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NEW QUESTION 31
The code block displayed below contains an error. The code block should return a DataFrame where all entries in column supplier contain the letter combination et in this order. Find the error.
Code block:
itemsDf.filter(Column('supplier').isin('et'))

• A. Instead of isin, it should be checked whether column supplier contains the letters et, so isin should be replaced with contains. In addition, the column should be accessed using col['supplier'].
• B. The expression only returns a single column and filter should be replaced by select.
• C. The expression inside the filter parenthesis is malformed and should be replaced by isin('et', 'supplier').
• D. The Column operator should be replaced by the col operator and instead of isin, contains should be used.

Answer: C

Explanation:
Explanation
Correct code block:
itemsDf.filter(col('supplier').contains('et'))
A mixup can easily happen here between isin and contains. Since we want to check whether a column
"contains" the values et, this is the operator we should use here. Note that both methods are methods of Spark's Column object. See below for documentation links.
A specific Column object can be accessed through the col() method and not the Column() method or through col[], which is an essential thing to know here. In PySpark, Column references a generic column object. To use it for queries, you need to link the generic column object to a specific DataFrame. This can be achieved, for example, through the col() method.
More info:
- isin documentation: pyspark.sql.Column.isin - PySpark 3.1.1 documentation
- contains documentation: pyspark.sql.Column.contains - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

NEW QUESTION 32
The code block displayed below contains an error. The code block should return the average of rows in column value grouped by unique storeId. Find the error.
Code block:
transactionsDf.agg("storeId").avg("value")

• A. "storeId" and "value" should be swapped.
• B. Instead of avg("value"), avg(col("value")) should be used.
• C. agg should be replaced by groupBy.
• D. All column names should be wrapped in col() operators.
• E. The avg("value") should be specified as a second argument to agg() instead of being appended to it.

Answer: C

Explanation:
Explanation
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/spark_practice_tests_code/#1/30.html ,
https://bit.ly/sparkpracticeexams_import_instructions)

NEW QUESTION 33
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. broadcast
  3. transactionsDf
  4. "transactionId"
  5. "left_semi"
• B. 1. itemsDf
  2. join
  3. broadcast(transactionsDf)
  4. "transactionId"
  5. "left_semi"
• C. 1. transactionsDf
  2. join
  3. broadcast(itemsDf)
  4. "transactionId"
  5. "left_semi"
• D. 1. transactionsDf
  2. join
  3. itemsDf
  4. transactionsDf.transactionId==itemsDf.transactionId
  5. "anti"
• E. 1. transactionsDf
  2. join
  3. broadcast(itemsDf)
  4. transactionsDf.transactionId==itemsDf.transactionId
  5. "outer"

Answer: C

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 34
Which of the following describes properties of a shuffle?

• A. Shuffles involve only single partitions.
• B. A shuffle is one of many actions in Spark.
• C. Operations involving shuffles are never evaluated lazily.
• D. Shuffles belong to a class known as "full transformations".
• E. In a shuffle, Spark writes data to disk.

Answer: E

Explanation:
Explanation
In a shuffle, Spark writes data to disk.
Correct! Spark's architecture dictates that intermediate results during a shuffle are written to disk.
A shuffle is one of many actions in Spark.
Incorrect. A shuffle is a transformation, but not an action.
Shuffles involve only single partitions.
No, shuffles involve multiple partitions. During a shuffle, Spark generates output partitions from multiple input partitions.
Operations involving shuffles are never evaluated lazily.
Wrong. A shuffle is a costly operation and Spark will evaluate it as lazily as other transformations. This is, until a subsequent action triggers its evaluation.
Shuffles belong to a class known as "full transformations".
Not quite. Shuffles belong to a class known as "wide transformations". "Full transformation" is not a relevant term in Spark.
More info: Spark - The Definitive Guide, Chapter 2 and Spark: disk I/O on stage boundaries explanation - Stack Overflow

NEW QUESTION 35
Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?

• A. transactionsDf.filter(col("productId")==3 | col("productId")<1)
• B. transactionsDf.where("productId"=3).or("productId"<1))
• C. transactionsDf.filter(productId==3 or productId<1)
• D. transactionsDf.filter((col("productId")==3) | (col("productId")<1))
• E. transactionsDf.filter((col("productId")==3) or (col("productId")<1))

Answer: D

Explanation:
Explanation
This question targets your knowledge about how to chain filtering conditions. Each filtering condition should be in parentheses. The correct operator for "or" is the pipe character (|) and not the word or. Another operator of concern is the equality operator. For the purpose of comparison, equality is expressed as two equal signs (==).
Static notebook | Dynamic notebook: See test 2

NEW QUESTION 36
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