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NEW QUESTION 40
The code block shown below should return the number of columns in the CSV file stored at location filePath.
From the CSV file, only lines should be read that do not start with a # character. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
__1__(__2__.__3__.csv(filePath, __4__).__5__)
- A. 1. size
2. spark
3. read()
4. escape='#'
5. columns - B. 1. size
2. pyspark
3. DataFrameReader
4. comment='#'
5. columns - C. 1. len
2. pyspark
3. DataFrameReader
4. comment='#'
5. columns - D. 1. len
2. spark
3. read
4. comment='#'
5. columns - E. 1. DataFrame
2. spark
3. read()
4. escape='#'
5. shape[0]
Answer: D
Explanation:
Explanation
Correct code block:
len(spark.read.csv(filePath, comment='#').columns)
This is a challenging question with difficulties in an unusual context: The boundary between DataFrame and the DataFrameReader. It is unlikely that a question of this difficulty level appears in the exam. However, solving it helps you get more comfortable with the DataFrameReader, a subject you will likely have to deal with in the exam.
Before dealing with the inner parentheses, it is easier to figure out the outer parentheses, gaps 1 and 5. Given the code block, the object in gap 5 would have to be evaluated by the object in gap 1, returning the number of columns in the read-in CSV. One answer option includes DataFrame in gap 1 and shape[0] in gap 2. Since DataFrame cannot be used to evaluate shape[0], we can discard this answer option.
Other answer options include size in gap 1. size() is not a built-in Python command, so if we use it, it would have to come from somewhere else. pyspark.sql.functions includes a size() method, but this method only returns the length of an array or map stored within a column (documentation linked below).
So, using a size() method is not an option here. This leaves us with two potentially valid answers.
We have to pick between gaps 2 and 3 being spark.read or pyspark.DataFrameReader. Looking at the documentation (linked below), the DataFrameReader is actually a child class of pyspark.sql, which means that we cannot import it using pyspark.DataFrameReader. Moreover, spark.read makes sense because on Databricks, spark references current Spark session (pyspark.sql.SparkSession) and spark.read therefore returns a DataFrameReader (also see documentation below). Finally, there is only one correct answer option remaining.
More info:
- pyspark.sql.functions.size - PySpark 3.1.2 documentation
- pyspark.sql.DataFrameReader.csv - PySpark 3.1.2 documentation
- pyspark.sql.SparkSession.read - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 41
The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.format("parquet").__2__(__3__).option(__4__, "brotli").__5__(storeDir)
- A. 1. store
2. with
3. "replacement"
4. "compression"
5. path - B. 1. write
2. mode
3. "overwrite"
4. compression
5. parquet - C. 1. write
2. mode
3. "overwrite"
4. "compression"
5. save
(Correct) - D. 1. save
2. mode
3. "replace"
4. "compression"
5. path - E. 1. save
2. mode
3. "ignore"
4. "compression"
5. path
Answer: D
Explanation:
Explanation
Correct code block:
transactionsDf.write.format("parquet").mode("overwrite").option("compression", "snappy").save(storeDir) Solving this question requires you to know how to access the DataFrameWriter (link below) from the DataFrame API - through DataFrame.write.
Another nuance here is about knowing the different modes available for writing parquet files that determine Spark's behavior when dealing with existing files. These, together with the compression options are explained in the DataFrameWriter.parquet documentation linked below.
Finally, bracket __5__ poses a certain challenge. You need to know which command you can use to pass down the file path to the DataFrameWriter. Both save and parquet are valid options here.
More info:
- DataFrame.write: pyspark.sql.DataFrame.write - PySpark 3.1.1 documentation
- DataFrameWriter.parquet: pyspark.sql.DataFrameWriter.parquet - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1
NEW QUESTION 42
The code block shown below should return a single-column DataFrame with a column named consonant_ct that, for each row, shows the number of consonants in column itemName of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
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.select(__1__(__2__(__3__(__4__), "a|e|i|o|u|\s", "")).__5__("consonant_ct"))
- A. 1. lower
2. regexp_replace
3. length
4. "itemName"
5. alias - B. 1. length
2. regexp_extract
3. upper
4. col("itemName")
5. as - C. 1. length
2. regexp_replace
3. lower
4. col("itemName")
5. alias - D. 1. size
2. regexp_replace
3. lower
4. "itemName"
5. alias - E. 1. size
2. regexp_extract
3. lower
4. col("itemName")
5. alias
Answer: C
Explanation:
Explanation
Correct code block:
itemsDf.select(length(regexp_replace(lower(col("itemName")), "a|e|i|o|u|\s", "")).alias("consonant_ct")) Returned DataFrame:
+------------+
|consonant_ct|
+------------+
| 19|
| 16|
| 10|
+------------+
This question tries to make you think about the string functions Spark provides and in which order they should be applied. Arguably the most difficult part, the regular expression "a|e|i|o|u|
\s", is not a numbered blank. However, if you are not familiar with the string functions, it may be a good idea to review those before the exam.
The size operator and the length operator can easily be confused. size works on arrays, while length works on strings. Luckily, this is something you can read up about in the documentation.
The code block works by first converting all uppercase letters in column itemName into lowercase (the lower() part). Then, it replaces all vowels by "nothing" - an empty character "" (the regexp_replace() part). Now, only lowercase characters without spaces are included in the DataFrame. Then, per row, the length operator counts these remaining characters. Note that column itemName in itemsDf does not include any numbers or other characters, so we do not need to make any provisions for these. Finally, by using the alias() operator, we rename the resulting column to consonant_ct.
More info:
- lower: pyspark.sql.functions.lower - PySpark 3.1.2 documentation
- regexp_replace: pyspark.sql.functions.regexp_replace - PySpark 3.1.2 documentation
- length: pyspark.sql.functions.length - PySpark 3.1.2 documentation
- alias: pyspark.sql.Column.alias - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 43
......
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