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Question No. 1
Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?
A. transactionsDf.select("storeId").dropDuplicates().count()
B. transactionsDf.select(count("storeId")).dropDuplicates()
C. transactionsDf.select(distinct("storeId")).count()
D. transactionsDf.dropDuplicates().agg(count("storeId"))
E. transactionsDf.distinct().select("storeId").count()
Answer: A
Question No. 2
The code block displayed below contains one or more errors. The code block should load parquet files at location filePath into a DataFrame, only loading those files that have been modified before 2029-03-20 05:44:46. Spark should enforce a schema according to the schema shown below. Find the error.
Schema:
1. root
2. |-- itemId: integer (nullable = true)
3. |-- attributes: array (nullable = true)
4. | |-- element: string (containsNull = true)
5. |-- supplier: string (nullable = true)
Code block:
1. schema = StructType([
2. StructType("itemId", IntegerType(), True),
3. StructType("attributes", ArrayType(StringType(), True), True),
4. StructType("supplier", StringType(), True)
5. ])
6.
7. spark.read.options("modifiedBefore", "2029-03-20T05:44:46").schema(schema).load(filePath)
A. The attributes array is specified incorrectly, Spark cannot identify the file format, and the syntax of the call to Spark's DataFrameReader is incorrect.
B. Columns in the schema definition use the wrong object type and the syntax of the call to Spark's DataFrameReader is incorrect.
C. The data type of the schema is incompatible with the schema() operator and the
modification date threshold is specified incorrectly.
D. Columns in the schema definition use the wrong object type, the modification date threshold is specified incorrectly, and Spark cannot identify the file format.
E. Columns in the schema are unable to handle empty values and the modification date threshold is specified incorrectly.
Answer: D
Question No. 3
The code block shown below should show information about the data type that column storeId of DataFrame transactionsDf contains. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__).__3__
A. 1. select 2. "storeId" 3. print_schema()
B. 1. limit 2. 1 3. columns
C. 1. select 2. "storeId" 3. printSchema()
D. 1. limit 2. "storeId" 3. printSchema()
E. 1. select 2. storeId 3. dtypes
Answer: B
Question No. 4
The code block shown below should return a DataFrame with only columns from DataFrame transactionsDf for which there is a corresponding transactionId in DataFrame itemsDf. DataFrame
itemsDf is very small and much smaller than DataFrame transactionsDf. The query should be executed in an optimized way. Choose the answer that correctly fills the blanks in the code block to accomplish this.
__1__.__2__(__3__, __4__, __5__)
A. 1. transactionsDf 2. join 3. broadcast(itemsDf) 4. transactionsDf.transactionId==itemsDf.transactionId 5. "outer"
B. 1. transactionsDf 2. join 3. itemsDf 4. transactionsDf.transactionId==itemsDf.transactionId 5. "anti"
C. 1. transactionsDf 2. join 3. broadcast(itemsDf) 4. "transactionId" 5. "left_semi"
D. 1. itemsDf 2. broadcast 3. transactionsDf 4. "transactionId" 5. "left_semi"
E. 1. itemsDf 2. join 3. broadcast(transactionsDf) 4. "transactionId" 5. "left_semi"
Answer: C
Question No. 5
Which of the following code blocks writes DataFrame itemsDf to disk at storage location filePath, making sure to substitute any existing data at that location?
A. itemsDf.write.mode("overwrite").parquet(filePath)
B. itemsDf.write.option("parquet").mode("overwrite").path(filePath)
C. itemsDf.write(filePath, mode="overwrite")
D. itemsDf.write.mode("overwrite").path(filePath)
E. itemsDf.write().parquet(filePath, mode="overwrite")
Answer: A