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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

2. 1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?

A) Add the option wholetext to the text() function.
B) Add the option lineSep to the text() function.
C) Add the option lineSep=", " to the text() function.
D) Add the option wholetext=False to the text() function.

3. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A) Broadcast a variable to share the maximum time among workers.
B) Configure the Spark UI to automatically collect maximum times.
C) Use an accumulator to record the maximum time on the driver.
D) Use an RDD action like reduce() to compute the maximum time.

4. What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)
B) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
C) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) and cache() - Can be used to set different storage levels to persist the contents of the DataFrame.
D) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK) and persist() - Can be used to set different storage levels to persist the contents of the DataFrame

5. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.sort("age", "salary", ascending=[True, True]).show()
B) df.orderBy("age", "salary", ascending=[True, False]).show()
C) df.orderBy(col("age").asc(), col("salary").asc()).show()
D) df.sort("age", "salary", ascending=[False, True]).show()

Solutions: