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NEW QUESTION 42
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. agg should be replaced by groupBy.
• B. The avg("value") should be specified as a second argument to agg() instead of being appended to it.
• C. Instead of avg("value"), avg(col("value")) should be used.
• D. All column names should be wrapped in col() operators.
• E. "storeId" and "value" should be swapped.

Answer: A

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 43
The code block shown below should return only the average prediction error (column predError) of a random subset, without replacement, of approximately 15% of rows in DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(avg('predError'))

• A. 1. sample
  2. True
  3. 0.15
  4. filter
• B. 1. sample
  2. 0.85
  3. False
  4. select
• C. 1. sample
  2. False
  3. 0.15
  4. select
• D. 1. fraction
  2. False
  3. 0.85
  4. select
• E. 1. fraction
  2. 0.15
  3. True
  4. where

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.sample(withReplacement=False, fraction=0.15).select(avg('predError')) You should remember that getting a random subset of rows means sampling. This, in turn should point you to the DataFrame.sample() method. Once you know this, you can look up the correct order of arguments in the documentation (link below).
Lastly, you have to decide whether to use filter, where or select. where is just an alias for filter(). filter() is not the correct method to use here, since it would only allow you to filter rows based on some condition. However, the question asks to return only the average prediction error. You can control the columns that a query returns with the select() method - so this is the correct method to use here.
More info: pyspark.sql.DataFrame.sample - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION 44
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?

• A. itemsDf.store()
• B. itemsDf.write.option('destination', 'memory').save()
• C. itemsDf.cache()
• D. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
• E. itemsDf.persist(StorageLevel.MEMORY_ONLY)

Answer: C

Explanation:
Explanation
The key to solving this question is knowing (or reading in the documentation) that, by default, cache() stores values to memory and writes any partitions for which there is insufficient memory to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option listed here. It is also worth noting that cache() does not have any arguments.
If you have troubles finding the storage level information in the documentation, please also see this student Q&A thread that sheds some light here.
Static notebook | Dynamic notebook: See test 2

NEW QUESTION 45
Which of the following code blocks concatenates rows of DataFrames transactionsDf and transactionsNewDf, omitting any duplicates?

• A. spark.union(transactionsDf, transactionsNewDf).distinct()
• B. transactionsDf.union(transactionsNewDf).distinct()
• C. transactionsDf.concat(transactionsNewDf).unique()
• D. transactionsDf.union(transactionsNewDf).unique()
• E. transactionsDf.join(transactionsNewDf, how="union").distinct()

Answer: B

Explanation:
Explanation
DataFrame.unique() and DataFrame.concat() do not exist and union() is not a method of the SparkSession. In addition, there is no union option for the join method in the DataFrame.join() statement.
More info: pyspark.sql.DataFrame.union - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION 46
The code block displayed below contains multiple errors. The code block should remove column transactionDate from DataFrame transactionsDf and add a column transactionTimestamp in which dates that are expressed as strings in column transactionDate of DataFrame transactionsDf are converted into unix timestamps. Find the errors.
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+----------------+
2.|transactionId|predError|value|storeId|productId| f| transactionDate|
3.+-------------+---------+-----+-------+---------+----+----------------+
4.| 1| 3| 4| 25| 1|null|2020-04-26 15:35|
5.| 2| 6| 7| 2| 2|null|2020-04-13 22:01|
6.| 3| 3| null| 25| 3|null|2020-04-02 10:53|
7.+-------------+---------+-----+-------+---------+----+----------------+ Code block:
1.transactionsDf = transactionsDf.drop("transactionDate")
2.transactionsDf["transactionTimestamp"] = unix_timestamp("transactionDate", "yyyy-MM-dd")

• A. Column transactionDate should be dropped after transactionTimestamp has been written. The withColumn operator should be used instead of the existing column assignment. Column transactionDate should be wrapped in a col() operator.
• B. The string indicating the date format should be adjusted. The withColumnReplaced operator should be used instead of the drop and assign pattern in the code block to replace column transactionDate with the new column transactionTimestamp.
• C. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment.
• D. Column transactionDate should be wrapped in a col() operator.
• E. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment. Operator to_unixtime() should be used instead of unix_timestamp().

Answer: C

Explanation:
Explanation
This question requires a lot of thinking to get right. For solving it, you may take advantage of the digital notepad that is provided to you during the test. You have probably seen that the code block includes multiple errors. In the test, you are usually confronted with a code block that only contains a single error. However, since you are practicing here, this challenging multi-error question will make it easier for you to deal with single-error questions in the real exam.
You can clearly see that column transactionDate should be dropped only after transactionTimestamp has been written. This is because to generate column transactionTimestamp, Spark needs to read the values from column transactionDate.
Values in column transactionDate in the original transactionsDf DataFrame look like 2020-04-26 15:35. So, to convert those correctly, you would have to pass yyyy-MM-dd HH:mm. In other words:
The string indicating the date format should be adjusted.
While you might be tempted to change unix_timestamp() to to_unixtime() (in line with the from_unixtime() operator), this function does not exist in Spark. unix_timestamp() is the correct operator to use here.
Also, there is no DataFrame.withColumnReplaced() operator. A similar operator that exists is DataFrame.withColumnRenamed().
Whether you use col() or not is irrelevant with unix_timestamp() - the command is fine with both.
Finally, you cannot assign a column like transactionsDf["columnName"] = ... in Spark. This is Pandas syntax (Pandas is a popular Python package for data analysis), but it is not supported in Spark.
So, you need to use Spark's DataFrame.withColumn() syntax instead.
More info: pyspark.sql.functions.unix_timestamp - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

NEW QUESTION 47
......