Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Dumps Questions Answers

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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Frequently Asked Questions

Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Sample Questions

Question # 1

Which of the following code blocks immediately removes the previously cached DataFrame transactionsDf from memory and disk?

 A. array_remove(transactionsDf, "*") 
B. transactionsDf.unpersist() (Correct)
 C. del transactionsDf 
D. transactionsDf.clearCache() E. transactionsDf.persist()

Show Answer

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Question # 2

The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this. Sample of 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.__1__(__2__).select(__3__, __4__) 

A. 1. filter 2. col("supplier").isin("Sports") 3. "itemName" 4. explode(col("attributes")) 
B. 1. where 2. col("supplier").contains("Sports") 3. "itemName" 4. "attributes" 
C. 1. where 2. col(supplier).contains("Sports") 3. explode(attributes) 4. itemName
 D. 1. where 2. "Sports".isin(col("Supplier")) 3. "itemName" 4. array_explode("attributes") 
E. 1. filter 2. col("supplier").contains("Sports") 3. "itemName" 4. explode("attributes")

Show Answer

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Question # 3

The code block shown below should add a column itemNameBetweenSeparators to DataFrame itemsDf. The column should contain arrays of maximum 4 strings. The arrays should be composed of the values in column itemsDf which are separated at - or whitespace characters. Choose the answer that correctly fills the blanks in the code block to accomplish this. 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.__1__(__2__, __3__(__4__, "[\s\-]", __5__)) 

A. 1. withColumn 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 4 (Correct) 
B. 1. withColumnRenamed 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 4 
C. 1. withColumnRenamed 2. "itemName" 3. split 4. "itemNameBetweenSeparators" 5. 4 
D. 1. withColumn 2. "itemNameBetweenSeparators" 3. split 4. "itemName" 5. 5 
E. 1. withColumn 2. itemNameBetweenSeparators 3. str_split 4. "itemName" 5. 5 

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Question # 4

Which of the following code blocks reads in the two-partition parquet file stored at filePath, making sure all columns are included exactly once even though each partition has a different schema? Schema of first partition: 1. root 2. |-- transactionId: integer (nullable = true) 3. |-- predError: integer (nullable = true) 4. |-- value: integer (nullable = true) 5. |-- storeId: integer (nullable = true) 6. |-- productId: integer (nullable = true) 7. |-- f: integer (nullable = true) Schema of second partition: 1. root 2. |-- transactionId: integer (nullable = true) 3. |-- predError: integer (nullable = true) 4. |-- value: integer (nullable = true) 5. |-- storeId: integer (nullable = true) 6. |-- rollId: integer (nullable = true) 7. |-- f: integer (nullable = true) 8. |-- tax_id: integer (nullable = false) 

A. spark.read.parquet(filePath, mergeSchema='y') 
B. spark.read.option("mergeSchema", "true").parquet(filePath) 
C. spark.read.parquet(filePath) 
D. 1. nx = 0 2. for file in dbutils.fs.ls(filePath): 3. if not file.name.endswith(".parquet"): 4. continue 5. df_temp = spark.read.parquet(file.path) 6. if nx == 0: 7. df = df_temp 8. else: 9. df = df.union(df_temp) 10. nx = nx+1 11. df E. 1. nx = 0 2. for file in dbutils.fs.ls(filePath): 3. if not file.name.endswith(".parquet"): 4. continue 5. df_temp = spark.read.parquet(file.path) 6. if nx == 0: 7. df = df_temp 8. else: 9. df = df.join(df_temp, how="outer") 10. nx = nx+1 11. df 

Show Answer

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Question # 5

Which of the following code blocks shows the structure of a DataFrame in a tree-like way, containing both column names and types? 

A. 1. print(itemsDf.columns) 2. print(itemsDf.types) 
B. itemsDf.printSchema() 
C. spark.schema(itemsDf) 
D. itemsDf.rdd.printSchema()
 E. itemsDf.print.schema() 

Show Answer

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Question # 6

The code block shown below should add column transactionDateForm to DataFrame transactionsDf. The column should express the unix-format timestamps in column transactionDate as string type like Apr 26 (Sunday). Choose the answer that correctly fills the blanks in the code block to accomplish this. transactionsDf.__1__(__2__, from_unixtime(__3__, __4__)) 

A. 1. withColumn 2. "transactionDateForm" 3. "MMM d (EEEE)" 4. "transactionDate" 
B. 1. select 2. "transactionDate" 3. "transactionDateForm" 4. "MMM d (EEEE)" 
C. 1. withColumn 2. "transactionDateForm" 3. "transactionDate" 4. "MMM d (EEEE)" 
D. 1. withColumn 2. "transactionDateForm" 3. "transactionDate" 4. "MM d (EEE)" 
E. 1. withColumnRenamed 2. "transactionDate" 3. "transactionDateForm" 4. "MM d (EEE)" 

Show Answer

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Question # 7

Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame? 

