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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Practice Test Questions ( Updated) – Real Exam Questions & Dumps PDF
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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Sample Questions – Free Practice Test & Real Exam Prep
Question #1
Which of the following code blocks returns a DataFrame where columns predError and productId areremoved from DataFrame transactionsDf?Sample of 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. +-------------+---------+-----+-------+---------+----+
B. transactionsDf.drop(["predError", "productId", "associateId"])
C. transactionsDf.drop("predError", "productId", "associateId")
D. transactionsDf.dropColumns("predError", "productId", "associateId")
E. transactionsDf.drop(col("predError", "productId"))
Answer: D
Explanation:
The key here is to understand that columns that are passed to DataFrame.drop() are ignored if they
do not exist in the DataFrame. So, passing column name associateId to transactionsDf.drop()
does not have any effect.
Passing a list to transactionsDf.drop() is not valid. The documentation (link below) shows the call
structure as DataFrame.drop(*cols). The * means that all arguments that are passed to
DataFrame.drop() are read as columns. However, since a list of columns, for example ["predError",
"productId", "associateId"] is not a column, Spark will run into an error.
More info: pyspark.sql.DataFrame.drop ” PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1, (
Databricks import instructions)
Question #2
Which of the following code blocks returns about 150 randomly selected rows from the 1000-rowDataFrame transactionsDf, assuming that any row can appear more than once in the returnedDataFrame?
A. transactionsDf.resample(0.15, False, 3142)
B. transactionsDf.sample(0.15, False, 3142)
C. transactionsDf.sample(0.15)
D. transactionsDf.sample(0.85, 8429)
E. transactionsDf.sample(True, 0.15, 8261)
Answer: E
Explanation:
Answering this
Question #3
The code block displayed below contains an error. The code block should use Python methodfind_most_freq_letter to find the letter present most in column itemName of DataFrame itemsDfandreturn it in a new column most_frequent_letter. Find the error.Code block:1. find_most_freq_letter_udf = udf(find_most_freq_letter)2. itemsDf.withColumn("most_frequent_letter", find_most_freq_letter("itemName"))
A. Spark is not using the UDF method correctly.
B. The UDF method is not registered correctly, since the return type is missing.
C. The "itemName" expression should be wrapped in col().
More info: pyspark.sql.functions.udf ” PySpark 3.1.1 documentation
Question #4
in column itemNameElements. Choose the answer that correctly fills the blanks in the code blockto accomplish this.Example of DataFrame itemsDf:1. +------+----------------------------------+-------------------+------------------------------------------+2. |itemId|itemName |supplier |itemNameElements |3. +------+----------------------------------+-------------------+------------------------------------------+4. |1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in,the, Snow]|5. |2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress]|6. |3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |7. +------+----------------------------------+-------------------+------------------------------------------+Code block:itemsDf.__1__(__2__(__3__)__4__)
A. 1. select2. count3. col("itemNameElements")4. >3
B. 1. filter2. count3. itemNameElements4. >=3
C. 1. select2. count3. "itemNameElements"4. >3
D. 1. filter2. size3. "itemNameElements"4. >=3(Correct)
knowing the difference between count and size (refer to documentation below). size is the correct
function to choose here since it returns the number
of elements in an array on a per-row basis.
The other consideration for solving this
Question #5
The code block displayed below contains an error. The code block below is intended to add a columnitemNameElements to DataFrame itemsDf that includes an array of all words in columnitemName. Find the error.Sample of DataFrame itemsDf:1. +------+----------------------------------+-------------------+2. |itemId|itemName |supplier |3. +------+----------------------------------+-------------------+4. |1 |Thick Coat for Walking in the Snow|Sports Company Inc.|5. |2 |Elegant Outdoors Summer Dress |YetiX |6. |3 |Outdoors Backpack |Sports Company Inc.|7. +------+----------------------------------+-------------------+Code block:itemsDf.withColumnRenamed("itemNameElements", split("itemName"))itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
A. All column names need to be wrapped in the col() operator.
B. Operator withColumnRenamed needs to be replaced with operator withColumn and a secondargument "," needs to be passed to the split method.
C. Operator withColumnRenamed needs to be replaced with operator withColumn and the splitmethod needs to be replaced by the splitString method.
D. Operator withColumnRenamed needs to be replaced with operator withColumn and a secondargument " " needs to be passed to the split method.
