FloatType with parentheses as in the previous answer will make this command fail.
spark.createDataFrame(throughputRates, FloatType)
Incorrect. Given that it does not matter whether you pass throughputRates in parentheses or not, see the explanation of the previous answer for further insights.
spark.DataFrame(throughputRates, FloatType)
Wrong. There is no SparkSession.DataFrame() method in Spark.
spark.createDataFrame(throughputRates)
False. Avoiding the schema argument will have PySpark try to infer the schema. However, as you can see in the documentation (linked below), the inference will only work if you pass in an "RDD of either Row, namedtuple, or dict" for data (the first argument to createDataFrame). But since you are passing a Python list, Spark's schema inference will fail.
More info: pyspark.sql.SparkSession.createDataFrame - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
NEW QUESTION # 33
Which of the following code blocks reduces a DataFrame from 12 to 6 partitions and performs a full shuffle?

• A. DataFrame.repartition(6)
• B. DataFrame.coalesce(6, shuffle=True)
• C. DataFrame.coalesce(6)
• D. DataFrame.coalesce(6).shuffle()
• E. DataFrame.repartition(12)

Answer: A Explanation:
Explanation
DataFrame.repartition(6)
Correct. repartition() always triggers a full shuffle (different from coalesce()).
DataFrame.repartition(12)
No, this would just leave the DataFrame with 12 partitions and not 6.
DataFrame.coalesce(6)
coalesce does not perform a full shuffle of the data. Whenever you see "full shuffle", you know that you are not dealing with coalesce(). While coalesce() can perform a partial shuffle when required, it will try to minimize shuffle operations, so the amount of data that is sent between executors.
Here, 12 partitions can easily be repartitioned to be 6 partitions simply by stitching every two partitions into one.
DataFrame.coalesce(6, shuffle=True) and DataFrame.coalesce(6).shuffle() These statements are not valid Spark API syntax.
More info: Spark Repartition & Coalesce - Explained and Repartition vs Coalesce in Apache Spark - Rock the JVM Blog
NEW QUESTION # 34
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")

• A. The argument to the where method should be "predError >= 5".
• B. Instead of where(), filter() should be used.
• C. Instead of >=, the SQL operator GEQ should be used.
• D. The argument to the where method cannot be a string.
• E. 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").

Answer: A Explanation:
Explanation
The argument to the where method cannot be a string.
It can be a string, no problem here.
Instead of where(), filter() should be used.
No, that does not matter. In PySpark, where() and filter() are equivalent.
Instead of >=, the SQL operator GEQ should be used.
Incorrect.
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").
No, Spark returns a new DataFrame.
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/sparkpracticetestscode/#1/27.html ,
https://bit.ly/sparkpracticeexamsimportinstructions)
NEW QUESTION # 35
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