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NEW QUESTION 45
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("storeId").between(3,25))
• B. transactionsDf.where(col("value").isNull()).select("productId", "storeId").distinct()
• C. transactionsDf.filter((col("storeId")!=25) | (col("productId")==2))
• D. transactionsDf.select("productId", "storeId").where("storeId == 2 OR storeId != 25")
• E. transactionsDf.filter(col("storeId")==25).select("predError","storeId").distinct()

Answer: E
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 46
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?

• A. spark.read().schema(fileSchema).parquet(filePath)
• B. spark.read.schema("fileSchema").format("parquet").load(filePath)
• C. spark.read().schema(fileSchema).format(parquet).load(filePath)
• D. spark.read.schema(fileSchema).format("parquet").load(filePath)
• E. spark.read.schema(fileSchema).open(filePath)

Answer: D
Explanation:
Explanation
Pay attention here to which variables are quoted. fileSchema is a variable and thus should not be in quotes.
parquet is not a variable and therefore should be in quotes.
SparkSession.read (here referenced as spark.read) returns a DataFrameReader which all subsequent calls reference - the DataFrameReader is not callable, so you should not use parentheses here.
Finally, there is no open method in PySpark. The method name is load.
Static notebook | Dynamic notebook: See test 1
 
NEW QUESTION 47
Which of the following code blocks reads all CSV files in directory filePath into a single DataFrame, with column names defined in the CSV file headers?
Content of directory filePath:
1._SUCCESS
2._committed_2754546451699747124
3._started_2754546451699747124
4.part-00000-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-298-1-c000.csv.gz
5.part-00001-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-299-1-c000.csv.gz
6.part-00002-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-300-1-c000.csv.gz
7.part-00003-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-301-1-c000.csv.gz spark.option("header",True).csv(filePath)

• A. spark.read.load(filePath)
• B. spark.read().option("header",True).load(filePath)
• C. spark.read.format("csv").option("header",True).option("compression","zip").load(filePath)
• D. spark.read.format("csv").option("header",True).load(filePath)

Answer: D
Explanation:
Explanation
The files in directory filePath are partitions of a DataFrame that have been exported using gzip compression.
Spark automatically recognizes this situation and imports the CSV files as separate partitions into a single DataFrame. It is, however, necessary to specify that Spark should load the file headers in the CSV with the header option, which is set to False by default.
 
NEW QUESTION 48
Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a DataFrame?

• A. spark.read.parquet("/FileStore/imports.parquet")
• B. spark.read.path("/FileStore/imports.parquet", source="parquet")
• C. spark.read().parquet("/FileStore/imports.parquet")
• D. spark.mode("parquet").read("/FileStore/imports.parquet")
• E. spark.read().format('parquet').open("/FileStore/imports.parquet")

Answer: A
Explanation:
Explanation
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/spark_practice_tests_code/#1/23.html ,
https://bit.ly/sparkpracticeexams_import_instructions)
 
NEW QUESTION 49
Which of the following code blocks returns a DataFrame that matches the multi-column DataFrame itemsDf, except that integer column itemId has been converted into a string column?

• A. itemsDf.withColumn("itemId", col("itemId").cast("string"))
  (Correct)
• B. itemsDf.select(cast("itemId", "string"))
• C. spark.cast(itemsDf, "itemId", "string")
• D. itemsDf.withColumn("itemId", col("itemId").convert("string"))
• E. itemsDf.withColumn("itemId", convert("itemId", "string"))

Answer: A
Explanation:
Explanation
itemsDf.withColumn("itemId", col("itemId").cast("string"))
Correct. You can convert the data type of a column using the cast method of the Column class. Also note that you will have to use the withColumn method on itemsDf for replacing the existing itemId column with the new version that contains strings.
itemsDf.withColumn("itemId", col("itemId").convert("string"))
Incorrect. The Column object that col("itemId") returns does not have a convert method.
itemsDf.withColumn("itemId", convert("itemId", "string"))
Wrong. Spark's spark.sql.functions module does not have a convert method. The question is trying to mislead you by using the word "converted". Type conversion is also called "type casting". This may help you remember to look for a cast method instead of a convert method (see correct answer).
itemsDf.select(astype("itemId", "string"))
False. While astype is a method of Column (and an alias of Column.cast), it is not a method of pyspark.sql.functions (what the code block implies). In addition, the question asks to return a full DataFrame that matches the multi-column DataFrame itemsDf. Selecting just one column from itemsDf as in the code block would just return a single-column DataFrame.
spark.cast(itemsDf, "itemId", "string")
No, the Spark session (called by spark) does not have a cast method. You can find a list of all methods available for the Spark session linked in the documentation below.
More info:

• pyspark.sql.Column.cast - PySpark 3.1.2 documentation
• pyspark.sql.Column.astype - PySpark 3.1.2 documentation
• pyspark.sql.SparkSession - PySpark 3.1.2 documentation
  Static notebook | Dynamic notebook: See test 3
   
  NEW QUESTION 50
  ......