E. The expressions "itemNameElements" and split("itemName") need to be swapped.
the split method definitely needs a second argument here (also look at the link to the documentation
below). Given the values in column itemName in
DataFrame itemsDf, this should be a space character " ". This is the character we need to split the
words in the column.
More info: pyspark.sql.functions.split ” PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1, (Databricks import instructions)
Question #6
Which of the following code blocks returns only rows from DataFrame transactionsDf in which valuesin column productId are unique?
A. transactionsDf.distinct("productId")
B. transactionsDf.dropDuplicates(subset=["productId"])
C. transactionsDf.drop_duplicates(subset="productId")
D. transactionsDf.unique("productId")
E. transactionsDf.dropDuplicates(subset="productId")
Answer: B
Explanation:
Although the
Question #7
Which of the following code blocks uses a schema fileSchema to read a parquet file at locationfilePath into a DataFrame?
A. spark.read.schema(fileSchema).format("parquet").load(filePath)
B. spark.read.schema("fileSchema").format("parquet").load(filePath)
C. spark.read().schema(fileSchema).parquet(filePath)
D. spark.read().schema(fileSchema).format(parquet).load(filePath)
E. spark.read.schema(fileSchema).open(filePath)
Answer: A
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,
Databricks import instructions)
Question #8
The code block displayed below contains multiple errors. The code block should return a DataFrame
that contains only columns transactionId, predError, value and storeId of DataFrame
transactionsDf. Find the errors.
Code block:
transactionsDf.select([col(productId), col(f)])
Sample of 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. +-------------+---------+-----+-------+---------+----+
A. The column names should be listed directly as arguments to the operator and not as a list.
B. The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator.
C. The select operator should be replaced by a drop operator.
D. The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and f should be replaced by transactionId, predError, value and storeId.
E. The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.
Answer: B Explanation: Correct code block: transactionsDf.drop("productId", "f") This 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 NO: will make it easier for you to deal with single-error questions in the real exam. The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed as strings without being wrapped in a col() operator. Correct! Here, you need to figure out the many, many things that are wrong with the initial code block. While the can be solved by using a select statement, a drop statement, given the answer options, is the correct one. Then, you can read in the documentation that drop does not take a list as an argument, but just the column names that should be dropped. Finally, the column names should be expressed as strings and not as Python variable names as in the original code block. The column names should be listed directly as arguments to the operator and not as a list. Incorrect. While this is a good first step and part of the correct solution (see above), this modification is insufficient to solve the question. The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and should be replaced by transactionId, predError, value and storeId. Wrong. If you use the same pattern as in the original code block (col(productId), col(f)), you are still making a mistake. col(productId) will trigger Python to search for the content of a variable named productId instead of telling Spark to use the column productId - for that, you need to express it as a string. The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed. No. This still leaves you with Python trying to interpret the column names as Python variables (see above). The select operator should be replaced by a drop operator. Wrong, this is not enough to solve the question. If you do this, you will still face problems since you are passing a Python list to drop and the column names are still interpreted as Python variables (see above). More info: pyspark.sql.DataFrame.drop ” PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3, (Databricks import instructions)
Question #9
Which of the following code blocks returns a new DataFrame in which column attributes of
DataFrame itemsDf is renamed to feature0 and column supplier to feature1?
A. itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1)
B. 1. itemsDf.withColumnRenamed("attributes", "feature0") 2. itemsDf.withColumnRenamed("supplier", "feature1")
C. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
D. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
E. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
Answer: D Explanation: itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1") Correct! Spark's DataFrame.withColumnRenamed syntax makes it relatively easy to change the name of a column. itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1) Incorrect. In this code block, the Python interpreter will try to use attributes and the other column names as variables. Needless to say, they are undefined, and as a result the block will not run. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1")) Wrong. The DataFrame.withColumnRenamed() operator takes exactly two string arguments. So, in this answer both using col() and using four arguments is wrong. itemsDf.withColumnRenamed("attributes", "feature0") itemsDf.withColumnRenamed("supplier", "feature1") No. In this answer, the returned DataFrame will only have column supplier be renamed, since the result of the first line is not written back to itemsDf. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1") Incorrect. While withColumn works for adding and naming new columns, you cannot use it to rename existing columns. More info: pyspark.sql.DataFrame.withColumnRenamed ” PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3, ( Databricks import instructions)
Question #10
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-MMdd")
A. 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().
B. 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.
C. Column transactionDate should be wrapped in a col() operator.
D. 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.
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.
Answer: E Explanation: This 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 NO: 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, (Databricks import instructions)
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