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NEW QUESTION 46
Which of the following code blocks returns a single-row DataFrame that only has a column corr which shows the Pearson correlation coefficient between columns predError and value in DataFrame transactionsDf?

• A. transactionsDf.select(corr("predError", "value"))
• B. transactionsDf.select(corr(predError, value).alias("corr"))
• C. transactionsDf.select(corr(col("predError"), col("value")).alias("corr")) (Correct)
• D. transactionsDf.select(corr(["predError", "value"]).alias("corr")).first()
• E. transactionsDf.select(corr(col("predError"), col("value")).alias("corr")).first()

Answer: C

Explanation:
Explanation
In difficulty, this question is above what you can expect from the exam. What this question NO:
wants to teach you, however, is to pay attention to the useful details included in the documentation.
pyspark.sql.corr is not a very common method, but it deals with Spark's data structure in an interesting way.
The command takes two columns over multiple rows and returns a single row - similar to an aggregation function. When examining the documentation (linked below), you will find this code example:
a = range(20)
b = [2 * x for x in range(20)]
df = spark.createDataFrame(zip(a, b), ["a", "b"])
df.agg(corr("a", "b").alias('c')).collect()
[Row(c=1.0)]
See how corr just returns a single row? Once you understand this, you should be suspicious about answers that include first(), since there is no need to just select a single row. A reason to eliminate those answers is that DataFrame.first() returns an object of type Row, but not DataFrame, as requested in the question.
transactionsDf.select(corr(col("predError"), col("value")).alias("corr")) Correct! After calculating the Pearson correlation coefficient, the resulting column is correctly renamed to corr.
transactionsDf.select(corr(predError, value).alias("corr"))
No. In this answer, Python will interpret column names predError and value as variable names.
transactionsDf.select(corr(col("predError"), col("value")).alias("corr")).first() Incorrect. first() returns a row, not a DataFrame (see above and linked documentation below).
transactionsDf.select(corr("predError", "value"))
Wrong. Whie this statement returns a DataFrame in the desired shape, the column will have the name corr(predError, value) and not corr.
transactionsDf.select(corr(["predError", "value"]).alias("corr")).first() False. In addition to first() returning a row, this code block also uses the wrong call structure for command corr which takes two arguments (the two columns to correlate).
More info:
- pyspark.sql.functions.corr - PySpark 3.1.2 documentation
- pyspark.sql.DataFrame.first - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

NEW QUESTION 47
Which of the following describes the conversion of a computational query into an execution plan in Spark?

• A. Spark uses the catalog to resolve the optimized logical plan.
• B. The executed physical plan depends on a cost optimization from a previous stage.
• C. Depending on whether DataFrame API or SQL API are used, the physical plan may differ.
• D. The catalog assigns specific resources to the optimized memory plan.
• E. The catalog assigns specific resources to the physical plan.

Answer: B

Explanation:
Explanation
The executed physical plan depends on a cost optimization from a previous stage.
Correct! Spark considers multiple physical plans on which it performs a cost analysis and selects the final physical plan in accordance with the lowest-cost outcome of that analysis. That final physical plan is then executed by Spark.
Spark uses the catalog to resolve the optimized logical plan.
No. Spark uses the catalog to resolve the unresolved logical plan, but not the optimized logical plan. Once the unresolved logical plan is resolved, it is then optimized using the Catalyst Optimizer.
The optimized logical plan is the input for physical planning.
The catalog assigns specific resources to the physical plan.
No. The catalog stores metadata, such as a list of names of columns, data types, functions, and databases.
Spark consults the catalog for resolving the references in a logical plan at the beginning of the conversion of the query into an execution plan. The result is then an optimized logical plan.
Depending on whether DataFrame API or SQL API are used, the physical plan may differ.
Wrong - the physical plan is independent of which API was used. And this is one of the great strengths of Spark!
The catalog assigns specific resources to the optimized memory plan.
There is no specific "memory plan" on the journey of a Spark computation.
More info: Spark's Logical and Physical plans ... When, Why, How and Beyond. | by Laurent Leturgez | datalex | Medium

NEW QUESTION 48
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.split("supplier", " ").size()
• D. itemsDf.split("supplier", " ").count()
• E. itemsDf.select(word_count("supplier"))

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 49
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. filter
  2. col("supplier").isin("Sports")
  3. "itemName"
  4. explode(col("attributes"))
• B. 1. where
  2. col(supplier).contains("Sports")
  3. explode(attributes)
  4. itemName
• C. 1. where
  2. col("supplier").contains("Sports")
  3. "itemName"
  4. "attributes"
• D. 1. filter
  2. col("supplier").contains("Sports")
  3. "itemName"
  4. explode("attributes")
• E. 1. where
  2. "Sports".isin(col("Supplier"))
  3. "itemName"
  4. array_explode("attributes")

Answer: D

Explanation:
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

NEW QUESTION 50
Which of the following code blocks performs a join in which the small DataFrame transactionsDf is sent to all executors where it is joined with DataFrame itemsDf on columns storeId and itemId, respectively?

• A. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "right_outer")
• B. itemsDf.join(transactionsDf, broadcast(itemsDf.itemId == transactionsDf.storeId))
• C. itemsDf.join(broadcast(transactionsDf), itemsDf.itemId == transactionsDf.storeId)
• D. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.storeId, "broadcast")
• E. itemsDf.merge(transactionsDf, "itemsDf.itemId == transactionsDf.storeId", "broadcast")

Answer: C

Explanation:
Explanation
The issue with all answers that have "broadcast" as very last argument is that "broadcast" is not a valid join type. While the entry with "right_outer" is a valid statement, it is not a broadcast join. The item where broadcast() is wrapped around the equality condition is not valid code in Spark. broadcast() needs to be wrapped around the name of the small DataFrame that should be broadcast.
More info: Learning Spark, 2nd Edition, Chapter 7
Static notebook | Dynamic notebook: See test 1
tion and explanation?

NEW QUESTION 51
......