A. spark.read.json(filePath) 
B. spark.read.path(filePath, source="json") 
C. spark.read().path(filePath) 
D. spark.read().json(filePath) 
E. spark.read.path(filePath) 

Show Answer

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Question # 8

The code block displayed below contains an error. The code block should write DataFrame transactionsDf as a parquet file to location filePath after partitioning it on column storeId. Find the error. Code block: transactionsDf.write.partitionOn("storeId").parquet(filePath) 

A. The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block. 
B. The partitionOn method should be called before the write method.
 C. The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath. 
D. Column storeId should be wrapped in a col() operator. 
E. No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead. 

Show Answer

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Question # 9

Which of the following code blocks creates a new DataFrame with 3 columns, productId, highest, and lowest, that shows the biggest and smallest values of column value per value in column productId 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. | 4| null| null| 3| 2|null| 8. | 5| null| null| null| 2|null| 9. | 6| 3| 2| 25| 2|null| 10. +-------------+---------+-----+-------+---------+----+ 

A. transactionsDf.max('value').min('value') 
B. transactionsDf.agg(max('value').alias('highest'), min('value').alias('lowest')) 
C. transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))
 D. transactionsDf.groupby('productId').agg(max('value').alias('highest'), min('value').alias('lowest'))
 E. transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")}) 

Show Answer

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Question # 10

Which of the following code blocks returns a DataFrame with approximately 1,000 rows from the 10,000-row DataFrame itemsDf, without any duplicates, returning the same rows even if the code block is run twice? 

A. itemsDf.sampleBy("row", fractions={0: 0.1}, seed=82371) 
B. itemsDf.sample(fraction=0.1, seed=87238) 
C. itemsDf.sample(fraction=1000, seed=98263) 
D. itemsDf.sample(withReplacement=True, fraction=0.1, seed=23536) 
E. itemsDf.sample(fraction=0.1) 

Show Answer

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Question # 11

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. +-------------+---------+-----+-------+---------+----+

A.transactionsDf.withColumnRemoved("predError", "productId")
B. transactionsDf.drop(["predError", "productId", "associateId"])
C. transactionsDf.drop("predError", "productId", "associateId")
D. transactionsDf.dropColumns("predError", "productId", "associateId")
E. transactionsDf.drop(col("predError", "productId"))

Show Answer

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Question # 12

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)

Show Answer

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Question # 13

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().
D. UDFs do not exist in PySpark.
E. Spark is not adding a column.

Show Answer

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Question # 14

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)
E. 1. select2. size3. "itemNameElements"4. >3

Show Answer

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Question # 15

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.

Show Answer

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Question # 16

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")

Show Answer

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Question # 17

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)

Show Answer

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Question # 18

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. 

Show Answer

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Question # 19

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") 

Show Answer

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Question # 20

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. 

Show Answer

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Question # 21

The code block displayed below contains an error. The code block should arrange the rows of DataFrame transactionsDf using information from two columns in an ordered fashion, arranging first by column value, showing smaller numbers at the top and greater numbers at the bottom, and then by column predError, for which all values should be arranged in the inverse way of the order of items in column value. Find the error. Code block: transactionsDf.orderBy('value', asc_nulls_first(col('predError'))) 

A. Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement. 
B. Column value should be wrapped by the col() operator. 
C. Column predError should be sorted in a descending way, putting nulls last
. D. Column predError should be sorted by desc_nulls_first() instead. 
E. Instead of orderBy, sort should be used. 

Show Answer

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Question # 22

The code block displayed below contains an error. The code block should configure Spark to split data in 20 parts when exchanging data between executors for joins or aggregations. Find the error. Code block: spark.conf.set(spark.sql.shuffle.partitions, 20) 

A. The code block uses the wrong command for setting an option.
 B. The code block sets the wrong option. 
C. The code block expresses the option incorrectly. 
D. The code block sets the incorrect number of parts. 
E. The code block is missing a parameter. 

Show Answer

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