15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

• A.streaming_df.count()
• B.streaming_df.filter("count < 30")
• C.streaming_df.select(countDistinct("name"))
• D.streaming_df.show()

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25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

• A.employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
• B.employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
• C.employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
• D.employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")

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Which command overwrites an existing JSON file when writing a DataFrame?

• A.df.write.mode("overwrite").json("path/to/file")
• B.df.write.overwrite.json("path/to/file")
• C.df.write.json("path/to/file", overwrite=True)
• D.df.write.format("json").save("path/to/file", mode="overwrite")

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A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes thesparkSparkSession?

• A.ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.
• B.A SparkSession is unique for eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.
• C.If a SparkSession already exists, this code will return the existing session instead of creating a new one.
• D.A new SparkSession is created every time thegetOrCreate()method is invoked.

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What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

• A.The conversion will automatically distribute the data across worker nodes
• B.The operation will fail if the Pandas DataFrame exceeds 1000 rows
• C.Data will be lost during conversion
• D.The operation will load all data into the driver's memory, potentially causing memory overflow

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